Consider the following regression estimates (FN8)SourcedfMSNunber of obs500F(2, 497)100.85Model71007499.535503749.8Prob > F0.0000%3DResidual174967111497 352046.502Total245974611499 492935.092incomeCoef.Std. Err.P>|t|(95% Conf. Interval]female-37.571953.55403-0.700.483-142.792167.6483hours18.793741.34566913.970.00016.1498421.43765„cons302.97547.652956.360.000209.349396.6011Where income is weekly income in NZS, female is equal to 1 is the person is female (and O otherwise) and hours is weekly working hours.Consider person A. She is a women, works 40 hour per week and earns 1300 NZS per week. What is her residual?Only stote the number (eg. do not odd NZS or $).

Solution: The estimated regression equation is Income ^= 302.975-37.5719 Female +18.79374 hours

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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3.3 You estimated a regression with the following output. Source | SS df MS Number of obs = 115 -------------+---------------------------------- F(1, 113) = 5454.39 Model | 186947380 1 186947380 Prob > F = 0.0000 Residual | 3873036.62 113 34274.6603 R-squared = 0.9797 -------------+---------------------------------- Adj R-squared = 0.9795 Total | 190820417 114 1673863.3 Root MSE = 185.13 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 28.58986 .3871141 73.85 0.000 27.82292 29.3568 _cons | 10.54686 26.92706 0.39 0.696 -42.80051 63.89423…
Can you please answer 7 & 8
1. You estimated a regression with the following output. Source | SS df MS Number of obs = 210 -------------+---------------------------------- F(1, 208) = 940.28 Model | 5529353.01 1 5529353.01 Prob > F = 0.0000 Residual | 1223155.7 208 5880.55624 R-squared = 0.8189 -------------+---------------------------------- Adj R-squared = 0.8180 Total | 6752508.71 209 32308.6541 Root MSE = 76.685 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 13.66711 .4457062 30.66 0.000 12.78843 14.54579 _cons | 103.7139 41.86814 2.48 0.014 21.17362 186.2542…
The following is a residual plot from a regression of a variable with the independent variable x. 0.6T 0.4+ 0.2+ 0.0- -0.2- -0.4+ -0.6+ -0.8- -1.0+ ㅇ + 1 + 2 + 3 + 4 + + 6 7 Residuals
4. You estimated the following regression. Which of the following is the estimated regression line? Source | SS df MS Number of obs = 309-------------+---------------------------------- F(1, 307) > 99999.00 Model | 3.4298e+09 1 3.4298e+09 Prob > F = 0.0000 Residual | 5008792.71 307 16315.2857 R-squared = 0.9985-------------+---------------------------------- Adj R-squared = 0.9985 Total | 3.4348e+09 308 11152012 Root MSE = 127.73------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- X | 39.83786 .0868877 458.50 0.000 39.66689 40.00883 _cons | 55.9075 9.036526 6.19 0.000 38.12613…
Proposed regression line: y ̂=5.4+(2.6)x n=4 Sum of squared deviations: ∑(??−?ℎ???)2∑(yi−yhati)2 = 25.2 Sums of squares: ∑(??−????????)2∑(yi−averagey)2 = 59 ∑?2?−?(????????)2∑xi2−n(averagex)2 = 5 Questions: (Enter answers rounded to one decimal place. If appropriate, include a zero before the decimal point) 1. What is ???Syx? 2. What is ??1Sb1? 3. What is SST? 4. What is SSE? 5. What is ?2R2?
Interpret the belavg coefficient. thank you
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
You estimated a regression with the following output. Source | SS df MS Number of obs = 325 -------------+---------------------------------- F(1, 323) = 42850.36 Model | 285905003 1 285905003 Prob > F = 0.0000 Residual | 2155111.65 323 6672.17228 R-squared = 0.9925 -------------+---------------------------------- Adj R-squared = 0.9925 Total | 288060115 324 889074.429 Root MSE = 81.683 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.83842 .0571895 207.00 0.000 11.72591 11.95093 _cons | 52.14457 5.949458 8.76 0.000 40.43999 63.84915…
In this regression, which predictor(s) is/are significant? Coefficients: (Intercept) time pub O'pubs' Neither 'time' Both Estimate Std. Error t value Pr (>|t|) 43082.4 3099.5 13.900 9.26e-09 982.9 121.8 452.1 2.174 0.0504 149.7 0.814 0.4317
You estimated a regression with the following output. Source | SS df MS Number of obs = 494 -------------+---------------------------------- F(1, 492) = 38566.69 Model | 803403712 1 803403712 Prob > F = 0.0000 Residual | 10249120.6 492 20831.546 R-squared = 0.9874 -------------+---------------------------------- Adj R-squared = 0.9874 Total | 813652832 493 1650411.42 Root MSE = 144.33 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 23.00296 .1171325 196.38 0.000 22.77281 23.2331 _cons | 34.71944 13.12788 2.64 0.008 8.925808 60.51307…
You estimated a regression with the following output. Source | SS df MS Number of obs = 423 -------------+---------------------------------- F(1, 421) = 267.80 Model | 8758968.84 1 8758968.84 Prob > F = 0.0000 Residual | 13769523.8 421 32706.7074 R-squared = 0.3888 -------------+---------------------------------- Adj R-squared = 0.3873 Total | 22528492.7 422 53385.0537 Root MSE = 180.85 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.150402 .3758334 16.36 0.000 5.411658 6.889145 _cons | -8.022201 24.02003 -0.33 0.739 -55.23632 39.19192…

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