3) The following two sets of Excel output use are from the same data set as described above, but the first set of output is for the set of 35 CEO’s that earn the lowest total compensation and the second set of output is for a set of 40 CEO’s that earn the highest total compensation.a) What is heteroscedasticity?b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain. RegressionStatisticsMultiple RR SquareAdjusted R SquareStandard Error0.3480.1210.004479Observations35ANOVAdfSSMSSignificance FRegressionResidual49487032371761.0320.407306893142229771Total347841846門TTIITTCoefficients Standard Errort StatInterceptEMPLP-value0.002Lower 95% Upper 95%732.41857.4550.93.372982.4-2.03.9-0.520.605-10.05.9МKTVALEPSIN103.478.91.310.200-57.7264.6-48.625.2-1.920.064-100.23.0YEAR DUM5.2168.70.030.976-339.3349.6Multiple RR SquareAdjusted R SquareStandard ErrorObservations0.4500.2020.111152140ANOVAdfSSMS5129072FSignificance FRegressionResidualTotal4205162892.2180.08713580948642231281839101464930Coefficients Standard Errort StatP-valueLower 95%Upper 95%7950.1InterceptEMPLMKTVALEPSINYEAR DUM4047.01922.62.100.043144.011.4176.16.71.700.099-2.2-307.125.0238.00.740.464659.3-365.9153.7-2.380.700.023-677.9-54.01387.2356.5507.70.487-674.1

Please find the solution below. Thank you

The following two sets of Excel output use are from the same data set as described above, but the first set of output is for the set of 35 CEO’s that earn the lowest total compensation and the second set of output is for a set of 40 CEO’s that earn the highest total compensation. a) What is heteroscedasticity? b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain.

The following two sets of Excel output use are from the same data set as described above, but the first set of output is for the set of 35 CEO’s that earn the lowest total compensation and the second set of output is for a set of 40 CEO’s that earn the highest total compensation. a) What is heteroscedasticity? b) Why is heteroscedasticity a problem? c) Based on a comparison of the two sets of output, does it appear that there is heteroscedasticity in the data set? Explain.

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 30EQ

See similar textbooks

Similar questions

Further Verification of Newtons Second LawThis exercise represents a hypothetical implementation of the experiment suggested in the solution of part 6 of Example 3.7. A mass of 15 kilograms was subjected to varying accelerations, and the resulting force was measured. In the following table, acceleration is in meters per second per second, and force is in newton. Acceleration Force 8 120 11 165 14 210 17 255 20 300 a. Construct a table of differences and explain how it shows that these data are linear. b. Find a linear model for the data. c. Explain in practical terms what the slope of this linear model is. d. Express, using functional notation, the force resulting from an acceleration of 15 meters per second per second, and then calculate that value. e. Explain how this experiment provides further evidence for Newtons second law of motion.
XYZ Corporation Stock Prices The following table shows the average stock price, in dollars, of XYZ Corporation in the given month. Month Stock price January 2011 43.71 February 2011 44.22 March 2011 44.44 April 2011 45.17 May 2011 45.97 a. Find the equation of the regression line. Round the regression coefficients to three decimal places. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict the stock price to be in January 2012? January 2013?
Regression StatisticsMultiple R 0.890579188 Business Administration Program Final Assessment Semester Spring 2020Page 4R Square 0.793131289Adjusted R Square 0.7379663Standard Error 30.28395534Observations 20ANOVA df SS MS FRegression 4 52743.23074 13185.81 14.37743932Residual 15 13756.76926 917.1179509 Total 19 66500 Coefficients Standard Error t Stat P-valueIntercept 73.33291 62.25276 1.17799 0.25715X1 -0.13882 0.05353 -2.59326 0.02037X2 3.73984 0.95568 3.91328 0.00138X3 0.37644 0.16876 2.23061 0.04140X4 17.70752 15.57847 1.13667 0.27351 a. Write down the fitted multiple regression equation
High-Low Method, Scatterplot, Regression Weber Valley Regional Hospital has collected data on all of its activities for the past 16 months. Data for cardiac nursing care follow: YCost XHours of Nursing Care May Year 1 $59,600 1,400 June Year 1 57,150 1,350 July Year 1 61,110 1,460 August Year 1 65,800 1,600 September Year 1 69,500 1,700 October Year 1 64,250 1,550 November Year 1 52,000 1,200 December Year 1 66,000 1,600 January Year 2 83,000 1,800 February Year 2 66,550 1,330 March Year 2 79,500 1,700 April Year 2 76,000 1,600 May Year 2 68,500 1,400 June Year 2 73,150 1,550 July Year 2 73,175 1,505 August Year 2 66,150 1,290 Required: In the computations below, use intercepts rounded to the nearest whole number and use X variable coefficients rounded to…
Model Parameters (Points) Source Value Standard Error t Pr > |t| Intercept 0.437798 5.9182 0.073948 0.9428 Hours 0.829539 0.109474 7.57748 0.0001 Analysis of Variance (Points) Source Sum of Squares Df Mean Square F Pr > |t| Model 3249.72 1 3249.72 57.42 0.0001 Error 452.779 8 56.5974 Corrected Total 3702.5 9 1. Write down the estimated regression equation that could be used to estimate the total points earned in the course given the hours spent studying. A. Y^=0.83+0.44X B. Y^=5.92+0.11X C. Y^=0.44+0.83X 1a. At the 0.05 level of significance, does hours spent studying have a significant effect on the total points earned in this course? A. Yes, because the p-value for this test is 0.0001 that is smaller than 0.05. B. No, because the p-value for this test is 0.0001 that is smaller than 0.05. C. Yes, because the p-value for this test is 0.9429 that is greater than 0.05. D. No, because the p-value for this test is 0.9429 that is greater…
16. The Least Squares Method of linear regression minimizes the sum of the squared residuals. True False
Which is the best answer? Simple linear regression or Ordinary Least Squares is: a. An appropriate inferential statistic for basic, two-variable research questions. b. Used when the researcher has a clearly directional hypothesis between the independent and dependent variables. c. Used to predict scores on one variable based on the scores from a second variable (when both variables are normal/continuous scale) d. All the Above
A multiple linear regression analysis involves eight predictor variables. Fill in the blanks.a. The maximum number of subsets of the predictor variables considered by the forward selection method is_______b. The maximum number of subsets of the predictor variables considered by the backward elimination method is_______c. The maximum number of subsets of the predictor variables considered by an all-subsets regression is_______
1. In simple linear regression, the dependent variable is the: a. Input variable b. x variable c. y variable d. Coefficient of determination 2. In simple linear regression: a. The input variable x and the output variable y may be either continuous or discrete variables. B. The input variable x and the output variable y are both considered as continuous variables. c. Neither of the above. d. Both of the above. 3. In simple linear regression: a. The standard least squares fit finds the line with the smallest sum of vertical deviations from the data points. b. The standard least squares fit finds the line with the smallest sum of squared vertical deviations from the data points. c. Neither of the above. d. Both of the above.
Consider the one-variable regression model Yi = β0 + β1Xi + ui, and suppose it satisfies the least squares assumptions in Key Concept 4.3. Suppose Yi is measured with error, so the data are ?"i = Yi + wi, where wi is the measurement error, which is i.i.d. and independent of Yi. Consider the population regression ?"i = β0 + β1Xi + vi, where vi is the regression error, using the mismeasured dependent variable ?"i.a) Showthatvi =ui +wi.b) Show that the regression ?"i = β0 + β1Xi + vi satisfies the least squares assumptions in KeyConcept 4.3. (Assume that wi is independent of Yj and Xj for all values of i and j and has afinite fourth moment.)c) Are the OLS estimators consistent? part b in another question
3. In simple linear regression: a. The standard least squares fit finds the line with the smallest sum of vertical deviations from the data points. b. The standard least squares fit finds the line with the smallest sum of squared vertical deviations from the data points. c. Neither of the above. d. Both of the above.
With the vibrating wear of mild steel and oil viscosity in an article in Wear(1992)relevant data is Gives.Representative data, X: oil viscosity and y: wear volume (10^-4 mm^3 ) a) Using a simple linear regression model by creating a scatter diagram of the data Explain whether it is reasonable. b) Construct the simple linear regression model using the Least Squares Method note: could you please write the solutions legibly?