What is multicollinearity?Discuss causes and consequences of multicollinearity for OLS estimation. Suggest possible remedial measures.b)Suppose you are estimating parameters of the following regression model: Ŷt= 9941 + 0.25 X2t+ 15125 X3t (6114) (0.121) (7349) R2= 0.87, RSS = 10310 (The figures in parentheses are the estimated standard errors. RSS are residual sum of squares.) (i) Comment on the explanatory power of the regression. (ii)Using t-tests show whether individual coefficients are significantly different from zero at 5% level of significance. (iii)Test whether the coefficient of X2issignificantly different from 1 at 5% level of significance .(iv)Carry out an appropriate test to check ifcoefficients are jointly significant.

What is multicollinearity?Discuss causes and consequences of multicollinearity for OLS estimation. Suggest possible remedial measures.b)Suppose you are estimating parameters of the following regression model: Ŷt= 9941 + 0.25 X2t+ 15125 X3t (6114) (0.121) (7349) R2= 0.87, RSS = 10310 (The figures in parentheses are the estimated standard errors. RSS are residual sum of squares.) (i) Comment on the explanatory power of the regression. (ii)Using t-tests show whether individual coefficients are significantly different from zero at 5% level of significance. (iii)Test whether the coefficient of X2issignificantly different from 1 at 5% level of significance .(iv)Carry out an appropriate test to check ifcoefficients are jointly significant.

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 3E

See similar textbooks

Similar questions

In regression analysis, a common metric used in assessing the quality of the model being used to fit the data is known as the R-squared coefficient. Explain the R-squared coefficient. What is the difference between the R-squared and adjusted R-squared coefficients?
Question 15 When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.
The estimated regression models having a different number of explanatory variables are compared on the basis of _____. Select one: a. Chi squared -statistic b. Adjusted R squared-statistic c. R squared-statistic d. None of the above
When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.
q12- Which method is NOT one of the remedial measures for the linear regression with an autocorrelated error term? Select one: a. Generalized least-squares estimation b. FDL model c. correlogram d. ARDL model
Consider a data set with 15 observations and consider a multiple linear regression model with 7 in-dependent variables. Assume you have estimated the model and you find that SST = 1,325 and SSR = 794.
The Pilot Pen Company has decided to use 15 test markets to examine the sensitivity of demand for its new product to various prices, as shown in the following table. Advertising effort was identical in each market. Each market had approximately the same level of business activity and population. Complete the following worksheet and then estimate the demand function for Pilot's new pen using a linear regression model. Test Market Price Charged Quantity Sold (cents) (Thousands of Pens) ii xixi yiyi xixiyiyi xi2xi2 yi2yi2 1 50 20 1,000 2,500 400 2 50 21 1,050 2,500 441 3 55 19 1,045 3,025 361 4 55 19.5 1,072.5 3,025 380.25 5 60 20.5 1,230 3,600 420.25 6 60 19 1,140 3,600 361 7 65 15.5 1,007.5 4,225 240.25 8 65 15 975 4,225 225 9 70 14.5 1,015 4,900 210.25 10 70 15.5 1,085 4,900 240.25 11 80 13 1,040 6,400 169 12 80 14 1,120 6,400 196 13 90 11.5 1,035 8,100 132.25 14 90 11 990 8,100 121 15 40 17 680 1,600 289 Total 980 246 ?…
Investigate what factors determine the number of times a person logs into Facebook per week. It is argued that these four factors are important: number of friends, age in years, whether the person is employed, and whether the student has a Twitter account. The function is: FACEBOOK LOGIN=f(FRIENDS,AGE,EMPLOYED,TWITTER) With this functions and variables in mind: Perform necessary tests and analysis to determine the validity of the regression attached in the below table and function provided The tests and analysis must be broken down into two sections namely: tests that are possible with the given regression’s information and tests that should be conducted but are not possible with the given information. For the tests that are possible please conduct them at a 5% significance level and for those that are not possible only mention their names without any further details
XYZ company is interested in quantifying the impact of consumer promotions on the sales of its packaged food product. XYZ has historical data on the following variables for 38 weeks: • Sales: Weekly sales volume in thousands of units.• Prom: Weekly spending on consumer promotions in thousands of Dollars" "A regression analysis was applied to XYZ historical dataset. The dependent variable is weekly Sales and the independent variables are weekly Prom and weekly Lagged Prom (i.e., last week Prom). This is a summary of the regression output:Sales = 0.80 + 1.20*Prom - 0.40*Lag(Prom) • R-squared=0.85• F-Statistic=23.83• p-value=0.001 (for the overall regression)•All regression coefficients are statistically significant at the 5% level." A. What will be the predicted sales volume ? B. What is the gross margin of this net volume impact due to $1000 spending per week on consumer promotions, if brand makes $2.20 gross margin per unit . C. What is the ROI of this promotion? D. What is predicted…
ABC, Inc., sells tea products to various customers. In recent years, profits have been declining. The CFO of the company investigated the reasons for the profit decline and performed regression analysis for sales and costs. She determined that sales depend on product price, delivery speed, customer services, and marketing expenses. She also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales. List two advantages and two limitations of regression analysis.
The following estimated regression equation relating sales to inventory investment and advertising expenditures was given. ŷ = 21 + 13x1 + 9x2 The data used to develop the model came from a survey of 10 stores; for those data, SST = 19,000 and SSR = 14,630. (a)For the estimated regression equation given, compute R2. R2 = ?? (b)Compute Ra2. (Round your answer to two decimal places.) Ra2 = ?? (c)Does the model appear to explain a large amount of variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the nearest integer.) The adjusted coefficient of determination shows that (??) % of the variability has been explained by the two independent variables; thus, we conclude that the model does explain a large amount of variability.
A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: a. Are the estimates of a, b, c, and d statistically significant at the 1 percent significance level? b. How much of the total variation is explained by this regression equation? c. Is the overall regression equation statistically significant at the 1 percent level of significance? d. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?