Consider the following regression estimates (FN8)SourceSSdfMSModel71007499.52 35503749.8Residual174967111497 352046.502Total245974611499 492935.092incometfemale-0.70 0.48313.97 0.000hours_cons6.360.000What is the R-squared of the regression above?Please state the answer as a value between 0 and 1.Coef. Std. Err.-37.5719 53.5540318.79374 1.345669302.975 47.65295Number of obs500F(2, 497)100.85Prob > F=0.0000P>|t| [95% Conf. Interval]-142.792167.648316.1498421.43765209.349396.6011||||

Answered: Image /qna-images/answer/5ebcf304-c0aa-4f78-936c-3e72b9fb4699.jpg

Answered: Consider the following regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variableThe regression equation isSALEPRIC = 1470 + 0.814 LANDVAL + 0.820 IMPROVAL + 13.5 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S = 79190.48 R-Sq = 89.7% R-Sq(adj) = 87.8% Analysis of Variance Source DF SS MS Regression 3 8779676741 2926558914 Residual Error 16 1003491259 62718204 Total 19 9783168000 For the problem above, we want to carry out the significance test about the coefficient of LANDVAL, what is the t-value for this test, and is it significant? 46.66, significant 2.05, significant 1.59, not significant 0.26, not significant
Table 8: Regression of Weight Loss on Hours of Exercise and Caloric Intalk Variable se b 95% CI Hours of Exercise 0.27 0.11 0.05, 0.49 Caloric Intake -0.002 .0008 -0.0035, -0.0004 Intercept 8.50 0.73 7.05, 9.95 Note: R? = .35, adj. R? = .35, F = 22.18, df = 2,117; n = 120 %3D swer the following questions about this regression analysis. Which predictors of weight loss are statistically significant, if either? What is the predicted weight loss for someone who consumes 2,000 calori hour per day on average? What is the literal interpretation for the slope of hours of aerobic exercise
Can you contruct a 95% confidence interval for the Y intercept for the regression equation. Then explain what do you conclude from this regression?
A regression model relating x, number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following regression output. Where ntotal = 40. ANOVA df SS MS F Significance F Regression 6,759.5 Residual Total 9,031.4 Coefficients Standard Error t Stat P-value Intercept 75.0 10.408 Number of 40.0 5.323 Salespersons a. Write the estimated regression equation (to whole number). ŷ = + b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) F-value p-value is - Select your answer - we - Select your answer - v Ho c. Compute the t statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) t Stat p-value is - Select your answer - we - Select your answer - v Ho: B, d. Predict the annual sales at the Memphis branch office. This branch employs 10 salespersons. $ thousand (to whole number) %24
If a regression model of the form y= B,+B,x,+... + B,x, is fit to 132 observations on each variable and yields an R´value of 0.87, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Source of Degrees of freedom Sums of Mean F statistic variation squares squares Regression 69 Error Total
A researcher's results are shown below using Femlab(labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 US states. What is the R2 for this regression? Source of variation df ss ms f Regression 1 5377.836 5377.836 5.228879 residual 48 49367.389 1028.487 total 49 54745.225
Consider the following Stata regression output (IDFB2) Source SS df MS Number of obs 935 %3D F(3, 931) 114.07 Model 1211.30177 3 403.767257 Prob > F 0.0000 %3D Residual 3295.51748 931 3.53976099 R-squared 0.2688 %3D Adj R-squared 0.2664 Total 4506.81925 934 4.82528828 Root MSE 1.8814 educ Coef. Std. Err. P>|t| [95% Conf. Interval] age .0074829 .0198281 urban 2546161 .1367932 IQ 0750274 .0040968 5.439236 .7981253 _cons What is the value of the t-statistic of the constant?
The following output comes from regression using the actual HW9 scores and the Final test scores from intro stats one semester. Sample size: 50R (correlation coefficient) = 0.3658R-sq = 0.1338Estimate of error standard deviation: 10.36256Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept 43.558118 11.1716 48 3.899 0.0003 Slope 0.360048 0.132225 48 2.723 0.009 Assume no assumptions are violated. Form a 87% Confidence interval for how much your final is supposed to increase for each problem done on homework 9.Use 5 decimal places
Beachcomber Ltd in a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model: SALES=a+BAGE+YMIL+SENG Where SALES= amount that a vehicle is sold for($000's), AGE = age of the vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained:
Consider the following regression estimates (FN4) Linear regression 29,471 318.65 = 0.0000 = 0.0108 = 801.34 Robust income Coef. Std. Err. t P>|t| [95% Conf. Interval] female -167.6086 9.389494 -186.0124 -149.2047 -17.85 0.000 60.38 0.000 _cons 946.3256 15.67369 915.6045 977.0468 where income is weekly income in NZ$, female is equal to 1 if the person if female and 0 if the person is male. What is the average income of all males in this regression? Please only state the number. Do not use the $ sign in your answer. Number of obs F(1, 29469) Prob > F R-squared Root MSE
table 7 autocorrelations of the residuals from estimating the regression ΔgPMt = 0.0006 − 0.33301 ΔgPMt −1 + εt 1Q:1992–4Q:2001 (40 Observations) regression Statistics R-squared Standard error Observations Durbin–watson intercept ΔgPMt −1 ΔgPMt −4 Coefficient −0.0001 −0.0608 0.8720 0.9155 0.0057 40 2.6464 Standard error 0.0009 0.0687 0.0678 t-Statistic −0.0610 −0.8850 12.8683 lag 1 2 3 4 5 autocorrelation −0.1106 −0.5981 −0.1525 0.8496 −0.1099 table 8 shows the output from a regression on changes in the gPM for home Depot, where we have changed the specification of the ar regression. table 8 Change in gross Profit Margin for home Depot 1Q:1992–4Q:2001 a. identify the change that was made to the regression model. b. Discuss the rationale for changing the regression specification
What is the residual for this (size = 240) observation? Round to 2 decimals

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