Statistics

Abstract

Mercedes-Benz United States International (MBUSI) built a manufacturing facility for the production of the new M-Class “All Activity Vehicle” (AAV). This plant consists of three large sequential shops: the Body Shop, the Paint Shop, and the Assembly Shop. When the plant reaches full production, 270 vehicles will be produced each day by two shifts. A finished vehicle is intended to leave the end of the assembly line every 3.6 minutes.

• The main objective of this study is to simulate the design and operational policies of the AAV assembly facility and to verify that the daily throughput requirements can be met. The simulation study also answered the following questions:
• What is the maximum throughput (capacity) of the facility?
  · What is the daily distribution of throughput?
  · Does the current design produce the required throughput of 270 cars per day?
  · How do the buffers behave in terms of quantity fluctuations?
  · What are the possible bottlenecks to the desired throughput?

This paper provides a description of the integrated simulation model to analyze the capability of the production facilities at MBUSI. This paper includes the inputs used for the development of each of the three individual models: the Body Shop, the Paint Shop, and the Assembly Shop. Additionally, it includes descriptions of the model features and the assumptions that were made.

Introduction

Prior to starting operations of its new assembly plant in Vance, Alabama, Mercedes-Benz United States International (MBUSI) approached the University of Alabama for simulation assistance. Separate efforts to model the operations had not produced an acceptable result.

The MBUSI facility is comprised primarily of three distinct functional “shops”: the Body Shop, the Paint Shop, and the Assembly Shop. The vehicle body is welded together in the Body Shop. It then goes through the Paint Shop for treatment and painting. Finally, the engine, transmission, and all other parts are installed in the Assembly Shop.

This facility, MBUSI’s first in the United States, is scheduled to produce 65,000 AAVs annually when fully operational. This is a relatively small output in comparison to typical automobile assembly plants. When the plant reaches full production, two nine-hour shifts will produce 270 vehicles per day, or in other words, a finished vehicle will be leaving the end of assembly every 3.6 minutes. The option content will vary widely from car to car; there will be many choices available in engines, transmissions, and interior appointments. Hence, the plant will operate a
“mixed model” assembly line. MBUSI hired external engineering firms to design each of the three shops. Two of the firms developed Automod simulations of their respective shops. Durr Engineering developed a simulation of the Paint Shop. Mitsubishi- Chiyoda did the same for the Assembly Shop.

These simulations were developed independently of each other in order to validate the local throughput capacity of each shop. For example, Durr Engineering developed the Paint Shop simulation without direct concern of starvation from the Body Shop or blockage from the Assembly Shop. MBUSI wanted to verify that the overall facility will be able to produce the targeted 65,000 units per year.

While the facility has been divided into three functional areas, the functions are very interdependent. The throughput of one affects the throughput of the others. For example, a problem in the Paint Shop could starve the Assembly Shop and block the Paint Shop. Therefore, it is important for Mercedes-Benz to understand the Park, Matson and Miller interdependencies between the shops. Since the factory layout is an entirely new design, Mercedes-Benz management thought it was important to investigate th design of the plant and its operational policies, and to validate it using simulation. This was considered important considering the effect stochastic disturbances and vehicle flow dynamics might have on the system performance. If problems were revealed in the design, they could be dealt with prior to implementation of the system.

The main objective of this study was to develop a detailed simulation model of the AAV assembly plant to verify that the daily throughput requirements could be met by the current design of the AAV assembly facility.

Additional objectives of this study were to address the concerns of the management. The simulation study answered the following questions:
· What is the maximum throughput (capacity) of the
facility?
· What is the daily distribution of throughput?
· Does the current design produce the required throughput of 270 cars per day?

Additional questions also addressed by the analysis were:

1. How do the buffers behave in terms of quantity fluctuations?
2. What are the possible bottlenecks to the desired throughput?

This paper provides a description of the work that has been done for this research. This paper includes the inputs used for the development of each of the three individual models: the Body Shop, the Paint Shop, and the Assembly Shop. Additionally, it includes descriptions of the model features and the assumptions that were made. Actual data used in the analysis will not be presented due to proprietary concern.

Model concepts

A modular approach was taken. Three individual models, each representing one of the functional shops (the Body, Paint, and Assembly Shops) were developed to study the capacity of each shop in isolation, without concern of starvation or blocking from the other two shops (Roher and Strong 1997). After the models were validated individually, they were integrated in order to study the performance of the entire system.

As part of the Paint Shop, the Selectivity Bank was modeled. Since a body’s option/accessory list will affect the level of work in the Assembly Shop required for assembly, it is important to provide the Assembly Shop with a stream of bodies that is optimally sequenced for a balanced workload. The bodies are optimally sequenced before they enter the Body Shop. However, there are several events that can occur in the Body Shop and Paint Shop that disrupt the original sequence of bodies. The Selectivity Bank, a 24-position buffer between the Paint Shop and Assembly Shop, is used to reshuffle the order of the bodies back to an optimal sequence before they enter the Assembly Shop.

The schematic presented in Figure 1 represents the construction of the integrated model. The Body, Paint, and Assembly Shops were modeled using the SIMAN (Pegden 1994) simulation language. But, due to the relative complexity of the algorithms involved in operating the Selectivity Bank, the Selectivity Bank was modeled separately in the C ++ language and is accessed by the Paint Shop model via files and event statements. As the schematic shows, the integrated model receives input from an ASCII text file containing information on the model mix. The model mix was developed from data provided by MBUSI. The data defined indicated the accessories and the percentage of the vehicles that will carry the given accessory. The model mix was developed under the assumption that high-end cars will have many options, and that low-end cars will have fewer options.

Model development

Once all data had been collected, the individual Body, Paint, Selectivity Bank, and Assembly Shop models were developed. Each of the models is explained below in details.

Body shop model

The Body Shop has a total of 35 stations. The skid system of the Body Shop was included in the simulation model. There is a marriage station, at which a body is placed on a particular skid and remains attached to this skid until it gets to a divorce station whereupon the two separate. The current design consists of 35 skids, whereas the skid system or skid cycle contains 45 stations, with 38 stations where a body can be stationed. The number of skids serves as a natural constraint on the in-process inventory of the Body Shop, with 35 being the maximum number of bodies in the cycle.

The cycle time, which is defined as the total amount of time it takes for a body to enter the station, receive work, and leave the station, is different from 178 to 209 seconds depending on the type of bodywork completed at the station. The mean cycles between failures (MCBF) and mean time to repair (MTTR) for the Body Shop are different from station to station. No repair time takes more than 23 minutes, and the minimum repair time is modeled as two minutes. The mean cycles between failures (MCBF) were converted into mean time between failures (MTBF) by multiplying the cycle counts by the station cycle time to make analysis easier with SIMAN.
The belt conveyor of the Body Shop is modeled as a no-gap conveyor. No-gap implies that there are no empty positions on the conveyor. This means that the conveyor will stop if there is no body waiting to be transferred at the station just after the divorce station. The stations on the conveyor are manual stations. Each station is designed to complete its work within the takt time of 215 seconds. The conveyor also has a certain operational downtime associated with it. The downtime can be due to mechanical failure of the conveyor, or due to a mechanical failure at the stations. Any failure stops the conveyor.

Paint shop model

The Paint Shop has 101 stations in total. The cycle times are quite different from 6 to 300 seconds. The mean time to repair (MTTR) is modeled as five minutes, with no repair taking more than 23 minutes, or less than two minutes. Several features unique to the Paint Shop are modeled. During times of scheduled failures or breaks, bodies empty into designated strip out areas. For the purpose of the simulation model, three distinct pre-strip out areas and strip out areas are defined. The system is modeled such that a body may not enter any pre-strip out area unless there is enough space in the strip out area to hold at least 70% of the bodies in the pre-strip out area. Second, parallel lines are modeled such that under normal operating conditions bodies are sent to the two stations alternately. If one line fails, bodies are sent to the line that is operating. Third, major repairs and spot repairs on a body are modeled. A total of 11.3 % of all bodies are sent to the major repair area and a total of 12.6 % of all bodies are sent to the spot repair area. A body may not receive more than two major repairs or more than 4 spot repairs.

Not all Paint Shop conveyors are modeled using the SIMAN Conveyors element. Some of them are modeled as queues and resources. This is performed to more accurately model special features, such as no-gap conveyors.

The conveyors that were modeled as resources simply follow the operating schedule by means of the SIMAN Schedules element and the machine breakdown by means of the SIMAN Failure element. For the conveyors that were modeled with the Conveyor element, a small submodel is written to start or stop the conveyors at the appropriate times to model the operating schedule and the machine breakdown.

Selectivity bank

The Selectivity Bank was developed as a C++ program. It is an external program that is accessed via files and event statements. The purpose of the Selectivity Bank, a 24- position buffer between the Paint Shop and the Assembly Shop, is to restore the sequence of cars coming out of the Paint Shop and going into the Assembly Shop. The various options available on the AAV take different amounts of time to install. It is therefore important to balance the sequence of vehicles in a way that provides the smoothest assembly process over time.

The Selectivity Bank is laid out in a series of six rows. Each row has four positions. This is illustrated in Figure 2. The numbers in the cells represent the sequence numbers of bodies. Positions without a body are marked as “empty.” As bodies enter the Selectivity Bank from the left, they are directed to the row with the fewest bodies. In this case, a car would be placed on row 6, since it has the fewest number of bodies.

There are rule sets that control the Selectivity Bank. Rule sets contain the criteria for selecting the next car to leave for the Assembly Shop. The rules are currently divided into three separate rule sets. The oldest car in the Park, Matson and Miller Selectivity Bank will be chosen as the first candidate for leaving for the Assembly Shop. It will have to pass all three rule sets in order to be removed from the Selectivity Bank. If it fails any of the rule sets, then the next oldest car will be selected for evaluation and the process continues until a car passes all three rule sets. If no car passes, then the oldest car is sent to the Assembly Shop.

Assembly shop model

The Assembly Shop consists of seven conveyor lines: Trim 1 Line, Trim 2 Line, Final Line, Chassis Line, Door Line, Engine Line, and Axle Line. Of the seven, the first four conveyors are modeled as non-accumulating conveyor systems, but the last three are modeled as resources (Savory and Mackular 1997). Buffers between the Trim 1 conveyor, the Trim 2 conveyor, the Final conveyor, and the Chassis conveyor lines define in-process storage. The cycle times used for each station on the conveyors and the lifter station is 3.6 minutes (216 seconds). All idle stations including buffers are assumed to cycle in 15.7 seconds. The assembly lines are assumed to incur mechanical breakdowns on a random basis. Initially, the mean time between failures (MTBF) and the mean time to repair (MTTR) were calculated using uptime percentages and repair times provided by the Chiyoda engineering subcontractor. But the MTBF and MTTR were revised to represent a more realistic situation after discussion with the MBUSI engineers. A truncated exponential distribution was assumed for both parameters (Jayaraman and Agarwal 1996). It was assumed that repair times would take 5 minutes on the average, and will never exceed 23 minutes or be less than 2 minutes.

Two types of input data are defined for the simulation models, operational data and process data (Thomson 1995). Operational data includes all data relevant to the general operations of the system, such as the layout schematics of the shops and the operational logic of the shops. Process data are the specific data used to drive the shops, such as the time between failures, the time to complete a repair, the daily operating schedule, the repair percentage, and the conveyor speeds and types. MBUSI engineers provided the operational data and the process data were primarily obtained from the company’s Standard Method & Procedures (SM&P) sheets.

After the models were developed, they were verified with the SIMAN trace reports in details. The SIMAN trace reports can be a very useful tool for model verification because it details the movement of entities (vehicles) from block to block and the processing of each entity at each block. Once the models have been verified with the SIMAN trace reports, each of them was again verified and validated through a series of meetings with MBUSI engineers and the project contact person. After deciding that the four individual models were correct, they were combined to form an integrated model of the MBUSI assembly plant.

Analysis

The integrated simulation model of the assembly plant has been run with the vehicle data generated from the SM&P Sheets as mentioned. Each run of the simulation model used a warm-up period of five days followed by 20 replications of one day each. Each day is 24 hours in length, contains two shifts, and operates according to the production schedule provided by MBUSI. This schedule includes the time required for all work and breaks. A run length of 20 days was chosen because it accurately portrays the behavior of the system. Comparisons were made between run lengths of 20 days and 100 days, and the differences in output were statistically insignificant.

Using the completed integrated model, statistics regarding the total number of cars in each of the three shops at any given time were collected. These statistics were generated using counters: a counter for a particular shop was incremented when a car entered the shop and the same counter was decreased when a car left the shop. The main objective of this study was to verify that the daily throughput requirements could be met by the current design of the AAV assembly facility. Additional objectives were to address the other management issues, such as the maximum throughput (capacity) of the facility, the daily distribution of throughput, the buffers’ behavior in terms of quantity fluctuations, and the possible bottlenecks to the desired throughput. Each of the objectives is taken into consideration during the process of simulation analysis. The results of simulation analysis are summarized below in terms of each objective.

Throughput: The issue of whether or not the current design can produce the required throughput of 270 cars per day was addressed by studying confidence intervals representing the throughput values for each of the three shops. The display for each statistic provides both the minimum and maximum values recorded for the statistic.

It also provides the 95% confidence intervals with the average value clearly marked. The confidence interval indicates the range of values in which one can be 95% confident that the actual value will fall. Table 1 provides the average throughputs with their 95% confidence intervals for the Body, Paint, and Assembly Shop. Simulation and Analysis of The Mercedes-Benz All Activity Vehicle (AAV) Production Facility

Inspection of the confidence intervals for the Assembly Shop daily throughput value indicates that the average throughput for the facility is 257 cars per day. Ninety–five percent of the time, the daily throughput value falls between 254 and 260 cars per day. These results indicate that the current design does not produce the required throughput of 270 cars per day.

Maximum Throughput Capacity: The maximum throughput capacity, also known as the theoretical maximum capacity, of the facility was determined by running the model without the effects of unscheduled downtime for a warm-up period of five days followed by 20 replications of one day each. A maximum throughput capacity of 289 cars per day was observed.

Buffer Fluctuations: Statistics regarding the fluctuation of the buffers of the Body Shop, the Paint Shop, and the Assembly Shop were collected. Specifically, in the Assembly Shop, the Trim 1 buffer remains full the majority of the time while the Chassis buffer remains empty almost 40% of the time, which indicates that a lack of chassises is a problem in the Assembly Shop. Each body must be paired with a chassis before final line processing can begin. If the chassis buffer is empty a large percentage of the time, then bodies are delayed until a chassis becomes
available.

Bottlenecks: After running the Body, Paint, and Assembly Shop models separately and comparing the individual model results to the integrated model results, it becomes evident that the Assembly Shop is the bottleneck to the desired throughput. This is best indicated by the fact that the Selectivity Bank remains full a large majority of the time, causing cars to back up onto the end of the Paint Shop. This implies that the Paint Shop is processing bodies faster than the Assembly Shop.

In an attempt to improve the Assembly Shop throughput, an experiment was run where the Chassis Line operated on a different schedule. For this particular experiment, the Chassis Line lifter was run longer at the end of each shift so that the Chassis buffer could fill and therefore begin each shift at its maximum capacity.

The results of this experiment do not show any improvement in terms of the average throughput, which indicates that the Chassis Line alone is not the only impediment to the system. Other throughput improvement scenarios need to be run and evaluated. These throughput improvements will focus on the speeds of the Trim 1, Trim 2, Chassis, and Final lines.

Conclusion

The purpose of the study was to develop a detailed simulation model of the AAV assembly plant to determine whether it can reach the targeted output of 270 cars per day. The models of the Body Shop, Paint Shop, Selectivity Bank, and Assembly Shop were developed and analyzed based on a five-day warm-up period followed by twenty one-day replications. Statistics are collected from each replication and tested statistically for their significance. Examination of the results reveals that the current plant design cannot consistently yield 270 cars per day. The fact that the Selectivity Bank remains full most of the time indicates that the Assembly Shop is the current bottleneck to the system.

It is recommended that work on the detailed Assembly Shop model continue. Once this work is complete, the model will provide a more accurate evaluation of the performance of the production facility as a whole. The model can then be used to study issues such as how to increase the daily throughput goal of the plant to a number other than 270 cars per day.

The completed integrated model, including the more detailed version of the Assembly Shop, may be used by MBUSI to explore several different issues. For instance, ways to increase daily throughput of the plant beyond the current goal of 270 cars per day may be studied. For a given desired throughput, the model can be used to determine the required cycle times and conveyor speeds. Completion of this task will provide MBUSI an accurate indication of how well the facility will perform under higher throughput demands.

Author biographies young

1. PARK is a Professor of Industrial Engineering at the Kwandong University in Koria. Dr.Park has his Ph.D. in Management Science from the Park, Matson and Miller University of Alabama, an MBA from the University of Arkasas, and a BA in Business Administration from the Kangweon National University. Dr. Park’s research interests are in the areas of system simulation, management information system, and statistics. Dr. Park is currently a visiting scholar at the Alabama Productivity Center, University of Alabama. JACK E. MATSON has over ten years of industrial experience as a manager with the Bell System in MIS and strategic planning and four years as the owner of a small business. Since 1991, has been a faculty member in the Industrial Engineering Department at the University of Alabama where he has taught the management-related courses in the Industrial Engineering curriculum, including engineering management, engineering economics, statistics, quantitative methods, simulation, and project and systems design.

The companies that he has consulted with include Mercedes-Benz, ACIPCO, Alabama Power Company, Goodyear Tire, NASA, Delphi Saginaw Steering Systems, as well as smaller industries such as Gulf States Steel, Royal Cup Coffee, Speedring, and American Olean. Dr. Matson received the Ph.D. in Management Science from The University of Alabama and the M.S. in Industrial Engineering from Mississippi State University. DAVID M. MILLER is currently a Professor of Management Science at the University of Alabama as well as Director of the Alabama Productivity Center. His professional honors include being appointed as the Reese Phifer Faculty Fellow in Manufacturing Management, selection as a Fellow in the World Academy of Productivity Sciences, appointment as a 1992 Malcolm Baldrige National Quality Award examiner as well as being listed in the International Who’s Who in Quality and the Who’s Who in Technology. Dr. Miller holds a Ph.D. in Industrial Engineering and Operations Research from the Georgia Institute of Technology. He also holds a masters in Industrial Engineering from Georgia Tech, along with a BS degree in Industrial Engineering from the University of Alabama. He has published over 45 professional articles in journals such as the Harvard Business Review and Management Science, as well as a text book on Industrial Engineering. In his capacity as Director of the Alabama Productivity Center, Dr. Miller oversees a $750,000 annual operation involving 15-18 industrial projects and 30 graduate students. Since starting the Center in 1986, he has directed over 200 projects in industries ranging from steel fabrication to apparel.

Statistics Midterm paper

Identify the implied population in the information here. Government agencies carefully monitor water quality and its effect on wetlands. Of particular concern is the concentration of nitrogen in water draining from fertilized lands. Too much nitrogen can kill fish and wildlife. Twenty-eight samples of water were taken at random from a lake. The nitrogen concentration (milligrams of nitrogen per liter of water) was determined for each sample.

1. The variable in this information is nitrogen concentration (mg nitrogen/l water).

• a. number of fish
• b. samples of water taken at random
• c. the wetlands d. nitrogen concentration (mg nitrogen/l water) in the entire lake
• e. government agencies

2. Find the technique for gathering data in the study below. A study of all-league football scores attained through touchdowns and field goals was conducted by the National Football League to determine whether field goals account for more scoring events than touchdowns (USA Today).

• a. sampling
• b. census
• c. experiment
• d. simulation
• e. observational study.

3. It’s not an easy life, but it’s a good life. Suppose you decide to take the summer off and sign on as a deckhand for a commercial fishing boat in Alaska that specializes in deep-water fishing for groundfish. What kind of fish can you expect to catch? One way to answer this question is to examine the reports on groundfish caught in the Gulf of Alaska. The following list indicates the types of fish caught annually in thousands of metric tons:

• a. Flatfish, 36. 3;
• b. Pacific cod, 68. 6
• c. sablefish, 16. 0;
• d. Walleye Pollock, 71. 2;
• e. Rockfish, 18. 9.

Make a Pareto chart showing the annual harvest for commercial fishing in the Gulf of Alaska.

4. How hot does it get in Death Valley? Assume that the following data are taken from a study conducted by the National Park System, of which Death Valley is a unit. The ground temperatures were taken from May to November near Furnace Creek. Compute the mode for these ground temperatures.

147 153 167 174 182 178 179 182 178 178 167 0 153 144

• a. 144
• b. 182
• c. 167
• d. 153
• e. 178

5. Find the sample variance s2 for the following sample data. Round your answer to the nearest hundredth. x: 23 17 12 35 29

• a. 84. 20
• b. 67. 36
• c. 101. 00
• d. 88. 84
• e. 126. 25

6. Do bonds reduce the overall risk of an investment portfolio? Let x be a random variable representing an annual percent return for the Vanguard Total Stock Index (all Stocks). Let y be a random variable representing annual return for the Vanguard Balanced Index (60% stock and 40% bond). For the past several years, assume the following data.

Compute. x: 14 0 36 23 33 25 26 14 14 23 y: 6 5 26 17 24 17 17 5 6 6

• a. 4607
• b. 4803
• c. 5332
• d. 4243
• e. 4940

Answers:

Question 1: The population is d. – the nitrogen population in the entire lake.

Question 2: The technique is a census since all events are to be measured.

Question 3: The Pareto Chart

Question 4: The mode is the most frequently occurring value in a set of data so here the mode is 178 so the answer is e.

Question 5: The sample variance is 84. 20 so the answer is a.

Question 6: The mean return for the Vanguard Total Stock Index is 20. 8 while the mean return for the Vanguard Balanced Index is 12. 9 (with bonds). Based on this data you would conclude that bonds do not reduce the overall risk of an investment portfolio since the mean return was actually less when the portfolio has bonds in it.

To carry out this test of comparative data from the United States Dollars and Japanese Yen in comparison with the UK Pounds, the test must follow a clear set of step by step plan. The Question of whether PPP theory was tested in the following three steps. The analysis was carried on a daily basis of for a total of 10 years. The interest rate difference data was taken for a period of seven years taking 1999 as base year. 1. The initial step and predominantly important step was to acquire the data required from Perfect Analysis for the periodic exchange rates of the British Pound and the Japanese Yen for the past 10 years.

These figures then were then placed in an Excel Spreadsheet and monthly figures were derived, eliminating all unnecessary dates were eliminated. Using Micro Soft Excel, the figures, the next task was to derive the percentage change in the exchange rates using the PPP formula stated: 2. Secondly once again using perfect analysis we then derived the relevant monthly interest rate difference figures between that United States and of the UK as well as Japan. As previously done these differences were placed in Excel and steps were taken to work out the inflation or deflation rates which are the changes in the index which have been calculated.

From that the inflation differential can be derived taking the US inflation rate and taking away the UK inflation rate as well as for US and Japanese rates. US (h) – UK (f) 3. Once all the information was derived, Excel was used in order to calculate the percentage change between the US and UK as well as US and Japan inflation rate. Then PPP formula was used in order to calculate the differences in the inflation rates in both the countries. A regression analysis was carried out using Minitab.

Two analysis were produced to show the correlation in the exchange rate fluctuations over the period of 10 years. Similarly a regression analysis was done in the Minitab to show the correlation of the interest rate difference during the period under consideration. “Regression Analysis is used to approximate quantitative functional relationships between dependant variables and one or more independent casual variables from actual data – experimental, time series, cross sectional – when the relationship among the variables is statistical in nature rather than exact.

By a statistical relationship it is meant that the dependant variable’s observed values are generated by a probability distribution that is a function of other causal variables” Within this analysis the P value is the probability that the coefficient is Zero. The smaller the number the more significant it is. If the P value ranges between 5 and 10 percent the value is deemed to be nearly significant. The F score is in regard to the explanatory power of R-sq. This all tells you what combined explanatory power regression is and from how much is being explained and how much is not.

The T score is only of any significance when its value is greater than 1. 06. The co-efficient is a measure of how much a relationship two variables show based on a scale of -1 to +1. The figure shows the change in Y if the X variable was increased by 1. And finally the coefficient of determination R-Sq. It is measured of what is classified as a ‘goodness of fit’ of a particular regression model. R-Sq takes a value between -1 and +1. Negative values arise when the two variables are inversely related and positive values occur when they are correlated positively.

EFFECTIVENESS OF HANDLING GUEST COMPLAINTS BY FRONT OFFICE DESK STAFF AS OBSERVED BY THE GUEST AT SELECTED HOTELS A Research Presented to the Faculty of College of Hospitality and Institutional Management Our Lady of Fatima University In Partial Fulfillment of the Requirements for the Degree Bachelor of Science in Hotel and Restaurant Management ROBIN JUDE B. ELAURIA KATRINA CARLA G. GERALDINO AILEEN JOY A. QUIDULIT CHRISTOPER S. ROSALES GENESIS D. C. SUSANA October 2011 ACKNOWLEDGEMENT

The researchers would like to extend their earnest gratitude for the people who made it possible for them to finish this research study. This would not be achievable without the help and supervision of the following people who are their motivation in doing this research study. To the cherished guest’s of Nice Hotel most especially to Mr. Renold Zenarosa Branch Manager of Nice Hotel Mandaluyong and Mr. Lawrence Villanueva Branch Manager of Nice Hotel Cubao Quezon City who allowed us to conduct our survey for their pilot and actual study.

To Ms. Maria Paz T. Castro, our adviser, who’s not tired of answering our question and few complains, teach us on how to exert effort in every task that we should do and sharing her knowledge and expertise in doing this research guiding until the final defense. To Mr. Cledante Navalta, our statistician, for their effort and time in plateful them to accomplish the statistical analysis of the research studies. To our parents who always supported us for financial all the way through the process of our research studies.

Last but not the least, the omnipresent God, for answering our prayers for giving us the strength to plod on despite our constitution wanting to give up and throw in the towel make us realize that there’s always a key in every lock, Thank you so much Dear Lord. ABSTRACT Title: EFFECTIVENESS OF HANDLING GUEST COMPLAINTS BY FRONT OFFICE DESK STAFF AS OBSERVED BY THE GUEST AT SELECTED HOTELS Proponents: ROBIN JUDE B. ELAURIA, KATRINA CARLA G. GERALDINO, AILEEN JOY A. QUIDULIT, CHRISTOPER S. ROSALES, GENESIS D. SUSANA Adviser: MS. MARIA PAZ CASTRO

Degree: BACHELOR OF SCIENCE IN HOTEL AND RESTAURANT MANAGEMENT Date Completed: OCTOBER 2011 The researchers conducted this study to determine the Effectiveness of Handling Guest Complaints by Front Office Desk Staff as Observed by the Guest at Selected Hotels. Specifically aims to answer the following questions about the profile of the respondents, how satisfied the respondents regarding their service satisfaction, recommendations that can provide solutions to the said problems and there is no significant relationship between the satisfaction of the respondents and their demographic profile of the respondents.

A descriptive method of research was used together with the questionnaire as the main instrument in gathering the needed data and information. Fifty (50) Guest at Nice Hotel in Cubao Quezon City was considered for the study. In analyzing and interpreting the data gathered, the statistical treatments used were, the percentage which was used to compare the frequencies of responses to the total number of responses, and the weighted mean which was used in measuring the Effectiveness of Handling Guest Complaints by Front Office Desk Staff as Observed by the Guest at Selected Hotels.

The chi-square test is used to determine whether there is no significant relationship between the expected frequencies and the observed frequencies in one or more categories. With all the gathered information, the researchers arrived at the following conclusion: (1) most of the guests are 30-39 years old, male, guests (2) most of them are satisfied regarding the effectiveness of handling guest complaints by front office desk staff at Nice Hotel in Cubao Quezon City. (3) It was recommended that the front office desk staff of the hotel should initiate talk with the guest to get feedback about their service to improve their service. 4) In terms of age and gender there is no significant relationship between the effectiveness in handling guest’s complaints by the front office desk staff in selected hotels and their demographic profile. In terms of educational attainment and status of employment there is a significant relationship between the effectiveness in handling guest’s complaints by the front office desk staff in selected hotels and their demographic profile TABLE OF CONTENTS Page TITLE PAGE…………………………………………………………………….. i APPROVAL SHEET………………………….. ……………………………….. ii ACKNOWLEDGEMENT…….. ………………………………………………iii ABSTRACT………………………………………………………………………iv TABLE OF CONTENTS………………………………………………………… v LIST OF TABLES………. …….. ……………………………………………….. ix LIST OF FIGURES………. …………………………………………………….. x CHAPTER 1. THE PROBLEM AND ITS BACKGROUND……………………………. 1 INTRODUCTION………………………………………………………1 Background of the Study…………………………………………….. 1 Statement of the problem………………………………………………………. 2 Hypothesis………………………………………………………………………….. 3 Significance of the study……………………………………………. 3 Scope and Delimitation of the study……………………………….. Definition of terms…………………………………………………….. 6 2. REVIEW OF RELATED LITERATURE AND RESEARCH STUDIES……………………………………………7 Related Foreign Literature………………………………………….. 7 Related Local Literature……………………………………………… 8 Related Foreign Studies…………………………………………….. 10 Related Local Studies……………………………………………….. 12 Conceptual Framework…………………………………………….. 14 Research Paradigm………………………………………………….. 16 3. RESEARCH METHOLOGY…………………………………………….. 17 Research Design…………………………………………………….. 17 Research Setting…………………………………………………….. 17 Research Subject…………………………………………………….. 18 Research Instrumentation…………………………………………… 18

Validation of Instrument………………………………………………18 Data Gathering Procedure……………………………………………18 Statistical Treatment………………………………………………………. 19 4. PRESENTATION, ANALYSIS AND INTERPRETATION OF THE DATA……………………………………21 Demographic Profile of the Respondent……………………………21 Common guest’s complaints handled by the front office desk staff in selected hotels in Manila…………25 Summary & Chi-square test on the significant relationship between effectiveness in handling guest’s complaints by the front office desk staff in selected hotels in Manila and the demographic profile of the respondents………. 28 5.

SUMMARY OF FINDINGS CONCLUSION, RECOMMENDATION….. 31 Summary of findings………………………………………………….. 31 Conclusions……………………………………………………………. 33 Recommendations……………….. ……………………………………34 REFERENCES…………………………………………………………35 APPENDICES…………………………………………………………. 36 A. Map of Research Setting…………………………………………37 B. Title Approval………………………………………………………38 C. Letter of Request for Conduct of a Pilot Study…………………… 39 D. Letter of Request for Conduct of Actual Study……………….. 40 E. Research Instrument…………………………………………….. 41 F. Hypothesis Testing………………………………………………. 49 G. Curriculum Vitae…………………………………………………. 50 List of Tables Table no.

I Demographic Profile of the Respondents 1. 1 Distribution of Respondents in Terms of Age21 1. 2 Distribution of Respondents in Terms of Gender22 1. 3 Distribution of Respondents in Terms 23 of Educational Attainment 1. 4 Distribution of Respondents in Terms 23 of Status of Employment Table no. II Common guest’s complaints handled by the24 front office desk staff in selected hotels in Manila Table no. III Summary & Chi-square test on the significant 26 relationship between effectiveness in handling guest’s complaints by the front office desk staff in selected hotels in Manila and the demographic profile of the respondents

List of Figures I. Conceptual Framework14 II. Figure 1 Research Paradigm16 Curriculum vitae GERALDINO, KATRINA CARLA G. Domingo de ramos street largo, Quezon city Contact no: 09461113536 Email address:Naughty_kc_21@yahoo. com ————————————————- EDUCATIONAL BACKGROUND 2009-Present : Bachelor of Science in Hotel and Restaurant Management Our lady of Fatima University Hilltop Subdivision, Lagro, Quezon City 2005-2008 : Secondary

Roxas National High School Roxas, San Isidro, Surigao del Norte 1999-2004 : Elementary Sto Nino Elementary School Sto Nino, San Isidro, Surigao del Norte ————————————————- PERSONAL BACKGROUND Gender: Female Age : 19 years old Birth date : June 09, 1992 Height : 5’4 Civil Status : Single ___________________________________ GERALDINO, KATRINA CARLA G. ELAURIA, ROBIN JUDE B.

Blk2 Lt27 Marvi Hills, Gulod Malaya San Mateo, Rizal Mobile number: 09213336967 Email address: elauriarobin@rocketmail. com EDUCATIONAL BACKGROUND| 2009-PresentBachelor of Science in Hotel and Restaurant Management Our lady of Fatima University Hilltop Subdivision, Lagro, Quezon City 2004-2008Secondary St. Mathhew College Miguel Cristi St. San Mateo, Rizal 1997-2003Elementary Gulod Malaya Elementary School Barangay Gulod Malaya, San Mateo Rizal PERSONAL BACKGROUND| Gender :Male Age:19 years old Birth date:November 12, 1991 Height:5’6 Civil Status:Single __________________ Robin Jude Elauria AILEEN JOY A.

QUIDULIT # 6 Geronimo St. Brgy Sta Monica Novaliches Q. C Contact no: 4828615/09155459133/09239238439 E-mail add: Aileenjoy_quidulit@yahoo. com ____________________________________________________________ __________ EDUCATIONAL BACKGROUND 2007 – Present: Bachelor of Science in Hotel and Restaurant Management Our Lady of Fatima University Lagro, Novaliches Quezon City Secondary 2003 – 2007:Holy Redeemer School of Kalookan Franville V. Subd. , Caloocan City Elementary 1996 – 2003:Rosa L. Susano Elementary School Brgy. Gulod Novaliches, Quezon City PERSONAL BACKGROUND Gender :Female

Age:20 yr/old Birth date:November 10, 1990 Weight:95 lbs Height:5’2’’ Civil Status:Single _______________________ AILEEN JOY A. QUIDULIT ROSALES, CHRISTOPER S. Blk 47 Lot 18 Area B lower 4 Sapang Palay City of San Jose Del Monte Bulacan Mobile number:09106184955 Email address:chrissiega@ymail. com/rchristoper@ymail. com EDUCATIONAL BACKGROUND| 2009-PresentBachelor of Science in Hotel and Restaurant Management Our lady of Fatima University Hilltop Subdivision, Lagro, Quezon City 2004-2007Associate in Hotel and Restaurant Management Academia De San Lorenzo Tialo Sto. Cristo, City of San jose Del Monte Bulacan 000-2004Secondary Sapang Palay National High School Area E Sapang Palay City of San Jose Del Monte Bulacan 1994-2000 Elementary Barangay Bagong Buhay III Elementary School Barangay Bagong Buhay III Area B SapangPalay City of SJDBMB PERSONAL BACKGROUND| Gender :Male Age:23 years old Birth date:November 05, 1987 Height:5’7 Civil Status:Single ____________________ Rosales, Christoper S. SUSANA GENESIS C. 83 E Maginoo St. Kalayaan Quezon,City Cell Number: 09151908382 Gen30_11susana@yahoo. com EDUCATIONAL BACKGROUND| 2009-Present Bachelor of Science in Hotel and Restaurant Mngt. Our Lady of Fatima University 1 Esperanza St. Hilltop Mansion Heigths Lagro Quezon, City 2000-2004 Amadeo National High School Amadeo, Cavite 1994-2000 Amadeo Elementary School Amadeo, Cavite PERSONAL BACKGROUND| Gender: Female Age: 23 Heigth: 5’4 Civil Status: Single _____________________ GENESIS C. SUSANA Computation Table 1 Table 1: Frequency and Percentage Distribution of Respondents in Terms of Age n=50 Age| F| P=f/n*100| %| Rank| 9 below| 3| (3/50*100)| 6| 5| 20-29| 13| (13/50*100)| 26| 2| 30-39| 17| (17/50*100)| 34| 1| 40-49| 12| (12/50*100)| 24| 3| 50 above| 5| (5/50*100)| 10| 4| Total| 50| | 100| | Computation Table 1. 1 Frequency and Percentage Distribution of Respondents in Terms of Gender n=50 Gender| F| P=f/n*100| %| Rank| Male| 27| (27/50*100)| 54| 1| Female| 23| (23/50*100)| 46| 2| total| 50| | 100| | Computation Table 1. 2: Frequency and Percentage Distribution of Respondents in Terms of Educational Qualification n=50 Educational Qualification| f| P=f/n*100| %| Rank| Elementary Grad. | 11| (11/50*100)| 22| 3| High School Grad. 14| (14/50*100)| 28| 2| College Grad. | 17| (17/50*100)| 34| 1| Post Grad. | 8| (8/50*100)| 16| 4| Total| 50| | 100| | Computation Table 1. 3: Frequency and Percentage Distribution of Respondents in Terms of Status of Employment n=50 Status of Employment| f| P=f/n*100| %| Rank| Worker| 19| (19/50*100)| 38| 2| Employee| 21| (21/50*100)| 42| 1| Self-employed| 10| (10/50*100)| 20| 4| Total| 50| | 100| | Table 2: Common guest’s complaints handled by the front office desk staff in selected hotels in Manila Situation| WM| Interpretation| Rank| 1. Missing of personal belonging| 3. 40| Good| 10| 2. Faulty equipments and facilities| 3. 2| Very Good| 7| 3. Lack of courtesy of the front office staff in dealing with the guest| 3. 64| Very Good| 3| 4. Slow and ineffective reservation procedures| 3. 82| Very Good| 1| 5. Neigbors intolerable noises| 3. 76| Very Good| 2| 6. Unsatisfactory of concierge| 3. 58| Very Good| 5| 7. Not well attended by front office staff| 3. 42| Good| 9| 8. Wrong room assignment or type of room given to the guest| 3. 56| Very Good| 6| 9. Poor service of the staff| 3. 48| Good| 8| 10. Delayed service of the front office desk staff| 3. 62| Very Good| 4| Grand Mean| 3. 62| Very Good| | Computation in terms of Age E=RT*CT/50Observed

Age| Poor| Average| Good| Very Good| Excellent| RT| 19 below| 0| 0| 0| 3| 0| 3| 20-29| 0| 0| 5| 7| 0| 12| 30-39| 0| 0| 9| 7| 1| 17| 40-49| 0| 0| 7| 6| 0| 13| 50 above| 0| 0| 2| 3| 0| 5| CT| 0| 0| 23| 26| 1| 50| Expected Age| Poor| Average| Good| Very Good| Excellent| 19 below| 0| 0| 1. 38| 1. 56| 0. 06| 20-29| 0| 0| 5. 52| 6. 24| 0. 24| 30-39| 0| 0| 7. 82| 8. 84| 0. 34| 40-49| 0| 0| 5. 98| 6. 76| 0. 26| 50 above| 0| 0| 2. 30| 2. 60| 0. 10| | | | | | | X? =(O-E)^2/E Age| Poor| Average| Good| Very Good| Excellent| 19 below| 0| 0| 1. 38| 1. 90| 0. 06| 20-29| 0| 0| 0. 05| 0. 09| 0. 24| 30-39| 0| 0| 0. 18| 0. 38| 0. 6| 40-49| 0| 0| 0. 17| 0. 09| 0. 26| 50 above| 0| 0| 0. 04| 0. 06| 0. 10| ?X? = 5. 56 Computation in terms of Gender E=RT*CT/50Observed Gender| Poor| Average| Good| Very Good| Excellent| RT| Male| 0| 0| 13| 14| 0| 27| Female| 0| 0| 22| 27| 1| 23| CT| 0| 0| 35| 41| 1| 50| Expected Gender| Poor| Average| Good| Very Good| Excellent| Male| 0| 0| 11. 88| 14. 58| 0. 54| Female| 0| 0| 10. 12| 12. 42| 0. 46| X? =(O-E)^2/E Gender| Poor| Average| Good| Very Good| Excellent| Male| 0| 0| 0. 11| 0. 02| 0. 54| Female| 0| 0| 0. 12| 0. 03| 0. 63| ?X? = 1. 45 Computation in terms of Educational Qualification E=RT*CT/50Observed

Educational Qualification| Poor| Average| Good| Very Good| Excellent| RT| Elementary Grad. | 0| 0| 4| 7| 0| 11| High School Grad. | 0| 6| 11| 1| 0| 14| College Grad. | 0| 0| 7| 9| 1| 17| Post Grad. | 0| 0| 4| 4| 0| 8| CT| | 6| 22| 21| 1| 50| Expected Educational Attainment| Poor| Average| Good| Very Good| excellent| Elementary Graduate| 0| 1. 32| 4. 84| 4. 62| 0. 22| High School graduate| 0| 1. 68| 6. 16| 5. 88| 0. 28| College Graduate| 0| 2. 04| 7. 48| 7. 14| 0. 34| Post Graduate| 0| 0. 96| 3. 52| 3. 36| 0. 16| X? =(O-E)^2/E Educational Qualification| Poor| Average| Good| Very Good| Excellent| Elementary Grad. | 0| 1. 2| 0. 15| 1. 23| 0. 22| High School Grad. | 0| 11. 11| 0. 11| 4. 05| 0. 28| College Grad. | 0| 2. 04| 0. 03| 0. 48| 1. 28| Post Grad. | 0| 0. 96| 0. 07| 0. 12| 0. 16| ?X? = 23. 61 Computation in terms of Status of Employment E=RT*CT/50 Observed Status of Employment| Poor| Average| Good| Very Good| Excellent| RT| Worker| 0| 0| 7| 12| 0| 19| Employee| 0| 0| 6| 4| 0| 10| Self-employed| 0| 0| 9| 11| 1| 21| CT| 0| 0| 22| 27| 1| 50| Expected Status of Employment| Poor| Average| Good| Very Good| Excellent| Worker| 0| 0| 8. 36| 10. 26| 0. 38| Employee| 0| 0| 4. 40| 5. 40| 0. 20| Self-employed| 0| 0| 9. 24| 11. 34| 0. 42| X? (O-E)^2/E Status of Employment| Poor| Average| Good| Very Good| Excellent| Worker| 0| 0| 0. 22| 0. 30| 0. 38| Employee| 0| 0| 0. 58| 0. 36| 0. 20| Self-employed| 0| 0| 9. 24| 0. 01| 0. 80| ?X? = 12. 09 Table 3 Summary & Chi-square test on the significant relationship between effectiveness in handling guest’s complaints by the front office desk staff in selected hotels in Manila and the demographic profile of the respondents Demographic Profile| ComputedX? | TabulatedX? | df| ? | Comparison| Decision| Conclusion| Age| 5. 56| 15. 51| 8| 0. 05| Less than| Accept Ho| There is no significant relationship| Gender| 1. 45| 5. 9| 2| 0. 05| Less than| Accept Ho| There is no significant relationship| Educational attainment| 23. 61| 16. 92| 9| 0. 05| Greater than| Reject Ho| There is a significant relationship| Status of Employment| 12. 09| 9. 49| 4| 0. 05| Greater than| Reject Ho| There is a significant relationship| Hypothesis Testing: Ho: There is no significant relationship between the effectiveness of handling guest complaints by the front office desk staff at selected hotels in Manila. Ha: There is a significant relationship between the effectiveness of handling guest complaints by the front office desk staff at selected hotels in Manila.

1st Hypothesis – For my first hypothesis I will investigate the relationship between the number of TV hours watched per week by the pupils against their IQ. I am going to use the columns “IQ” and “Average number of hours TV watched per week” taken from the Mayfield high datasheet. I think that there will be a relationship between them and will attempt to reveal it.

2nd Hypothesis – For my second hypothesis I will investigate the relationship between “Average number of TV hours watched per week” and “weight (kg)”. I think that there will not be any major relationship between as they will not affect each other greatly.

I will present my analysis and the results in graphs and tables and explain the results using the correlation of the graphs and arrangements of the figures.

I will select a number of pupils to base my data on and will use random sampling to ascertain the correct number of male and female pupils needed to make the investigation fair.

Stratified Sampling

I do not want to use all of the data in the database for my analysis so I will need to take a sample of the number of people in the school. I would like to take about 10% of the overall figure. I will also need to use stratified sampling to make it an equal proportion of the number of males and females in the school to make it fair.

The total number of pupils at the school is 813 so I will need to take 10% as my number, 81.3 is rounded down to 81.

The overall ratio for boys and girls in the school is: 414:399

Now I will need to do my sampling

Males = 414 multiplied by 81 = 41

813

Females = 399 multiplied by 81 = 40

813

Random Sampling

Now I have the number of samples I will need to select the samples I will be taking. To do this I will use random sampling. I will take random samples until I have 81. I can do this on Excel using the following formula: = round(round()*120.

Once I have gathered the samples I am ready to start analyzing my samples.

Analysis

Hypothesis 1 Males

The first thing I need to do in my analysis is to analyze my graphs which are the source of the investigation. I have created scatter graphs to show the relationship if the two data sources for my first hypothesis. I have separated them into male and female graphs as there is a separation in the numbers.

First male scatter graph:

This first graph presented a bit of a problem. There was an anomalous result that affected the trend line and the scale of the graph. I decided to create a new graph that didn’t include that 1 piece of data. This way it would help me to analyze the rest of the data.

Second male scatter graph:

This graph showed the data much clearer and I could then start analyzing it. There is no correlation between the 2 sets of data. This means that it is unlikely that there is a relationship between IQ and Average number of TV hours watched per week. In this it may be that my hypothesis is incorrect. There is only a very slight gradient on the trendline that leans towards a negative correlation, but the gradient is not steep enough to draw any conclusions about the relationship between the two sets of data. I will have to use the cumulative frequency graphs and boxplots to see if any conclusions can be made.

Cumulative frequency graphs for IQ and Average number of TV hours watched per week:

From these graphs I could create box plots and compare the two sets of data. Before that I analyzed the cumulative frequency graphs to draw initial conclusions. The majority of the IQs for males are between 90 – 105, this shows that the data is quite spread out as this section only covers a small area of the graph. For the TV hour’s graph, again the data is spread among 1 main area; in this case it is between 5-25. There is almost a straight line near the top of the graph; this shows that there is likely to be some anomalous results and 0 pupils in between that result and the main bulk. Now I will create box plots so I can compare the two graphs together.

Box plots for cumulative frequency graphs of IQ and average number of TV hours watched per week: (for interquartile ranges look at copies of graphs at the back)

From the box plots I can see that the data spread is relatively the same apart from a possible anomalous result in the TV hour’s data. This similarity is the reason why the scatter graph had no correlation and therefore no relationship. This means that my hypothesis is wrong.

Hypothesis 1 Females

Again I will start with the scatter graphs. As with the male graph I had an anomalous result that spread out the data and scale down the graph so most of the relevant data couldn’t be analyzed. I then did another graph without that specific piece of data.

Scatter Graphs 1 and 2 to show the relationship between IQ and average number of TV hours watched per week for Females:

As you can see on both the graphs there is no correlation between the two sets of data. This again means that my first hypothesis is unlikely to be correct. There is only a slight gradient on the trend line which is not steep enough to draw any conclusions from it. There is another anomalous result on the graph but it doesn’t affect the trend line and my conclusions so I left it on the graph. I will now crate cumulative frequency graphs to see if they can help me to draw conclusions.

Cumulative frequency graphs for the IQ and number of TV hours watched per week:

I will now analyze the graphs before drawing box plots to compare the graphs. The IQs graph is much more erratic which means that the data is spread over a larger range. Although there is 1 area where the data is concentrated and the gradient very steep, between 95-105. The TV hours graph is much smoother and the data less spread. The data number of hour’s increases steadily to a certain point then it goes flat until the end. This means that there is a n anomalous result somewhere. I know that it can only be 1 or 2 anomalous because the point where it goes flat is at about 38 and there are only 39 sets of data in the graph. I will now look at the box plots to compare the two cumulative frequency graphs.

Box plots for cumulative frequency graphs of IQ and number of TV hours watched for females:

The box plots for these graphs show me that the IQ data has a much larger range and that it is quite evenly spread. I can see this because the interquartile range is quite large and the median evenly spread. There may be a few exceptions as 1 pupil is likey to have a very low IQ which is why the lowest value is so low. The TV hour’s data seems to be much more concentrated and the data is generally lower. This shows that there can’t be any relationship between them as they each grouped in certain areas. Also the box plot for TV hours shows that there is likely to bge an anomalous result as the highest value is so far out of the upper quartile.

Hypothesis 2 Males

In this hypothesis I will be comparing the Average number of TV hours watched per week and Weight, to see if there is any relationship between them. I will again start with Males and the Scatter graphs.

Scatter graphs 1 and 2 to show the relationship between Weight and the Average number of TV hours watched per week for males:

In these scatter graphs there is a slight negative correlation. This means that as the number of TV hours goes up Weight goes down. This may not be an accurate graph as there are a few anomalous results that may have caused the trend line to be that gradient. If this is so my hypothesis would have been correct, if it is not the gradient of the trend line isn’t steep enough to say that it is 100% certain that it is accurate. I will need to use the cumulative frequency graphs to draw complete conclusions.

Cumulative frequency graphs for the number of TV hours watched and Weights of males:

These two graphs look quite different; the weights graph has most of its data concentrated in the middle of the range, between 30-50 and looks like a normal cumulative frequency curve. Whereas the number of TV hours has most of its data concentrated at the beginning between 0-30, showing that there is likely to be an anomalous result at the end of the range. These anomalous results on the TV hours graph are what caused the slight negative correlation on the trend line. I will be able to make complete conclusions after looking at the female sample and seeing if that graph follows suit. The box plots for these graphs will look quite different and will make it easy to make a simple comparison.

Box plots for Cumulative frequency graphs IQ and Weight for males:

From the box plots I can see that the two sets of data are almost identical in range which would cause a straight line on the scatter graph it is because of the anomalous results on the TV hours which caused the slight negative correlation. The weights box plot shows me that the data is quite evenly spread in the middle of the range apart from a very heavy person at the end which is why the highest figure is so far apart from the upper quartile. Overall the box plots show me that the similarity in the data means there is no relationship and hypothesis was correct.

Hypothesis 2 Females

Again I will start with the scatter graphs to show the relationship between Number of TV hours watched and weight. The graphs should be similar to the males and the conclusions the same. Again I had an anomalous result and had to create a second scatter graph without it there.

Scatter graphs 1 and 2 to show the relationship between the Number of TV hours watched per week and Weight:

The second scatter graph in this section, without the anomalous result completely changed the trend line. The first graph looks a lot more like the male graph whereas the second follows my hypothesis a lot better. In graph 1 there is a slight gradient on the graph which points towards a negative correlation, like those of the male sample. On the graph without the anomalous result there is clearly no correlation whatsoever as the line is nearly horizontal. I will take the results of the male sample to be wrong as I said earlier there are a few anomalous results which caused the trend line to be at that gradient. Now I will look at the cumulative frequency graphs to see what results I get from them.

Cumulative frequency graphs for Average number of TV hours watched per week and Weight for Females:

As on the males graph the TV hours for females have a lot of anomalous results. But for the scatter graphs I cancelled them all out which gave no correlation. If the line at the top of the TV hours graph is blanked out the two graphs look almost identical. This is why the scatter graph got a near horizontal trend line. The box plots for these to graphs will look alike apart from there will be a much longer line at the end of the TV hours graph because of the anomalous results.

Box plots of cumulative frequency graphs for Number of TV hours watched and weights of females:

These box plots show me the same as the males did, that the data is almost identical if placed 1 on top of the other. This is what caused the horizontal line in my scatter graphs and proves my hypothesis.

Conclusion

Hypothesis 1: My first hypothesis has been proved incorrect. The scatter graphs show that there is no correlation between the two sets of data. For my hypothesis to have been correct there would have needed to be a strong positive correlation. The cumulative frequency graphs and box plots again proved my hypothesis incorrect, the similarities in the two sets of data’s box plots showed that there was no relationship and showed why the scatter graphs showed a straight line. Both the male and female samples showed that my hypothesis was incorrect although some anomalous results created a slight negative correlation in both it was obvious that it was still wrong.

Hypothesis 2: My second hypothesis was proved correct. The scatter graphs showed that there was absolutely no correlation on the graphs which means no relationship. Although the male graphs did show a a negative correlation it was proved to be made by a few anomalous results by the cumulative frequency and later the inconsistency with the female sample. The female scatter graph showed a near horizontal trend line which was what I needed to prove my hypothesis. The similarities on the cumulative frequency graphs and box plots further proved my hypothesis was correct.

Evaluation

The investigation went quite well although my first hypothjesis was incorrect it showed that careful analysis of data is needed before drawing conclusions. When I next do an investigation into data I will use histograms to aid me in my analysis as they come in useful when looking for relationships in two sets of data as the cumulative frequency graphs do. I could have made the cumulative frequency graphs a little better as the program I used did not put a scale on the x axis but only the length of the range.

Executive Summary

This paper aims to prove whether thee is effect of being Trend-minded; Relaxed; Paranoid; Thorough; Irresponsible; Positive-minded, Pro-active; Responsible Parent; and Patriotic/Loyal, based on recorded results of survey questions for Daimler Chrysler’ customers. Applying statistical tools, this researcher came up with the computed F greater than the tabular values of F-statistics at 0.05 degree of freedom which indicates significance of identified variables with the sole dependent variable of attitude to consider buying Daimler car unit name Viper. Results also reflected a small value of MSE denominator over the MSE numerator thus indicating that the estimator is a good fit of the regression. Multiple coefficient of determination also indicated that the data produced a good set of predictor variables. This is also confirmed by the fact the adjusted R2 is closer to the unadjusted R2.

Background:

Using Daimler Chrysler’s data, there is evidence to show a declined car market trend starting in 1990 and ending in 2004. In 1990, the company has market share of 23.9 percent but in 2004 this went down to 18.3 percent. A good warning as that, Daimler Chrysler must do something it has to survive competition. The company thus made efforts to check the importance of styling, prestigious promotion and other product features based on what the market demands and it found a way on how to attract Yuppies crowds as part of their target market for new product called Dodge Viper but it needed research how to go through it.

Definition of the Problem

The main objective this research paper is to determine whether there is a significant relationship (difference) between the attitude to  consider buying a Dodge Viper and selected variables such as : Trend-minded; Relaxed; Paranoid; Thorough; Irresponsible; Positive-minded; Pro-active; Responsible parenting; and Patriotic/Loyal customers for  Daimler Chrysler.

Research questions

To get the need insights, the defined problem is crafted into relevant research questions as follows:

a. What steps in statistical analysis must be used to process and to summarize and display Daimler Chrysler’s given data?

b. What statistical test must be used to measure the risk associated with variables extracted and defined based from Daimler Chrysler’ data?

c. What statistical treatment is applicable to show existence of linear relationships between variables found in Daimler Chrysler’s data?

d. Is there a significant difference between the attitude to consider buying a Dodge Viper and selected variables such as being: Trend-minded; Relaxed; Paranoid; Thorough; Irresponsible; Positive-minded; Pro-active; Responsible parenting; and Patriotic/Loyal?

Hypotheses:

The null hypothesis, Ho is: There is no significant difference between the attitude to consider buying Dodge Viper and other variables under Daimler Chrysler’ given data.  The alternative hypothesis Ha is:  There is significant difference between the attitude to consider buying Dodge Viper and other variables under Daimler Chrysler’ given data

Case facts:

Data from Daimler Chrysler showed declined car market share from 1990 to 2004. From market share of 23.9 percent in 1990, it declined in 2004 to about 18.3 percent. This has therefore sounded alarm for Daimler Chrysler and the latter must act accordingly. Having checked the importance of styling and prestigious promotion and other variable in order to survive market competition, the company plans to attract yuppies crowds for its Dodge Viper.

As to how the company may package the product in respond to the need of the market in the subject of analysis of this paper. Questions surveys were given to customer and there are about 400 recorded data per customer that would answer about 31 questions. The data therefore represented 400 rows and 31 columns using SPSS and effort were made to find cluster out of related variable under the 31 columns as indicated.  The researcher made 9 clusters based by grouping related variables and new categories are now called under the new variable names as follows: Trend-minded; Relaxed; Paranoid; Thorough; Irresponsible; Positive-minded; Pro-active; Responsible parenting; and Patriot/ Loyalty.

Research design

·    Sample Size: From the 400 given dataset 9 cluster were produce using relevant relations among variable and afterwards the means of every clusters, were used in applying  multiple regression for determination of the predictors elasticity.

·    Sample selection: No sample was remove  from the dataset were used as a result of clustering

·    Editing of data:  SPSS standard procedures were applied:

·  Analysis of data: To find relationships among variables this paper performed simple tabulation and cross-tabulation.

RESULTS:

Results from the tabulation and cross tabulation indicated significant difference between the attitude to consider to buy Dodge Viper and selected variables such as: Trend-minded; Relaxed; Paranoid; Thorough; Irresponsible; Positive-minded; Pro-active; Responsible parenting; and Patriot/ Loyalty.  The researcher decided to apply the method of Multiple Regression model for the purposes of examining the effect of the other values in relation to the attitude to consider buying a Dodge Viper and  other variables under Daimler Chrysler’s given data.  In presenting the date, it was also necessary to use descriptive statistics was also used in this study for the presentation purposes. Graphs 1 below reflect the behaviour of elasticity of the variables.

Nonprofit organizations are providing many critical services (e. g. culture activities, environmental issues, education, healthcare) since the 16th century. But in the last couple of decades, Non-profit organizations are facing a lot of difficulties. The government has decided to reduce costs and therefore a significant reduction in governmental funding of donation programs has been made.  For instance, in England, the government has decided to cut in charity funding. The non-profit sector in England will receive? 10m less this year. Government regulations are not the only difficulty non-profit organizations have to deal with. Due to the economic crisis, the donations of non-profit organizations are decreasing. Only 12% of the non-profit organizations in the United States of America expect to run above the break-even point this year. Non-profit organizations that think they are able to cover their operating expenses are only 16 percent in both 2009 and 2010. People are saving rather than spending their money. The first cost they cut is giving money to charity. This is why the economy is slowly recovering. But at the same time, the number of volunteers is increasing. A number of non-profit organizations has shown an explicit growth. In 1940 there were only 12. 500 non-profit organizations, in the United States of America. Today there are over 1,500,000 non-profit organizations registered. This is an increase of 12,000 %. Which lead to much more competition in the non-profit organization’s sector. Therefore non-profit organizations are receiving less money.

If we sum up all the above we come to the following research question: What are the factors that have an effect on the intention to donate?

Hypothesis Development

A personal link to the cause and intention to donate To convince people to donate to a charity, it is important for non-profit organizations to understand that people who have a link to the purpose of an organization are more likely to help them realize their goals. Previous studies have shown that when an individual has a personal link to the goal of a non-profit organization, he or she will probably be more likely to donate to this organization E. g. If someone has just been cured of cancer, this person knows how it feels how it is to go through such an experience and would be more likely to donate to an organization that does research on a cure for this disease, like the American Cancer Society (ACS). The person donating doesn’t only do so because he/she has been told what good it would do for others, but mainly because this person has gone true the same experience as the one being donated to. The impact of this experience leads to a loyal donor. Therefore we expect that the more an individual is linked to the cause of an organization, the more likely this person is willing to donate.

H1: A personal link to the cause has a positive direct effect on the intention to donate.

Good brand personality of a non-profit organization and intention to donate. Another factor that plays a role in donating is brand personality. As can be read in previous studies, non-profit organizations with a bad reputation discourage people from donating to these organizations. For instance, Greenpeace has a very aggressive way of disapproving of certain companies or even certain government policies. They are often accused of being involved in illegal acts. This puts this organization in a difficult spot. People who are thinking of becoming a donor will take this into account. That is why it is important for a non-profit to have a good reputation. This can be achieved, for example, by providing a good service quality, keeping the donor informed what the organization is doing to reach its goal, but also how their money is being spent. For instance, if a manager of a large non-profit organization has an absurd high salary, people will lose their trust in the organization because they are not comfortable with the way their money is being spent. Therefore we assume it is necessary for an organization to be clear about its mission in order to create donor loyalty.

H2: A good brand personality of a non-profit organization has a positive direct effect on the intention to donate. 2. 3 Income in relation to the intention to donate People with a high level of income are more likely to donate to a non-profit organization because these people are able to buy their necessity goods, take care of their family, and are overall secure enough not have to worry about an uncertain future. As mentioned in the introduction, a higher level of income is defined as an income that is above standard, which is 65. 000 dollars a year. People with an income below standard are not always able to buy their necessities goods and can’t afford to donate. Let’s take students for example. They are already having problems coming around with their income and are not certain about their future, which will not lead to donating. Therefore we can posit.

H3: People with higher incomes are more likely to donate than people with lower incomes. The hypothesis relationships are explained in figure 1.

Figure 1. Conceptual Model.

Sample & Procedure

When it comes to analyzing the intention to donate, we are confronted with a large relevant population. Since there aren’t many requirements to become a donor, it is possible for anyone to donate to a non-profit organization. By approaching our unit of analysis (18+) through an online questionnaire we would like to get a better view of the intention to donate. This was done with the use of non-probability samples, where not all elements have the same chance of being included in a sample. In our case, we chose for convenience sampling since our population is quite vague and hard to define. This way we could be unrestricted, and it is easy to perform. The disadvantages that must be dealt with when it comes to holding a survey are that questions often remain multi-interpretable, the lack depth because of limited preparedness, and the respondents are more likely to give a socially acceptable answer.

Some ways to solve these problems are that every consumer must receive an e-mail invitation to participate in a survey. This gives the company a chance to make sure that the same name and contact information isn’t already assigned to another e-mail address in the system. Also, a minimum time for completing an online survey can be set. This cuts down on cheaters who fly through the survey just randomly answering questions. 3. 2 Measurement instruments An online questionnaire was created for respondents to participate in the survey using the following measurement instruments. Independent variables Personal link to the cause was measured with four ways of being connected to the charity (i. e. , Someone I know has been affected by the issues dealt with by this charity, Someone I know might benefit from my support, My family has a strong link to this charity, This cause is not related to an important aspect of my life). A sum score was calculated by adding up the responses to the question of whether respondents were offered these four options. Responses ranged from 1 (completely disagree) to 7 (completely agree). Good brand personality was measured by the quality of the brand name, with the availability of seven options (i. e. , Honest, loving, compassionate, Reputable, Committed, Reliable, Financially stable). A sum score was calculated by adding up responses to the question of whether respondents were offered these Seven options. Responses on this two sum score ranged from 1(completely disagree) to 7 (completely agree). Incomer was measured by asking respondents whether they had an income that was lower(0) or higher(1) than $65. 000.

Dependent variable

Intention to donate was measured with three statements: “Unlikely-Likely, Improbable-probable, Uncertain-certain”. Respondents could answer on a 7-point scale ranging from 1 (completely disagree) to 7 (completely agree).

Statistical analyses

With the purpose of testing the three hypotheses presented above, three analyses are performed. In order to test the first hypothesis (A personal link to the cause has a positive effect on the intention to do), a regression analysis will be calculated between the personal link to the cause and the intention to.

The second hypothesis

(A good brand personality of a non-profit organization has a positive effect on the intention to donate) is also tested via a regression analysis in which a higher good brand personality has an effect on the intention to donate.

The third hypothesis

(People with higher income are more likely to donate than people with lower income) is tested by means of an independent samples t-test. For all analyses, test values (r in case of the regressions and t in case of the t-test) with a significance of p?. 05 are deemed significant.

Descriptive statistics

The overall fit of the model: The 5. 9% of the variance of the dependent variable is explained by the model including the two independent variables.

A personal link to the cause has a positive effect on the intention to donate.

(Hypothesis 1) Hypothesis 1 was tested with a regression analysis. This way we can determine whether a (higher) personal link to the cause also leads also to the intention to donate. The raw SPSS output will be given in Appendix 1. As indicated by the analysis, the regression reveals a positive and insignificant effect between a personal link and the intention to donate (? -. 053 p;0. 26). Therefore, we have to reject Hypothesis A good brand personality of a non-profit organization has a positive effect on the intention to donate.

(Hypothesis 2) Hypothesis 2 was also tested with a regression analysis. This way we can determine whether a good brand personality leads to an intention to donate. The raw SPSS output will be given in Appendix 1. As indicated by the analysis, the regression reveals a positive and significant effect between a personal link and the intention to donate (? =0. 26, p;0. 001). Therefore, Hypothesis 2 is supported.  People with higher incomes are more likely to donate than people with lower incomes.

(Hypothesis 3) Hypothesis 3 was tested with an independent samples t-test. The raw SPSS output is given in Appendix 1. As indicated by the t-test, people with a higher income (M=5. 11) are significantly more likely to donate than people with a lower income (M=4. 86). (p ; 0. 02). Therefore, Hypothesis 3 is supported. In table 2 a summary of this study’s hypotheses will be given as well as the results of all hypothesis-testing analyses.

Conclusions

In this study, we have discussed two different factors (a personal link to the cause and brand personality) that have an effect on the intention to donate between people with higher income and people with lower income. According to the results of our regression analysis, people who have a personal link to the cause of a non-profit organization are not more likely to donate than people who don’t have a personal link to the non-profit organization. A possible explanation for this unexpected finding is that a personal link to the cause is an important factor for these organizations, but not sufficient for people to become more likely to donate. Previous studies have shown that there is a significant difference in the intention to donate and a personal link to the cause. A possible explanation for this unexpected finding is that there were not a lot of people in our survey who had a personal link to the cause. As we expected from our hypothesis people are indeed more likely to donate to a nonprofit organization with a good brand personality than to an organization with a perverse brand personality. The expected difference in the intention to donate between people with a higher income and people with a lower income was found in our analysis. According to our data, people are indeed more likely to donate when their income becomes higher. With this information, we can conclude that people take their income into account when it comes to making a donation.

Shortcomings and future research

One of the shortcomings of our study lies in the fact that we might have had some multi-interpretable questions with the lack of depth because of limited preparedness. A second restriction is that our analysis might be influenced by some personal bias. It could be that people were influenced to give a socially responsible answer. The third limitation is based on the fact that all our data was collected at one point at the time. If we would have found for example that people with a personal link to the cause were more likely to donate, we still could not conclude that this will always be the case; therefore you have to collect data over a longer period of time. If we sum up all shortcomings, future research on the intention to donate should focus on taking the survey separately, so that people could not influence each other and therefore not the outcome of the data. The data should be collected over a longer period of time to get a better insight if people with a higher income change their donation behavior.

Theoretical implications

What do we learn from this study? Was the existing theory confirmed or rejected? One theoretical assumption of this research is that a personal link to the cause and brand personality would lead to a higher intention to donate. This study shows however that this is not necessarily true according to the personal link to the cause of a nonprofit organization. Moreover, this research has shown that a higher income would have a positive effect on the intention to donate, as we expected.

Practical implications

One of the most important implications of the results we have found is that in practice non-profit organizations should not only focus on a certain group of potential donors but also come in contact with them through information. This way the donor will feel like a part of the organization as a whole. By letting the donors know what their future plans, initiatives, and successes are. These organizations should try to find active donors who will eventually become dedicated to their cause and will donate themselves.

Reference

1. Smith, N. 2011.
2. Charities ‘hit by funding cuts’ BBC News UK. Retrieved 16 November 2011 from http://www.bbc. co.uk/news/UK-politics-14366522 McKenna, T and Noble, C. (2009, March 3).
3. Nonprofit Finance Fund Survey: America’s Nonprofit in danger. Nonprofit finance fund. Retrieved16 November 2011 from http://nonprofitfinancefund. org/news/2009/nonprofit-finance-fund-survey-Americas-nonprofits-danger Rabe Thomase, J. (2010, June 21)
4. In a recession, non-profit agencies see volunteers increase as funding shrinks. The CT Mirror. Retrieved 11 November 2011 from http://ctmirror.org/story/6460/non-profits-gaining%20volunteers by. (2008)
5. Non-profits in Carlisle: History of Non-profits in the U. S. Carlisle History. Retrieved 18 November 2011 from http://carlislehistory.dickinson.edu/? page_id=278 by. (n. d. )
6. Building Relationships with Major-Gift Donors: A Major-Gift Decision-Making, Relationship-Building Model. Journal of Nonprofit & Public Sector Marketing, 21 (4), 384-406. Sargeant, A., & Woodliffe, L. (2007).
7. Building Donor Loyalty: The Antecedents and Role of Commitment in the Context of Charity Giving. Journal of Nonprofit & Public Sector Marketing, 18 (2), 47-68. Venable, B. T., Rose, G. M., Bush, V. D., & Gilbert, F. W. (2005).