Econ 306 Exam 1 MC

If the value of the estimated slope coefficient in the regression equation was found to be​ zero, then what would have been the value of Upper R squaredR2​? A. 0. B. 0.5. C. 1. D. Insufficient information provided.

A. 0.

Suppose you are interested in investigating the wage gender gap using data on earnings of men and women. Which of the following models best serves this​ purpose? A. Wage= B0 + B1Female + u, where Female (=1 if female) is an indicator variable and u the error term. B. Male= B0 + B1Female + u, where Male (=1 if male) is an indicator variable and u the error term. C. Female= B0 + B1Wage + u where Female (=1 if female) is an indicator variable and u the error term. D. Wage= B0 + u where u is the error term.

A. Wage= B0 + B1Female + u, where Female (=1 if female) is an indicator variable and u the error term.

To decide whether the slope coefficient indicates a​ "large" effect of X on Y​, you look at​ the: A. economic importance implied by the slope coefficient. B. regression R2. C. value of the intercept. D. size of the slope coefficient.

A. economic importance implied by the slope coefficient.

One of the primary advantages of using econometrics over typical results from economic theory is​ that: A. it potentially provides you with quantitative answers for a policy problem rather than simply suggesting the direction​ (positive/negative) of the response. B. it teaches you how to use statistical packages. C. you learn how to invert a 4 by 4 matrix. D. all of the above.

A. it potentially provides you with quantitative answers for a policy problem rather than simply suggesting the direction​ (positive/negative) of the response.

Analyzing the effect of minimum wage changes on teenage employment across the 48 continental U.S. states from 1980 to 2010 is an example of​ using: A. panel data. B. having a treatment group versus a control​ group, since only teenagers receive minimum wages. C. cross-sectional data. D. time series data.

A. panel data.

A large p​-value ​implies: A. that the observed value -Yact is consistent with the null hypothesis. B. a large -actYact. C. a large t​-statistic. D. rejection of the null hypothesis.

A. that the observed value -Yact is consistent with the null hypothesis.

Changing the units of measurement —that ​is, measuring test scores in​ 100s, will do all of the following except for changing​ the: A. numerical value of the intercept. B. interpretation of the effect that a change in X has on the change in Y. C. residuals. D. numerical value of the slope estimate.

B. interpretation of the effect that a change in X has on the change in Y.

Econometrics can be defined as follows with the exception​ of: A. a set of tools used for forecasting future values of economic variables. B. measuring the height of economists. C. the science of testing economic theory. D. fitting mathematical economic models to​ real-world data.

B. measuring the height of economists.

E(ui | Xi)= 0 says​ that: A. the sample mean of the Xs is much larger than the sample mean of the errors. B. the conditional distribution of the error given the explanatory variable has a zero mean. C. the sample regression function residuals are unrelated to the explanatory variable. D. dividing the error by the explanatory variable results in a zero​ (on average).

B. the conditional distribution of the error given the explanatory variable has a zero mean.

A type II error​ is: A. the error you make when choosing type II or type I. B. the error you make when not rejecting the null hypothesis when it is false. C. typically smaller than the type I error. D. cannot be calculated when the alternative hypothesis contains an​ "=".

B. the error you make when not rejecting the null hypothesis when it is false.

Using 143​ observations, assume that you had estimated a simple regression function and that your estimate for the slope was​ 0.04, with a standard error of 0.01. You want to test whether or not the estimate is statistically significant. Which of the following decisions is the only correct​ one? A. You decide that the coefficient is small and hence most likely is zero in the population. B. The response of Y given a change in X must be economically important since it is statistically significant. C. The slope is statistically significant since it is four standard errors away from zero. D. Since the slope is very​ small, so must be the regression R2.

C. The slope is statistically significant since it is four standard errors away from zero.

Suppose that the random variables X and Y are independent and you know their distributions. Which of the following explains why knowing the value of X tells you nothing about the value of Y​? A. The variance of X might be different from the variance of Y. B. The mean of X might be different from the mean of Y. C. X and Y might be independent. D. All of the above.

C. X and Y might be independent.

Considering a large​ sample, could the researcher make inferences about the population covariance from the sample covariance​ value? A. No, because the value of sample covariance changes from sample to sample. B. Yes, because the sample covariance is an unbiased estimator for the population covariance. C. Yes, because the sample covariance is a consistent estimator for the population covariance. D. No, because the sample covariance is not an efficient estimator for the population covariance.

C. Yes, because the sample covariance is a consistent estimator for the population covariance.

The correlation between X and Y​: A. cannot be negative since variances are always positive. B. is given by corr (X,Y)=cov (X,Y)var (X)var(Y). C. can be calculated by dividing the covariance between X and Y by the product of the two standard deviations. D. is the covariance squared.

C. can be calculated by dividing the covariance between X and Y by the product of the two standard deviations.

Analyzing the behavior of unemployment rates across U.S. states in March of 2010 is an example of​ using: A. time series data. B. panel data. C. cross-sectional data. D. experimental data.

C. cross-sectional data.

The standard error of the regression ​(SER​) is 655.3. What are the units of measurement for the SER​? A. Dollars per week. B. Dollars per year. C. Dollars. D. Unit-free.

C. dollars

In the simple linear regression​ model, the regression​ slope: A. represents the elasticity of Y on X. B. when multiplied with the explanatory variable will give you the predicted Y. C. indicates by how many units Y ​increases, given a​ one-unit increase in X. D. indicates by how many percent Y​ increases, given a one percent increase in X.

C. indicates by how many units Y ​increases, given a​ one-unit increase in X.

E(ui | Xi) = 0 says that: A. the sample mean of the Xs is much larger than the sample mean of the errors. B. the sample regression function residuals are unrelated to the explanatory variable. C. the conditional distribution of the error given the explanatory variable has a zero mean. D. dividing the error by the explanatory variable results in a zero​ (on average).

C. the conditional distribution of the error given the explanatory variable has a zero mean.

The regression R2 is a measure​ of: A. whether or not ESS ​> TSS. B. the square of the determinant of R. C. the goodness of fit of your regression line. D. whether or not X causes Y.

C. the goodness of fit of your regression line.

The regression R2 is a measure​ of: A. whether or not X causes Y. B. whether or not ESS ​> TSS. C. the goodness of fit of your regression line. D. the square of the determinant of R.

C. the goodness of fit of your regression line.

In a randomized controlled​ experiment: A. you control for the effect that random numbers are not truly randomly generated. B. you control for random answers. C. there is a control group and a treatment group. D. the control group receives treatment on even days only.

C. there is a control group and a treatment group.

Assume that Y is normally distributed N​ (muμ​, sigmaσ2​). Moving from the mean ​(muμ​) 1.96 standard deviations to the left and 1.96 standard deviations to the​ right, then the area under the normal p.d.f.​ is: A. 0.05. B. 0.33. C. 0.67. D. 0.95.

D. 0.95

Data on hours spent on training a group of ten different employees in a certain day. a. an observational cross minus sectional data set b. an ideal randomized controlled experiment c. an observational time series data set d. an observational panel data set.

a. an observational cross minus sectional data set

The regression R2 is 0.024. What are the units of measurement for the R2​? A. Unit-free. B. Dollars per week. C. Dollars per year. D. Dollars.

A. Unit-free.

Which of the following statements are true about the value of R2​? ​(Check all that apply). (R2 = 0.82) A. The value of R2 lies between 0 and 1. B. If the independent variable explains all the variations in the dependent​ variable, then the R2 will be −1. C. The value of R2 lies between −1 and +1. D. If the independent variable explains none of the variations in the dependent​ variable, R2 will be 0.

A. The value of R2 lies between 0 and 1. D. If the independent variable explains none of the variations in the dependent​ variable, R2 will be 0.

Let Y be a random variable. Suppose you are interested in estimating the population​ mean, ​E(Y). Which of the following statements about confidence intervals and hypotheses tests are​ true? ​(Check all that​ apply.) A. A confidence interval summarizes the set of hypotheses about the population​ mean, ​E(Y)​, you can and cannot reject at a given significance level. B. A confidence interval defines a rejection​ rule, at a specified significance​ level, for only one possible value of the population​ mean, ​E(Y)​, whereas a single hypothesis test is a rejection rule for all possible values of the population mean. C. A single hypothesis test about the population​ mean, ​E(Y)​, contains more information than a confidence interval. D. A confidence interval defines a rejection​ rule, at a specified significance​ level, for all possible values of the population​ mean, ​E(Y)​, whereas a single hypothesis test is a rejection rule for only one possible value of the population mean.

A. A confidence interval summarizes the set of hypotheses about the population​ mean, ​E(Y)​, you can and cannot reject at a given significance level. D. A confidence interval defines a rejection​ rule, at a specified significance​ level, for all possible values of the population​ mean, ​E(Y)​, whereas a single hypothesis test is a rejection rule for only one possible value of the population mean.

Suppose that you want to measure the causal effect of hours spent studying on the performance on a microeconomics exam for a class of 30 students. Which of the following could be an ideal randomized controlled experiment to study the desired causal​ effect? A. Allow fifteen​ students, chosen at​ random, an extra day to study for the microeconomics exam. Then measure the exam score differences between students who got the extra day to study and those that did not. B. Allow all students an extra day to study for the microeconomics exam. Then measure the exam score difference between the student with the highest score and lowest score respectively. C. Allow the fifteen students with the highest grades in the class an extra day to study for the microeconomics exam. Then measure the exam score differences between students who got the extra day to study and those that did not. D. All of the above could be ideal randomized controlled experiments.

A. Allow fifteen​ students, chosen at​ random, an extra day to study for the microeconomics exam. Then measure the exam score differences between students who got the extra day to study and those that did not.

What is the difference between an estimator and an ​estimate? A. An estimator is a function of a sample of data to be drawn randomly from a population whereas an estimate is the numerical value of the estimator when it is actually computed using data from a specific sample. B. An estimate is a function of a sample of data to be drawn randomly from a population whereas an estimator is the numerical value of the estimator when it is actually computed using data from a specific sample. C. Both an estimator and an estimate are functions of a sample of data to be drawn randomly from a population. D. Both an estimator and an estimate are numerical values computed using data from a specific sample.

A. An estimator is a function of a sample of data to be drawn randomly from a population whereas an estimate is the numerical value of the estimator when it is actually computed using data from a specific sample.

Suppose that Y1, ..., Yn are i.i.d. random variables with a N​(mu Subscript Upper YμY​, sigma Subscript Upper Y Superscript 2σ2Y​) distribution. How would the probability density of Upper Y overbarY change as the sample size n ​increases? ​Hint: Think about the law of large numbers. A. As the sample size​ increases, the variance of Upper Y overbarY decreases.​ So, the distribution of Upper Y overbarY becomes highly concentrated around mu Subscript Upper YμY. B. As the sample size​ increases, the variance of Upper Y overbarY decreases.​ So, the distribution of Upper Y overbarY becomes less concentrated around mu Subscript Upper YμY. C. As the sample size​ increases, the variance of Upper Y overbarY increases.​ So, the distribution of Upper Y overbarY becomes less concentrated around mu Subscript Upper YμY. D. As the sample size​ increases, the variance of Upper Y overbarY increases.​ So, the distribution of Upper Y overbarY becomes highly concentrated around mu Subscript Upper YμY.

A. As the sample size​ increases, the variance of Upper Y overbarY decreases.​ So, the distribution of Upper Y overbarY becomes highly concentrated around mu Subscript Upper YμY.

In the simple linear regression model Yi= B0 + B1Xi + ui A. B0 + B1Xi represents the population regression function. B. the intercept is typically small and unimportant. C. B0 + B1Xi represents the sample regression function. D. the absolute value of the slope is typically between 0 and 1

A. B0 + B1Xi represents the population regression function.

An estimator ModifyingAbove mu with caret Subscript Upper YμY of the population value mu Subscript Upper YμY is consistent​ if: A. ModifyingAbove mu with caret Subscript Upper Y Baseline ModifyingAbove right arrow With p mu Subscript Upper YμYp→μY. B. Y is normally distributed. C. Upper Y overbar ModifyingAbove right arrow 0 With pYp→0. D. its mean square error is the smallest possible.

A. ModifyingAbove mu with caret Subscript Upper Y Baseline ModifyingAbove right arrow With p mu Subscript Upper YμYp→μY. (sample mean hat, Y -> sample mean, y)

Consider a random variable Y. What is the difference between the sample average Upper Y overbarY and the population​ mean? A. The population mean is a true measure of the central tendency of the distribution of Y whereas the sample average Upper Y overbarY is an estimator of the population mean. B. Both the population mean and the sample average Upper Y overbarY are true measures of the central tendency of the distribution of Y. C. Both the population mean and the sample average Upper Y overbarY are estimators of the central tendency of the distribution of Y. D. The sample average Upper Y overbarY is a true measure of the central tendency of the distribution of Y whereas the population mean is an estimator of the sample average.

A. The population mean is a true measure of the central tendency of the distribution of Y whereas the sample average Upper Y overbarY is an estimator of the population mean.

Which of the following statements hold true about the standard error of​ regression? A. The standard error of the regression is an estimator of the standard deviation of the regression error. B. The standard error of the regression and the dependent variable are measured in the same unit. C. The standard error of the regression and the regressor have the same measure of unit. D. The standard error of the regression is an estimator of the standard error of the dependent variable.

A. The standard error of the regression is an estimator of the standard deviation of the regression error. B. The standard error of the regression and the dependent variable are measured in the same unit.

Wage= B0 + B1Education + u Suppose further that you estimate the unknown population linear regression model by OLS. What is the difference between the OLS predicted value Wagehat and E(Wage|Education)? A. Wagehat is the expected value of Wage for given values of Education, while E(Wage|Education) is the OLS predicted value of Wage for given values of Education. B. E(Wage|Education) is the expected value of Wage for given values of Education, while Wagehat is the OLS predicted value of Wage for given values of Education. C. E(Wage|Education) and Wagehat are equivalent representations of the true value of Wage for given values of Education. D. E(Wage|Education) is the true value of Wage for given values of Education, while Wagehat is the OLS predicted value of Wage for given values of Education.

B. E(Wage|Education) is the expected value of Wage for given values of Education, while Wagehat is the OLS predicted value of Wage for given values of Education.

An econometrics class has 80​ students, and the mean student weight is 145 lb. A random sample of four students is selected from the​ class, and their average weight is calculated. Will the average weight of the students in the sample equal​ 145lb? A. Yes. B. No. Using this​ example, which of the following best explains the sample average Upper Y overbarY​? A. Although each observation Upper Y Subscript iYi is​ random, the value of their​ average, Upper Y overbarY​, is not random. Upper Y overbarY is equal to the population mean. B. Because each observation Upper Y Subscript iYi is drawn at​ random, the value of their​ average, Upper Y overbarY​, is also random. The value of Upper Y overbarY differs from one sample to the next. C. The value of Upper Y overbarY is not​ random, but it differs from one sample to the next. D. The value of Upper Y overbarY is random.​ However, it is the same for all samples.

B. No. B. Because each observation Yi is drawn at​ random, the value of their​ average, overbarY​, is also random. The value of overbarY differs from one sample to the next.

Assume that you have collected a sample of observations from over 100 households and their consumption and income patterns. Using these​ observations, you estimate the following regression Ci= B0 + B1Yi + ui where C is consumption and Y is disposable income. The estimate of B1 will tell you: A. Income/ Predicted consumption B. Predicted consumption/ Income C. The amount you need to consume to survive D. Predicted consumption/ Income

B. Predicted consumption/ Income

A randomly selected member of this population reports being unemployed. The probability that this worker is a college graduate is 0.196​, and the probability that this worker is a​ non-college graduate is 0.804. ​Are educational achievement and employment status​ independent? A. Since Pr (X=0, Y=1)=Pr (X=0)​, educational achievement and employment status are independent. B. Since Pr X=0 | Y=1)≠Pr (X=0)​, educational achievement and employment status are not independent. C. Since Pr (X=0 | Y=1)=Pr (X=0)​, educational achievement and employment status are independent. D. Since Pr (X=0, Y=1)≠Pr (X=0)​, educational achievement and employment status are not independent.

B. Since Pr (X=0 | Y=1)≠Pr (X=0)​, educational achievement and employment status are not independent.

Which of the following statements hold true about the standard error of​ regression? (R2=2.36) A. The standard error of the regression is an estimator of the standard error of the dependent variable. B. The standard error of the regression is an estimator of the standard deviation of the regression error. C. The standard error of the regression and the regressor have the same measure of unit. D. The standard error of the regression and the dependent variable are measured in the same unit.

B. The standard error of the regression is an estimator of the standard deviation of the regression error. D. The standard error of the regression and the dependent variable are measured in the same unit.

If the sample correlation between two random variables is​ zero, then which of the following statements could be the possible reasons behind observing this​ value? ​(Check all that apply​.) A. There exists a negative linear relationship between the two variables. B. There is no evident relationship between the two variables. C. There may exist a​ non-linear relationship between the two variables. D. There exists a positive linear relationship between the two variables.

B. There is no evident relationship between the two variables. C. There may exist a​ non-linear relationship between the two variables.

What role does the central limit theorem play in statistical hypothesis testing statistical hypothesis testing​? A. The central limit theorem allows us to directly compute the true value of thetaθ. B. To construct a rejection rule for Upper H 0To construct a rejection rule for H0​, it is necessary to know the sampling distribution of theta overbarθ under the null hypothesis. If the sampling distribution is​ unknown, the central limit theorem says that it can be approximated by a normal distribution when the sample size n is sufficiently large. C. The central limit theorem plays no role in statistical hypothesis testing statistical hypothesis testing. D. To construct a rejection rule for Upper H 0To construct a rejection rule for H0​, it is necessary to know the sampling distribution of theta overbarθ under the null hypothesis. If the sampling distribution is​ unknown, the central limit theorem says that it can be approximated by a normal distribution when the sample size n is sufficiently small.

B. To construct a rejection rule for Upper H 0To construct a rejection rule for H0​, it is necessary to know the sampling distribution of theta overbarθ under the null hypothesis. If the sampling distribution is​ unknown, the central limit theorem says that it can be approximated by a normal distribution when the sample size n is sufficiently large.

Which of the following statements best describes what the central limit theorem​ states? A. Under general​ conditions, the mean of Y is the weighted average of the conditional expectation of Y given X​, weighted by the probability distribution of X. B. Under general​ conditions, when n is​ large, the distribution of Upper Y overbarY is well approximated by a normal distribution even if Upper Y Subscript iYi are not themselves normally distributed. C. Under general​ conditions, when n is​ large, Upper Y overbarY will be near mu Subscript Upper YμY with very high probability. D. Under general​ conditions, when n is​ large, the distribution of Upper Y overbarY is well approximated by a standard normal distribution even if Upper Y Subscript iYi are not themselves normally distributed.

B. Under general​ conditions, when n is​ large, the distribution of Upper Y overbarY is well approximated by a normal distribution even if Upper Y Subscript iYi are not themselves normally distributed.

Wage= B0 + B1Education + u Suppose further that you estimate the unknown population linear regression model by OLS. What is the difference between u and uhat? A. uhat represents the deviation of observations from the population regression​ line, while u is the difference between Wage and its predicted value Wagehat B. u represents the deviation of observations from the population regression​ line, while uhat is the difference between Wage and its predicted value Wagehat C. u represents the intercept of the population regression​ line, while uhat is the difference between Wage and its predicted value Wagehat. D. u represents the deviation of observations from the population regression​line, while uhat is the OLS estimator of Wage.

B. u represents the deviation of observations from the population regression​ line, while uhat is the difference between Wage and its predicted value Wagehat.

Yi = B0 + B1Xi + ui, E(ui) = 0 Which of the following are true about the unobservable ui​? ​(Check all that apply​) A. All students will necessarily have the same value of ui because they are part of the same population. B. ui represents factors other than time that influence the​ student's performance on the exam. C. Different students will have different values of ui because they have unobserved individual specific traits that affect exam performance. D. ui will be zero for all students because time spent studying is likely the only factor that affects exam performance.

B. ui represents factors other than time that influence the​ student's performance on the exam. C. Different students will have different values of ui because they have unobserved individual specific traits that affect exam performance.

Wage= B0 + B1Education + u Suppose further that you estimate the unknown population linear regression model by OLS. What is the difference between B1 and B1hat? A. β1hat is a true population​ parameter, the slope of the population regression​ line, while β1 is the OLS estimator of β1hat. B. β1 is a true population​ parameter, the slope of the population regression​ line, while β1hat is the OLS estimator of β1. C. ​Both, B1 and B1hat, are true parameters of the population regression line. D. Both, B1 and B1hat, are OLS estimators of true parameters of the population regression line.

B. β1 is a true population​ parameter, the slope of the population regression​ line, while β1hat is the OLS estimator of β1.

How can you relate your answers above to the law of large​ numbers? (n goes up, sample mean goes up) A. The sample size has no effect on the sample mean. B. The sample mean is equal to the population mean regardless what the sample size is. C. As the sample size​ increases, the sample mean approaches the population mean. D. As the sample size​ increases, the positive distance between the sample mean and the population mean increases.

C. As the sample size​ increases, the sample mean approaches the population mean.

Did the survey contain statistically significant evidence that the incumbent was ahead of the challenger at the time of the​ survey? A. For the test Ho: p≠0.5 versus H1: p>0.5, we cannot reject the null hypothesis at the​ 5% significance level. The p​-value is larger than 0.05. The test suggests that the survey did not contain statistically significant evidence that the incumbent was ahead of the challenger at the time of the survey. B. For the test Ho: p>0.5 versus H1: p>0.5, we can reject the null hypothesis at the​ 5% significance level. The p​-value is less than 0.05. The test suggests that the survey contained statistically significant evidence that the incumbent was ahead of the challenger at the time of the survey. C. For the test Ho: p>0.5 versus H1: p>0.5, we cannot reject the null hypothesis at the​ 5% significance level. The p​-value is larger than 0.05. The test suggests that the survey did not contain statistically significant evidence that the incumbent was ahead of the challenger at the time of the survey. D. For the test Ho: p≠0.5 versus H1: p>0.5, we can reject the null hypothesis at the​ 5% significance level. The p​-value is less than 0.05. The test suggests that the survey contained statistically significant evidence that the incumbent was ahead of the challenger at the time of the survey.

C. For the test Ho: p>0.5 versus H1: p>0.5, we cannot reject the null hypothesis at the​ 5% significance level. The p​-value is larger than 0.05. The test suggests that the survey did not contain statistically significant evidence that the incumbent was ahead of the challenger at the time of the survey.

Why do the ​p-values for Ho: p=0.5 versus H1: p≠0.5 and Ho: p=0.5 versus H1: p>0.5 differ? A. Ho: p=0.5 versus H1: p>0.5 is a​ two-sided test and the p​-value is the area in the tails of the standard normal distribution outside ± the calculated t​-statistic. B. Ho: p=0.5 versus H1: p≠0.5 is a​ one-sided test and the p-value is the area under the standard normal distribution to the right of the calculated t​-statistic. C. Ho: p=0.5 versus H1: p≠0.5 is a​ two-sided test and the p​-value is the area in the tails of the standard normal distribution outside ± the calculated t​-statistic. D. Ho: p=0.5 versus H1: p>0.5 is a​ one-sided test and the p-value is the area under the standard normal distribution to the left of the calculated t​-statistic.

C. Ho: p=0.5 versus H1: p≠0.5 is a​ two-sided test and the p​-value is the area in the tails of the standard normal distribution outside ± the calculated t​-statistic.

Which of the following statements best describes an unbiased​ estimator? A. Its value is a function of the sample size. B. Its value is always equal to the true parameter value. C. Its average​ value, over repeated sampling for the same sample​ size, is equal to the population value. D. Its value is always the same in repeated sampling for the same sample size.

C. Its average​ value, over repeated sampling for the same sample​ size, is equal to the population value.

What are the units of measurement for the t​-statistic? A. Points of the test score. B. Test score/ STR C. Standard deviations. D. Number of students per teacher.

C. Standard deviations.

Suppose that X denotes the amount of rainfall in your hometown during a given month and Y denotes the number of children born in Los Angeles during the same month. Which of the following statements best explains why X and Y are not​ independent? A. The expected value of rainfall in inches is equal to the expected number of children born. B. The variance for the amount of rainfall in inches is equal to the variance of the number of children born. C. The ratio of the amount of rainfall in inches to the number of children born is usually one. D. The amount of rainfall may tell you something about the season​ and, since births are​ seasonal, it may also tell you something about the number of children born.

D. The amount of rainfall may tell you something about the season​ and, since births are​ seasonal, it may also tell you something about the number of children born.

The OLS residuals, uiHAT, are the sample counterparts of the population: A. regression function intercept. B. regression function slope. C. regression​ function's predicted values. D. errors.

D. errors

You allow fifteen​ students, chosen at​ random, an extra day to study for the microeconomics​ exam, and then measure the score differences between those who got the extra day to study and those that did not. Which of the following could be impediments to implementing this experiment in​ practice? A. It could be considered unethical to allow some students more time to study. B. It could be costly to administer the same exam to two different groups of students in the same class on different days. C. It could be against school policy to administer the same exam to two different groups of students in the same class on different days. D. A and C only. E. All of the above could be impediments to implementing this experiment in practice.

E. All of the above could be impediments to implementing this experiment in practice.

Choose a random group of employees to receive ten hours per week in additional training for a period of four weeks.​ Then, estimate the difference in productivity between workers who received the additional training and those that did not. a. an observational cross minus sectional data set b. an ideal randomized controlled experiment c. an observational time series data set d. an observational panel data set.

b. an ideal randomized controlled experiment

Data on hours spent on training the same employee for seven consecutive days. a. an observational cross minus sectional data set b. an ideal randomized controlled experiment c. an observational time series data set d. an observational panel data set.

c. an observational time series data set

Data on hours spent training for a group of ten different employees for seven consecutive days. a. an observational cross minus sectional data set b. an ideal randomized controlled experiment c. an observational time series data set d. an observational panel data set.

d. an observational panel data set.