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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A regression of average monthly expenditure (AME, measured in euros) on average monthly income (AMI, measured in euros) using a random sample of college-educated full-time workers earnings €100 to €1.5 million yields the following: AME = 710.7 + 8.8 × AMI, R2 = 0.030, SER = 540.30 d. What does the regression predict will be the expenditure of a person with an income of €100? With an income of €200? e. Will the regression give reliable predictions for a person with an income of €2 million? Why or why not? ( f. Given what you know about the distribution of earnings, do you think it is plausible that the distribution of errors in the regression is normal? (Hint: Do you think that the distribution is symmetric or skewed? What is the smallest value of earnings, and is it consistent with a normal distribution?).
Source SS df MS Number of obs 935 F(2, 932) Prob F R-squared 66.31 %3D Model 19023273.1 9511636.56 0.0000 Residual 133692895 932 143447.312 0.1246 %3D Adj R-squared 0.1227 %3D Total 152716168 934 163507.675 Root MSE 378.74 %3D wage Coefficient Std. err. P>|t| [95% conf. interval] IQ 8.503461 .8240927 10.32 0.000 6.886169 10.12075 age 22.19023 3.991491 5.56 0.000 14.35688 30\.02358 _cons -637.3626 159.7452 -3.99 0.000 -950.8645 -323.8608 Consider the following regression estimates (ID233), where wage is monthly wage in US$, IQ is score on an IQ inte Interpret the age coefficient.
A set of n=15 pairs of x and y scores has sx=10, ssy=40, and sp=30. What is the slope for the regression for predicting y from x?
In a regression analysis if SSE = 64 and SSR = 364, then what is the coefficient of determination? (please keep 2 decimal places)
For half-life demonstration, plot ln(half-lives) in the x-axis vs ln(no. of “tails-up” coins) in the y-axis. Perform linear regression to obtain the value of the slope, the value of the y-intercept, and the correlation coefficient r. What is the significance of these values?
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
If you have a b of 0.56 in a regression equation, what does this mean? For every one-unit increase in x, you get an increase of 0.56 in y r = .31 On average, the variability of real scores around the regression line is 0.56 For every 1 standard deviation increase in x, you get an increase of 0.56 standard deviations in y
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.
R2

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