Class time (Please Circle): 11:10am-12:25pm. or 12:45pm-2:00pm

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1 Name: UIN: Class time (Please Circle): 11:10am-12:25pm. or 12:45pm-2:00pm Instructions: 1. Please provide your name and UIN. 2. Circle the correct class time. 3. To get full credit on answers to this exam, be clear, rigorous, and thorough in your responses. 4. You cannot get full credit (full or partial) unless something is written. 5. Sign the Aggie Pledge: On my honor, as an Aggie, I have neither given nor received unauthorized aid on this academic work. Signature Date 1

2 (29pts) Fundamentals of Regression Analysis (2 pts) 1. What is the name of the statistical technique associated with estimating parameters of a multiple regression model? (2 pts) 2. If the SST = 1,000 and SSE = 100, the SSR =. (2 pts) 3. Calculate the R 2 statistic associated with Question 2. (6 pts) 4. In a particular regression analysis, the sample size is 128, the number of parameters to be estimated is 8, and SSE = 1,600. (1 pt) (a) Degrees of freedom =. (2 pts) (b) MSE =. (2 pts) (c) Residual variance =. (1 pt) (d) Standard error of the regression =. (2 pts) 5. True or False. The residual variance is assumed to be constant across all observations in regression analysis. The technical name of this assumption is heteroscedasticity. (2 pts) 6. True or False. 2 R is always between 0 and 1. (2 pts) 7. Often in practice, analysts may consider different models in regression analysis. The dependent variable is the same across the different models, although the number of explanatory variables and the number of observations may differ across respective specifications. List two model selection criteria that could be used to ascertain which model is best supported by the underlying data. (a) (b) 2

3 (1 pt) 8. The theorem guarantees the parameter estimates associated with any regression model have desirable statistical properties. (2 pts) 9. List two desirable statistical properties of parameter estimates associated with any regression model. (a) (b) (2 pts) 10. If x is the data matrix in conjunction with the explanatory variables and y represents the column vector of data for the dependent variables, provide the formula to find the OLS parameter estimates. (1 pt) 11. For any regression model linear in parameters, if the model includes a(n), the sum of the residuals is zero. (1 pt) 12. The zero conditional mean assumption of regression analysis means that no explanatory variable is correlated with the. (2 pts) 13. List two components of regression analysis: (a) (b) (1 pt) 14. Other things the same, simple models generally are preferable to complex models, especially in forecasting. This statement refers to what fundamental principle? (1 pt) 15. Imposing restrictions either on estimated parameters or on forecasts often improves model performance. This statement refers to what fundamental principle? 3

4 (16 pts) 16. Data Considerations (a) Calculate the mean and the median of the following sample of observations for a variable labeled INTR. (2 pt) (i) Mean (2 pts) (ii) Median 16, 26, 13, 17, 31, 10, 28, 23, 14, 27, 35, 12, 19 (b) List two measures of the spread or dispersion of the variable INTR. DO NOT calculate these measures. (1 pt) (i) (1 pt) (ii) (1 pt) (c) Calculate the range of the data for the variable INTR. (1 pt) (d) The diagram to the right reflects 10 observations of the price and quantity of bananas. The solid line reflects the best fitting line associated with these observations. What is the technical name of this diagram? Price of Bananas Quantity of Bananas (3 pts) (e) The correlation coefficient is a measure of the degree of linear association between two variables. The minimum value of the correlation coefficient is. The maximum value of the correlation coefficient is. If there is no association at all between two variables, then the correlation coefficient is. (2 pts) (f) True or False. The skewness coefficient associated with the variable INTR is This distribution of INTR is skewed to the left. (2 pts) (g) True or False. The kurtosis coefficient associated with the variable INTR is This distribution of INTR is less peaked than the standard normal. (1 pt) (h) Name the statistic associated with testing whether the variable INTR follows a normal distribution. 4

5 (10 pts) 17. Suppose we wish to consider the relationship between food away from home expenditures (FAFH) and disposable income (DPI). We express this relationship as: FAFH t = β 0 + β 1 DPI t + u t t = 1, 2,, 90 (1 pt) (a) What is the technical name of this relationship? (1 pt) (b) What is the technical name of FAFH t, in the context of regression analysis? (1 pts) (c) What is the technical name of DPI t, in the context of regression analysis? (3 pts) (d) Suppose that 90 ( DPI DPI )( FAFH FAFH ) = 1, 200 t= 1 t and that 90 ( DPI t DPI ) = 1, 600 t = 1 Calculate 1 ˆβ. Show all work. 2 t (2 pts) (e) Suppose that DPI = $50,000 and FAFH = $40,000. Calculate ˆβ. 0 Show all work. (2 pts) (f) Suppose that the SIC of this model (call it model A) is Now you estimate an alternative version of the model, namely, FAFH t = 1, lnDPI t. You find that the SIC for this model (call it model B) is (1 pt) (i). What does the acronym SIC stand for? (1 pt) (ii). Which version of the relationship between FAFH and DPI do you choose? That is, do you choose model A or model B? 5

6 (17 pts) 18. Consider the multiple regression model Y t = a 0 +a 1 x 1t + a 2 x 2t + a 3 x 3t +a 4 x 4t + v t. Our estimation model is given as: Y = x x x x 4. (6.73) (0.72) (0.47) (1.25) (0.31) Standard errors of the estimated coefficients are reported in parentheses. The number of the sample observations in this regression analysis is 65. (2 pts) (a) Suppose that x 4 is measured in dollars. The impact of a $20 increase in x 4 is equal to what kind of change in y? Show all work. (2 pts) (b) If x 2 = 10 and y = 50, calculate the percentage change in y due to a 1 percent change in x 2. Show all work. (3 pts) (c) Test H 0 :a 2 = 1. Use a 0.05 level of significance. Show all work. (3 pts) (d) True or False. X 1 is a statistically significant factor of Y. Explain fully. Use a 0.05 level of significance and assume a two-tailed test. Be sure to report the critical value of the test statistic. (3 pts) (e) Conduct a one-tailed test of the hypothesis that a3 > 0. Use a 0.05 level of significance. (4 pts) (f) Suppose we wish to test a 2 = 1.75 and, simultaneously, a 4 = (2pts) (i). What test statistic do we use? (2pts) (ii). What are the appropriate degrees-of-freedom associated with this test statistic? 6

7 (28 pts) 19. Mrs. Smyth s Inc. is a Chicago-based food company. The following regression equation was fit to determine the demand for Mrs. Smyth s Gourmet Frozen Fruit Pies: Q t = b 0 + b 1 P t + b 2 A t + b 3 PX t + u t, where Q t is the quantity of pies sold during the t th quarter; P t is the retail price in dollars of Mrs. Smyth s frozen pies; A t represents the dollars spent for advertising; and PX tt is the price, measured in dollars, for competing premiumquality frozen fruit pies. The summary of the output of the regression analysis conducted in Excel is given in the attached page. (2 pts) (a) How much variability in the quantity of Mrs. Smyth s Gourmet Frozen Fruit Pies is explained by the regression analysis? (4 pts) (b) From the information given, test H 0 : b 1 = b 2 = b 3 = 0 at the 0.05 level of significance. Please give the appropriate test statistic, degrees of freedom, and p- value. (3 pts) (c) The t-statistic associated with the estimated coefficient for P t is Derive this measure from the information given. 2 (3 pts) (d) The R of the regression analysis is given as Derive this measure from the information given. (6pts) (e) Which variable(s) P t, A t, or PX t are significantly significant drivers of the quantity of Mrs. Smyth s pies sold? Assume a level of significance of

8 (6pts) (f) The 95% confidence interval for b 2, the coefficient associated with A t, is given by [7.51, 12.75]. Derive this confidence interval from the information given. Show all work. (2 pts) (g) Predict the quantity of pies sold with P t = $2, A t = $1,000, and PX t = $3. Show all work. (2 pts) (h) List two additional potential explanatory factors associated with the demand for Mrs. Smyth s Gourmet Frozen Fruit Pies. Be specific. 8

9 Q t = b 0 + b 1 P t + b 2 A t + b 3 PX t + u t 9

10 Table B.2 Critical Values at the 95 Percent Confidence Level (α = 0.05) (continued) Degrees of Freedom in the Numerator inf inf

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Chapter 3 Multiple Regression Complete Example

Department of Quantitative Methods & Information Systems ECON 504 Chapter 3 Multiple Regression Complete Example Spring 2013 Dr. Mohammad Zainal Review Goals After completing this lecture, you should be