Scientific Method

Introduction Exchange rates play a vital role in a county’s level of trade, which is critical to every free market economies in the world. Besides, exchange rates are source of profit in forex market. For this reasons they are among the most watched, analyzed and governmentally manipulated economic measures. Therefore, it would be interesting to explore the factors of exchange rate volatility. This paper examines possible relationship between EUR/AMD and GBP/AMD exchange rates. For analyzing relationship between these two currencies we apply to co-integration and error correction model.

The first part of this paper consists of literature review of the main concepts. Here we discussed autoregressive time series, covariance stationary series, mean reversion, random walks, Dickey-Fuller statistic for a unit root test. * The second part of the project contains analysis and interpretation of co-integration and error correction model between EUR/AMD and GBP/AMD exchange rates. Considering the fact, that behavior of these two currencies has been changed during the crisis, we separately discuss three time series periods: * 1999 2013 * 1999 to 2008 * 2008 to 2013. ——————————–

Autoregressive time series A key feature of the log-linear model’s depiction of time series and a key feature of the time series in general is that current-period values are related to previous period values. For example current exchange rate of USD/EUR is related to its exchange rate in the previous period. An autoregressive model (AR) is a time series regressed on its own past values, which represents this relationship effectively. When we use this model, we can drop the normal notation of Y as the dependent variable and X as the independent variable, because we no longer have that distinction to make.

Here we simply use Xt. For instance, below we use a first order autoregression for the variable Xt. Xt=b0+b1*Xt-1+? t Covariance stationary series To conduct valid statistical inference we must make a key assumption in time series analysis: We must assume that the time series we are modeling is Covariance Stationary. The basic idea is that a time series is covariance stationary, if its mean and variance do not change over time. A covariance stationary series must satisfy three principal requirements. Expected value of the time series must be constant and finite in all periods. * Variance should be constant and finite. * The covariance of the time series with itself for a fixed number of periods in the past or future must be constant and finite. So, we can summarize if the plot shows the same mean and variance through time without any significant seasonality, then the time series is covariance stationary. What happens if a time series is not covariance stationary but we use auto regression model? The estimation results will have no economic meaning.

For a non-covariance- stationary time series, estimating the regression with the help of AR model will yield spurious results. Mean Reversion We say that time series shows mean reversion if it tends to fall when its level is above its mean and rise when its level is below its mean. If a time series are currently at its mean reverting level, then the model predicts, that the value of the time series will be the same in the next period Xt+1=Xt. For an auto regressive model, the equality Xt+1 = Xt implies the level Xt = b0 + b1 * Xt or Xt = b0 / (1 – b1)

So the auto regression model predicts that time series will stay the same if its current value is b0/(1 – b1), increase if its current value is below b0 / (1 – b1), and decrease if its current value is above b0 / (1 – b1). Random Walks A random walk is a time series in which the value of the series in one period is the value of the series in the previous period plus an unpredictable error. Xt = Xt-1 + ? t, E(? t)=0, E(? t2) = ? 2, E(? t, ? s) = 0 if t? s This equation means that the time series Xt is in every period equal to its value in the previous period plus an error term, ? , that has constant variance and is uncorrelated with the error term in previous periods. Note, that this equation is a special case of auto correlation model with b0=0 and b1=1. The expected value of ? t is zero. Unfortunately, we cannot use the regression methods on a time series that is random walk. To see why, recall that if Xt is at its mean reverting level, than Xt = b0/ (1 – b1). As, in a random walk b0=0 and b1=1, so b0/ (1 – b1) = 0/0. So, a random walk has an undefined mean reverting level. However, we can attempt to convert the data to a covariance stationary time series.

We create a new time series, Yt, where each period is equal to the difference between Xt and Xt-1. This transformation is called first-differencing. Yt= Xt – Xt-1 = ? t, E (? t) = 0, E (? t2) = ? 2, E (? t, ? s) = 0 for t? s The first-differenced variable, Yt, is a covariance stationary. First note, that Yt=? t model is an auto regressive model with b0 = 0 and b1 = 0. Mean-reverting level for first differenced model is b0/ (1 – b1) = 0/1 = 0. Therefore, a first differenced random walk has a mean reverting level of 0. Note also the variance of Yt in each period is Var(? ) = ? 2. Because the variance and the mean of Yt are constant and finite in each period, Yt is a covariance stationary time series and we can model it using linear regression. Dickey-Fuller Test for a Unit Root If the lag coefficient in AR model is equal to 1, the time series has a unit root: It is a random walk and is not covariance stationary. By definition all random walks, with or without drift term have unit roots. If we believed that a time series Xt was a random walk with drift, it would be tempting to estimate the parameters of the AR model Xt = b0 + b1 * Xt -1 + ? using linear regression and conduct a t-test of the hypothesis that b1=1. Unfortunately, if b1=1, then xt is not covariance stationary and the t-value of the estimated coefficient b1 does not actually follow the t distribution, consequently t-test would be invalid. Dickey and Fuller developed a regression based unit root test based on a transformed version of the AR model Xt = b0 + b1 * Xt -1 + ? t. Subtracting xt-1 from both sides of the AR model produces xt- xt-1=b0+(b1-1)xt-1+ ? t or xt-xt-1 = b0 + g1xt-1+ ? t, E(? ) = 0 where gt = (b1-1). If b1 = 1, then g1 = 0 and thus a test of g1 = 0 is a test of b1 = 1. If there is a unit root in the AR model, then g1 will be 0 in a regression where the dependent variable is the first difference of the time series and the independent variable is the first lag of the time series. The null hypothesis of the Dickey-Fuller test is H0: g1 =0 that is, that the time series has a unit root and is non stationary and the alternative hypothesis is Ha: G1 ; 0, that the time series does not have a unit root and is stationary.

To conduct the test, one calculates a t- statistic in the conventional manner for g(hat)1 but instead of using conventional critical values for a t- test, one uses a revised set of values computed by Dickey and Fuller; the revised set of critical values are larger in absolute value than the conventional critical values. A number of software packages incorporate Dickey- Fuller tests. REGRESSIONS WITH MORE THAN ONE TIME SERIES Up to now, we have discussed time-series models only for one time series. In practice regression analysis with more than one time-series is more common.

If any time series in a linear regression contains a unit root, ordinary least square estimates of regression test statistics may be invalid. To determine whether we can use linear regression to model more than one time series, let us start with a single independent variable; that is, there are two time series, one corresponding to the dependent variable and one corresponding to the independent variable. We will then extend our discussion to multiple independent variables. We first use a unit root test, such as the Dickey-Fuller test, for each of the two time series to determine whether either of them has a unit root.

There are several possible scenarios related to the outcome of these test. One possible scenario is that we find neither of time series has a unit root. Then we can safely use linear regression to test the relations between the two time series. A second possible scenario is that we reject the hypothesis of a unit root for the independent variable but fail to reject the hypothesis of a root unit for the independent variable. In this case, the error term in the regression would not be covariance stationary.

Therefore, one or more of the following linear regression assumptions would be violated; 1) that the expected value of the error term is 0. 2 that the variance of the error term is constant for all observations and 3) that the error term is uncorrected across observations. Consequently, the estimated regressions coefficients and standard errors would be inconsistent. The regression coefficient might appear significant, but those results would be spurious. Thus we should not use linear regression to analyze the relation between the two time series in this scenario.

A third possible scenario is the reverse of the second scenario: We reject the hypothesis of a unit root for the dependent variable but fail to reject the hypothesis of a unit root for the independent variable. In the case also, like the second scenario, the error term in the regression would not be covariance stationary, and we cannot use linear regression to analyze the relation between the two time series. The next possibility is that both time series have a unit root. In this case, we need to establish where the two time series are co-integrated before we can rely on regression analysis.

Two time series are co-integrated if a long time financial or economic relationship exists between them such that they don’t diverge from each other without bound in the long run. For example, two time series are co-integrated if they share a common trend. In the fourth scenario, both time series have a unit root but are not co-integrated. In this scenario, as in the second and third scenario above, the error term in the linear regression will not be covariance stationary, some regressions assumptions will be violated, the regression coefficients and standard errors will not be consistent, and we cannot use them for the hypothesis tests.

Consequently, linear regression of one variable on the other would be meaningless. Finally, the fifth possible scenario is that both time series have unit root, but they are co-integrated in this case, the error term in the linear regression of one term series on the other will be covariance stationary. Accordingly, the regression coefficients and standard errors will be consistent, and we can use them for the hypothesis test. However we should be very cautious in interpreting the results of regression with co-integrated variables.

The co-integrated regression estimates long term relation between the two series but may not be the best model of the short term relation between the two series. Now let us look at how we can test for co-integration between two time series that each have a unit root as in the last two scenarios above. Engle and Granger suggest this test: if yt and xt are both time series with a unit root, we should do the following: 1) Estimate the regression yt = b0 + b1xt + ? t 2) Test whether the error term from the regression in Step 1 has a unit root coefficients of the regression, we can’t use standard critical values for the Dickey – Fuller test.

Because the residuals are based on the estimated coefficients of the regression, we cannot use the standard critical values for the Dickey- Fuller test. Instead, we must use the critical values computed by Engle and Granger, which take into account the effect of the uncertainty about the regression parameters on the distribution of the Dickey- Fuller test. 3) If the (Engle – Granger) Dickey- Fuller test fails to reject the null hypothesis that the error term has a unit root, then we conclude that the error term in the regression is not covariance stationary.

Therefore, the two time series are not co-integrated. In this case any regression relation between the two series is spurious. 4) If the (Engle- Granger) Dickey- Fuller test rejects the null hypothesis that the error term has a unit root, then we conclude that the error term in the regression is covariance stationary. Therefore, the two time series are co-integrated. The parameters and standard errors from linear regression will be consistent and will let us test hypotheses about the long – term relation between the two series. .

If we cannot reject the null hypothesis of a unit root in the error term of the regression, we cannot reject the null hypothesis of no co-integration. In this scenario, the error term in the multiple regressions will not be covariance stationary, so we cannot use multiple regression to analyze the relationship among the time series. Long-run Relationship For our analysis we use EUR/AMD and GBP/AMD exchange rates with respect to AMD from 1999 to 2013 with monthly bases. After estimating the normality of these time series we found out that the normality has rejected.

We got right skewness result and to correct them we used log values of exchange rates. Studying the trade between Armenia and Europe or Great Britain we found out that there is almost no trade relationship between them. Besides we assume, that Armenian Central Bank keeps floating rate of AMD. Taking into consideration these two factors the impact of AMD is negligible to have an essential influence on EUR/GBP rate. That is why we assume that the next models we will build show the relation between EUR and GBP. Graph 1 represents movement of EUR/AMD ; GBP/AMD since 1999 to 2013.

From it we can assume that these two currencies have strong long run relationship until Global Financial Crisis. As a result of shock in 2008 the previous relationship has been changed. However, it seems to be long term co-movement between the currencies. To accept or reject our conclusions we examine exchange rates until now including Global Financial Crisis, without crisis and after crisis. Co-integration of period from 1999 to 2013 To be considered as co-integrated the two variables should be non-stationary. So the first step in our model is to check the stationarity of variables by using Augmented Dickey-Fuller Unit Root Test.

EViews has three options to test unit-root: * Intercept only * Trend and Intercept * None From the first graph it is visible, that the sample average of EUR/AMD time series is greater than 0, which means that we have an intercept and it should be included in unit-root test. Although, series goes up and down, data is not evolving around the trend, we do not have increasing or decreasing pattern. Besides, we can separately try each of the components and include trend and intercept, if they are significant. In the case of EUR/AMD the appropriate decision is only intercept. Table 1. 1Table 1. We see it from the Table 1. 1, where Augmented Dickey-Fuller test shows p-value of 0. 1809 and as we have decided to use 5% significance level, Null Hypothesis cannot be rejected, which means there is a unit root. So, EUR/AMD exchange rate time-series is non-stationary. The same step should be applied with GBP/AMD exchange rates. We have estimated it and found out, that Augmented Dickey-Fuller test p-value is 0. 3724, which gives us the same results, as in the previous one: the variable has unit root. Since, the two variables are non-stationary, we can build the regression model yt = b0 + 1xt + ? t (Model 1. 1) and use et residuals from this model. So, the second step is to check stationarity for these residuals. Here we should use Eagle Granger 5% critical value instead of Augmented Dickey Fuller one, which is equal to -3. 34. Comparing this with Augmented Dickey-Fuller t-Statistic -1. 8273. Here minus signs should be ignored. So, comparing two values, we cannot reject Null Hypothesis, which means residuals have unit-root, they are non-stationary. This outcome is not desirable, which means the two variables are not co-integrated.

Co-integration till crisis period (1999-2008) Referring back to graph 1, we assume that in 1999-2013 time series two variables are not co-integrated because of shock related to financial crisis. That is why it will be rational first to exclude data from 2008 to 2013 and then again check co-integration between two variables. Here the same steps should be applied as in checking co-integration for time series from 1999 to 2013. For time series from 1999 to 2008, for EUR/AMD exchange rate, Augmented Dickey-Fuller test p-value is 0. 068. From the p-value it is clear that we cannot reject Null Hypothesis, which means it has a unit root. Having unit root means EUR/AMD exchange rate time-series is non-stationary. Now we should test stationarity of GBP/AMD exchange rates. The Augmented Dickey-Fuller test p-value is 0. 2556, which means the variable is non-stationary. Since, the two variables are non-stationary, we should build the regression model and using residuals check stationarity. Table 2. 1 In the table above Augmented Dickey Fuller t-test is 3. 57 and so greater than Eagle-Granger 5% significance level critical value 3. 34. That is why we can reject Null Hypothesis and accept Alternative Hypothesis, which means that residuals in regression model has no unit root. Consequently, they are stationary and we can conclude, that EUR/AMD and GBP/AMD time series are co-integrated: have long run relationship. As the variables such as EUR/AMD and GBP/AMD are co-integrated, we can run the error correction model (ECM) as below D(yt) = b2 + b3*D(xt) + b4*Ut-1 +V (Model 1. 2) * D(yt) and D(xt) are first differenced variables b2 is the intercept * b3 is the short run coefficient * V white noise error term * Ut-1 is the one period lag residual of ? t . Ut-1 is also known as equilibrium error term of one period lag. This Ut-1 is an error correction term that guides the variables of the system to restore back to equilibrium. In other words, it corrects this equilibrium. The sign before b4 or the sign of error correction term should be negative after estimation. The coefficient b4 tells as at what rate it corrects the previous period disequilibrium of the system.

When b4 is significant and contains negative sign, it validates that there exists a long run equilibrium relationship among variables. After estimating Model 1. 2, short run coefficient value b3 has been 1. 03 and was found significant. And b4, the coefficient of error term has been 5. 06 percent meaning that system corrects its previous dis-equilibrium at a speed of 5. 06% monthly. Moreover, the sign of b4 is negative and significant indicating that validity of long run equilibrium relationship between EUR and GBP.

Co-integration during crises period (2008-2013) Now is the time to check stationarity of variables in the period after crisis by the same way as we did above. From the ADF test it is clear that the two variables are non-stationary, after which we can construct ADF ; Eagle Granger test for residuals. However, because of ADF t-statistic is smaller, than Eagle Granger critical value, we could not reject that the residuals have unit-root. So, they are non-stationary and co-integration does not exist between the two currencies.

The intent of hypothesis testing is to let an person to take between two different hypotheses refering to the value of a population parametric quantity. Learning squad C has conducted a hypothesis trial environing the sum of clip spent on prep by males and females. and will turn to if there is a correlativity between the variables. Additionally. larning squad C will find if there is a positive or negative correlativity. and how strong that correlativity is between both variables. Overall. statistics can be really ambitious and we will portion some of the most enigmatic constructs experienced in Quantitative Analysis for Business therefore far. When carry oning a hypothesis trial. it is imperative that a void hypothesis is identified. The void hypothesis is the hypothesis that is assumed to be true unless there is sufficient plenty grounds to turn out that it is false ( McClave. 2011 ) . The void hypothesis for this experiment: Is the average sum of clip spent on prep by females equal to the sum of clip spent on prep by males? The ascertained significance degree is. 05. which means that there is a five per centum opportunity that we will reject the void hypothesis. even when it is true. The activity informations set provided were eight informations points for adult females and six informations points for work forces.

Because of the little sample size. we have conducted a t-test for this experiment. The grades of freedom equal 12. which we assign a critical value of 2. 179 from a t-table. If the trial statistic ( t-statistic ) is less than -2. 179. or greater than 2. 179 we will reject the void hypothesis in favour of the option. The t-statistic for the clip spent on prep by work forces and adult females is – . 4899. This figure does non fall into the rejection part. so we fail to reject the void hypothesis. In other words. the average sum of clip spent on prep by work forces and adult females are equal with a 95 per centum assurance degree. We have besides determined the correlativity coefficient. The correlativity coefficient ( denoted by the missive R ) is the step of the grade of additive relationship between two variables ( Webster. edu. n. d. ) . The correlativity coefficient can be any value between negative one and one. If the correlativity coefficient mark is negative. it means that as one variable decreases the other variable additions. The opposite is true for a positive correlativity coefficient. if the value of one variable increases the other variable lessenings. It is of import to observe that correlativity does non needfully intend causing ; we can non presume a right decision based on correlativity entirely.

For this experiment. the correlativity between work forces and adult females was 0. 346102651. When informations with values of R are close to zero. they show small to no straight-line relationship ( Taylor. 2015 ) . Even though the correlativity for this experiment was positive. it is non a strong correlativity. The closer the value of R to zero agencies that there is a greater fluctuation around the line of best tantrum ( Laerd Statistics. 2015 ) . Statisticss can be a really dashing topic. and there have been some constructs that have proven to be hard for each member of larning squad C. Many squad members struggle with the proper choice of expressions in Microsoft Excel. while others struggle to replace values into the many equations involved in statistics. There are besides legion symbols to retrieve. and decently place when calculating an equation.

From a conceptual point of view. chance is tough subject to hold on. The construct itself seems unintuitive. and is hard to understand an intangible construct that is based on guesswork and the best opportunity that an person has to see one event or another is random ( chance ) . When you take that construct and seek to do it touchable by seting it into an equation. things get rather confounding. Hypothesis proving can be good when an person is seeking make up one’s mind on what hypothesis to take refering to the value of a population parametric quantity. When make up one’s minding to carry on hypothesis proving it is of import to travel through the five stairss of the hypothesis proving process that include: making premises. saying the nothing and alternate hypothesis. finding the right trial statistic and trying distribution. calculating the trial consequences. and construing the determination ( Boston University. n. d. ) .

Interpreting the determination can include comparing the agencies for each of the groups can give a better apprehension of where each group falls as an norm. Interpreting the determination besides includes finding whether there is a correlativity between the two variables and finding whether the correlativity is positive or negative. For this experiment. the end was to find if there was a important difference for clip spent making prep by males and females. Hypothesis testing is used to find if there is adequate statistical grounds to back up a certain belief about a parametric quantity.

Mentions
Boston University. ( n. d. ) . The 5 stairss in hypothesis testing. Retrieved from Boston University. web site. Laerd Statistics. ( 2015 ) . Pearson-product minute correlativity. Retrieved from hypertext transfer protocol: //statistics. laerd. com/statistical-guides/pearson-correlation-coefficient-statistical-guide. php McClave. J. T. ( 2011 ) . Statistics for concern and economic sciences ( 11th ed. ) . Boston. MA: Pearson Education. Taylor. C. ( 2015 ) . How to cipher the correlativity coefficient. Retrieved from hypertext transfer protocol: //statistics. about. com/od/Descriptive-Statistics/a/How-To-Calculate-The-Correlation-Coefficient. htm Webster. edu. ( n. d. ) . Correlation. Retrieved from hypertext transfer protocol: //www2. Webster. edu/~woolflm/correlation/correlation. hypertext markup language

TOOTSIE ROLL POPS 1 How Many Licks Does it Take? Niklas Andersson Saginaw Valley State University of Michigan TOOTSIE ROLL POPS 2 Abstract Tootsie Roll Pops are known for the catch phrase, “How many licks does it take to get to the center of a Tootsie Roll Pop? ” The phrase was first introduced in an animated commercial in 1970. The whole point of the commercial is that no one will ever know how many licks it takes because you can’t resist the great temptation of biting into the candy shell. To test this hypothesis correctly, you must stop counting the moment that the center becomes exposed.

This study suggests that the flavor of the Tootsie Pop will be a participating factor. Are there any other factors at play? Will the world ever know how many licks it truly takes to get to the center of a Tootsie Roll Pop? TOOTSIE ROLL POPS 3 Introduction When the first Tootsie Roll Pop commercial debuted, many men, women, and children have asked, “How many licks does it take to get to the center of a Tootsie Roll Pop? ” A Tootsie Roll Pop is similar to a sucker, but the difference is the middle.

Inside, you will find a chewy chocolate center. There have been other experiments to determine the number of licks, but every other experiment seems to have different results. I have yet to find a credible study where every factor is at play. I will not be conducting this experiment with other participants, but with yours truly. My hypothesis for this experiment is that the number of licks is not different from each individual flavor. Method For this experiment I will be using the five popular flavors, chocolate, cherry, orange, grape, and raspberry.

The sole purpose of this research is to systematically determine how many licks it takes to get to the center. The lick will be defined as sticking out the tongue and running the Tootsie Roll Pop down the side of the tongue. With saliva playing a crucial role, I will retract my tongue every ten licks. The center is determined to have been reached when licking yields the texture of the Tootsie Roll. This eliminates any false positives as a result of bubbles in the candy, oddly textured regions, and seeing chocolate through the candy. I will be licking five of each flavor for a total of twenty-five Tootsie Roll Pops.

For every Tootsie Roll Pop I finish, I will drink a cup of water and rest for fifteen minutes before proceeding. TOOTSIE ROLL POPS 4 Results The numbers you see on the graph are the average amount of licks for each flavor. Over 15,000 licks later, the results are staggering. The chocolate Tootsie Roll Pop took over twice as many licks than any other flavor. Orange, grape, and raspberry were a surprisingly tight bundle with an average of fifty licks apart. It appears cherry takes the least amount of licks to reach the center.

The total average to reach the center of a Tootsie Roll Pop is 717 licks. FlavorsTrial 1Trial 2Trial 3Trial 4Trial 5Average Chocolate114011201055130011651156 Cherry520555560535510536 Orange600690584570620613 Grape665630715640660662 Raspberry615580610665630620 TOOTSIE ROLL POPS 5 Discussion I did not expect the chocolate flavor would differentiate from the other flavors. The four other flavors are not far apart from each other. This leads me to believe that any dye or ingredient used for the chocolate flavored Tootsie Pops create a stronger shell or coating.

Perhaps with an even larger sample size, the data will become more condensed or more stretched. I could continue this experiment, but I believe many other factors are at work here. Other possible areas of research include the effects of tongue size, saliva production, age, and gender. The data shown above is just the average for an eighteen year old male participant. What would happen if I included every possible factor to the experiment? TOOTSIE ROLL POPS 6 Works Cited Tootsie. (n. d. ). Retrieved from http://www. tootsie. com/

Knowledge, the key to progress, has proven to be a human being’s most powerful and significant weapon. We gain knowledge when we put our brain to work at the problems we need to solve in life. It doesn’t matter what we are trying to accomplish, whether it be creating a new technology or learning how to put together a puzzle, the matter of fact is that both request great examination and research to resolve and learn. Scientific research is a technique used to investigate phenomena, correct previous understanding, and acquire new knowledge.

Knowledge could lead us to a possible cure for cancer, an alternative for fossil fuels, and the creation of a revolutionary technology. Nevertheless, all these benefits are a reason why John M. Barry writes about scientific research with admiration, curiosity, and passion in which he blends a use of rhetorical strategies in order to give off an overall perspective of the necessity and mystery within scientific research. Foremost, John M.

Barry creates the sense of importance by describing unknown yet highly desired information that all scientists wish to obtain from scientific research, through the strategy of abstract diction where connotation is implemented at its best. The word “wilderness” is referred to in the passage various times and its dictionary meaning is not what’s really being discussed here. The wilderness John is referring to is the place where scientists must begin in every study in order to resolve or prove something. This is a place or in other words a moment where scientists have to take action and start guessing what is needed to do.

The answer is never in the face of the scientist, and before you find it you must know where to look and how to look. Obviously, using the word wilderness creates the idea that the knowledge scientists are looking for is hard and hidden and it is a successful form of emotional appeal to characterize scientific research as secreted yet vital. Following, through a successful form of a logical appeal the writer draws an analogy that further embodies the obscurity of finding knowledge and the importance of scientific research.

The author shoots to compare apples to apples when he puts side by side a shovel to the experiments performed during scientific research. When a successful comparison is given between two subjects it creates an understanding to the reader of what the overall intended purpose is for both. Both subjects compared here are meant to find an answer, or in the case of scientific research, knowledge. However, you must know what correct tool or experiment will find the answer. It’s not easy, so a scientist must really put a lot of thought into what must be done and how it must be done to find the information and key data they need.

In other words, the author explains how the success towards finding new knowledge is hidden deep within and can only be found if we understand step by step how we’re going to reach that data. Therefore, when the answer is not in the face of the questioner it takes hard effort to find but when found its benefits are unmeasured. Preceding, John M. Barry is sure not to exclude the use of an ethical appeal in which he presents a carefully and edited argument to create the strong message that scientific research is important to succeed.

In the beginning of the passage, it opens by explaining what certainty and uncertainty is. Then it clarifies what it takes to be a scientist, what a scientist must do, what happens if a scientist succeeds in their work, and why a scientist may fail. The main focus of these points is based on scientific research which proves its importance in his point of view and how it can make or break a scientist. John makes it clear that scientific research is essential and is not as easy as following step by step. It takes time, dedication, and most of all determination.

When someone is determined they will do whatever it takes, especially thinking out of the box, to accomplish their goal. Overall, the essay was presented in a logical and comprehensible way that allowed the reader to understand how essential yet possibly hard it can be to use scientific research. Closing, the writer is successful in making his opinion and perspective towards scientific research through the use of logos, pathos, and ethos. The overall analysis brought me to the conclusion that the John M. Barry portray scientific research as the chief ingredient to putting together answers and information. Yet still, doesn’t deny the complexity of scientific research and that’s its not straightforward as a scientist wishes it could be. Nevertheless, the benefits scientific research has brought along we see them everyday because of our overall advancement as a world. Didn’t I say knowledge is our most powerful weapon? Well observe, for it has destroyed the slow and premature society humans once used to live in and created a beautiful, diverse, and intelligent culture today.

GAS CHROMATOGRAPHY EXPERIMENT The purpose of this experiment is for the student: 1)to learn the general theoretical aspects of gas chromatography as a separation method, 2)to learn how to operate gas chromatographs specific to COD, 3)to become familiar with using the gas chromatograph (GC) to qualitatively identify components of mixtures, 4)to be introduced to and to interpret the quantitative data available via gas chromatography, 5)to gain insight into how the GC technique is used in the chemical industry both as a qualitative and quantitative tool.

As a means of accomplishing these objectives, we will attempt to identify the three major organic components of two different kinds of nail polish remover. PRELAB ASSIGNMENT Read Technique 22 in Pavia, 4th ed. Be sure that you understand the components of a gas chromatograph and the factors affecting separation. Pay particular attention to the definitions of retention time and resolution and how the GC can be used for qualitative analysis. ·Fill out a gold sheet for all compounds present in the purple nail polish remover as listed below. Write a procedural flow chart for the experiment. EXPERIMENTAL PROCEDURE Each student will be required to make at least one injection into the GC. Each student will also be a member of a group and will share information and chromatograms with other group members and between groups. All GC injections will be one micro-liter “sandwiched injections”. The procedure for preparing the syringe is described below. ·Place your sample in a small test-tube. ·Rinse the syringe three times with your sample. ·Draw approximately 1 micro-liter of air into the syringe. Draw 2 or 3 micro-liters of your sample into the syringe with the air. ·Turn the syringe so that the tip of the needle is pointing up and expel liquid from the syringe until only 1 micro-liter of liquid remains in the syringe. ·Pull the plunger back and draw in approximately 1 micro-liter of air. You now have a 1 micro-liter sample “sandwiched” between two air bubbles. Your group will be assigned either regular (purple) Revlon nail polish remover or acetone-free (blue) Revlon nail polish remover. The contents are listed below.

Your group must gather enough information to be able to identify the three major peaks in the gas chromatogram for your assigned nail polish remover. Acetone, ethyl acetate, and isopropyl alcohol in addition to the two nail polish removers will be available as samples for injection. You may use these chemicals to make mixtures that you will inject into the GC. You may not inject any of these neat liquids (pure chemical samples) because the column may become overloaded and the peaks will show a lot of trailing.

When analyzing the data and planning your mixtures, keep in mind that our GC’s have flame ionization detectors that do not detect non-flammable substances such as water. Someone in the group will need to inject the assigned nail polish remover into a GC and wait for the instrument to record the chromatogram. While the GC is cooling down, label the chromatogram with your name, the name of your sample, and the number of the GC which was used. Have the instructor initial the original chromatogram.

When the “ready” indicator light turns green on the GC, another member of the group should make an injection into the same GC in order to have the same experimental conditions for comparisons of results. The chemical make up of this second and subsequent injections should be determined after consultation within the group. You must get the approval of the instructor before making any mixtures for injection into the GC. Each person must submit at least one original initialed chromatogram attached to the cover sheet.

All other chromatograms will be obtained from your partners and by exchanging data within a group. The second type of nail polish will be analyzed using class data that will be provided by your instructor. The labels on the two nail polish removers list the contents of each in the following order: PURPLE Nail Polish|BLUE Nail Polish| acetone|ethyl acetate| water|isopropyl alcohol| ethyl acetate|water| isopropyl alcohol|jojoba oil| benzophenone-1|butyl alcohol| dyes|butyl acetate| |toluene| |dyes|