Consider the following regression estimates (FN3)Linear regressionRobustincomeCoef.Std. Err.hours18.91906 1.481248_cons281.461834.36264where income is weekly income in NZ$ and hours is working hours per week.In this regression, what is the value of n?Number of obsF(1, 498)Prob > FR-squaredRoot MSE500163.130.00000.2880593.03[95% Conf. Interval]16.008821.82933213.9482348.9754t P>|t|12.77 0.0008.190.000|| || || || ||===

Solution: The independent or predictor variable is the regression is working hours per week. The…

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

See similar textbooks

Similar questions

Interpret the belavg coefficient. thank you
None
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…
You estimated a regression with the following output. Source | SS df MS Number of obs = 246 -------------+---------------------------------- F(1, 244) = 16642.70 Model | 187647307 1 187647307 Prob > F = 0.0000 Residual | 2751112.55 244 11275.0514 R-squared = 0.9856 -------------+---------------------------------- Adj R-squared = 0.9855 Total | 190398419 245 777136.405 Root MSE = 106.18 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 13.79687 .106947 129.01 0.000 13.58621 14.00753 _cons | 17.60822 9.208341 1.91 0.057 -.5297613 35.7462…
From the results shown above, write the regression equation.
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…
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
The slope, b represents * O predicted value of Y when X = 0. O the estimated average change in Y per unit change in X. O variation around the line of regression. O the predicted value of Y.
You estimated a regression with the following output. Source | SS df MS Number of obs = 208 -------------+---------------------------------- F(1, 206) > 99999.00 Model | 2.3001e+09 1 2.3001e+09 Prob > F = 0.0000 Residual | 4378568.43 206 21255.1865 R-squared = 0.9981 -------------+---------------------------------- Adj R-squared = 0.9981 Total | 2.3045e+09 207 11132679.3 Root MSE = 145.79 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 36.19925 .1100424 328.96 0.000 35.9823 36.41621 _cons | 12.65388 12.36197 1.02 0.307 -11.71832 37.02608…
2. You estimated a regression with the following output. Source | SS df MS Number of obs = 332 -------------+---------------------------------- F(1, 330) = 2.32 Model | 71599.1822 1 71599.1822 Prob > F = 0.1284 Residual | 10170207.7 330 30818.8111 R-squared = 0.0070 -------------+---------------------------------- Adj R-squared = 0.0040 Total | 10241806.9 331 30942.0147 Root MSE = 175.55 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | .7575183 .4969893 1.52 0.128 -.2201484 1.735185 _cons | 28.87757 42.80371 0.67 0.500 -55.32498 113.0801…
In bivariate regression, the value of Y when X equals 0 is: Group of answer choices Intercept (b0) Residual Slope (b1)

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS