QUESTION 1Consider the following regression estimates (FN5)Linear regressionNumber of obsF(3, 1256)Prob > F1,26055.690.0000R-squared0.0765Root MSE4.4842RobustwageCoef. Std. Err.tP>|t|[95% Conf. Interval]exper.0544182.0974159.075917 .0109584.7327579 .30722956.93 0.0002.39 0.017union.13001841.3354981.320637.8588732married_cons.235370918044265.61 0.00021.13 0.0001.78244.1661323.8121293.458127·wage is hourly wage in US$.• Union is a dummy variable which is equal to 1 if the person is member of a labour union and 0 otherwise.• exper is years of work experience.• married is a dummy variable which is equal to 1 if the person is married and 0 otherwise.Interpret the exper coefficient.where|| || || || ||=

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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 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
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…
Interpret the estimated R2 Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
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
The following table contains the average number of employees per 100 hectares (x) and the production rate per 100 hectares ( y) at 15 different farms in a region. X y 7.5 404 16.6 702 10.9 507 6.5 385 10 464 12.1 495 8.6 400 10.2 503 7.5 310 5 268 6.2 280 12 596 17.1 711 14.1 551 6.1 242
Take a look at the following sample regressions (type of regression in top row), choose the type of model that best fits the data. Linear Logarithmic Intercept 6.7904 −5.6712 x 1.0607 NA In(x) NA 10.5447 se 2.4935 1.5231 R2 0.8233 0.9341 Adjusted R2 0.8013 0.9259 Linear Logrithmic Both equally Neither model explains a majority of the variation in y
Is the following statements correct or incorrect: Suppose that a random vector Y' = (Y1, Y2, Y3) has multi-normal distribution with mean vector u and variance-covariance matrix £ given by and Σ 흑 0 1 respectively. CH), Then the distribution of Z = Y is N 1
In the below residual plot, which assumption(s) of regression is/are violated? -4 Residuals -2 0 2 Residuals vs Fitted 00 8080 00 08 810 300 049 -8 -6 -4 -2 -2 0 2 4 6 Fitted values Equal variance across levels of X Normality Residuals are independent Linear relationship between X and Y
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.
Indicate whether 25 is sufficient or whether a sample of 50 would be necessary. You will want to calculate the probability of the sample mean being between $11,000 and $13,000 for both a sample size of 25 and 50. (Thank You!)
Consider the following regression estimates (FN6) Source SS df MS 526 64.11 Model 1927.87673 3 642.625576 0.0000 Residual 5232.53756 522 10.0240183 0.2692 0.2650 Total 7160.41429 525 13.6388844 3.1661 wage Coef. Std. Err. t P>|t| [95% Conf. Interval] educ .5953429 .0530251 11.23 0.000 .4911741 .6995118 exper .268287 .0368969 0.000 .1958023 .3407717 7.27 -5.61 0.000 exper2 -.0046123 .000822 -.006227 -.0029975 _cons -3.96489 .7521526 -5.27 0.000 -5.442508 -2.487272 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper * exper). At how many years is the marginal effect of increasing experience on predicted wage equal to zero? Only state the number in your answer (e.g. don't write US$ or $ or years). Number of obs = F(3, 522) = Prob > F R-squared Adj R-squared = Root MSE 11
3. Regression analysis breaks scores on the DV into... (explain and give equations)

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