Suppose that n = 100 i.i.d. observations for (Y, X) yield the followingregression results:Ŷ 32.1 + 66.8X, SER = 15.1, R² = 0.81.=(15.1) (12.2)Another researcher is interested in the same regression, but he makes anerror when he enters the data into his regression program: He enters eachobservation twice, so he has 200 observations (with observation 1 enteredtwice, observation 2 entered twice, and so forth).a. Using these 200 observations, what results will be produced by hisregression program? (Hint: Write the "incorrect" values of the sam-ple means, variances, and covariances of Y and X as functions of the"correct" values. Use these to determine the regression statistics.)Ŷ -X, SER=6tc() ()b. Which (if any) of the internal validity conditions are violated?

Answered: Image /qna-images/answer/f1ddf218-db44-4b4e-b306-fe8c973fb57f.jpg

Answered: Suppose that n = 100 i.i.d.…

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 was run to determine if there is a relationship between hours of study per week (x) and the final exam scores (y). The results of the regression were: y=ax+b a=5.865 b=25.06 r²=0.81 r=0.9 Use this to predict the final exam score of a student who studies 4.5 hours per week, and please round your answer to a whole number.
Data from 147 colleges from 1995 to 2005 (Lee,2008) were tested to predict the endowments (in billions) to a college from the average SAT score of students attending the college. The resulting regression equation was Y = -20.46 + 4.06 (X). This regression indicates that: a. for every one-point increase in SAT scores, a college can expect 4.06 billion more in endowments. b. most colleges have very high endowments. c. for every one-point increase in SAT scores, a college can expect 20.46 billion fewer in endowments. d. for every one-dollar increase in endowments, the college can expect a half-point increase in SAT scores.
A researcher is interested in finding out the factors which determined the yearly spending on family outings last year (Y, measured in dollars). She compiles data on the number of members in a family (X1), the annual income of the family (X2), and the number of times the family went out on an outing in the last year (X3). She collects data from 196 families and estimates the following regression: Y=120.45+1.54X1+2.12X2+2.12X3. Suppose β1, β2, β3, denote the population slope coefficients of X1, X2, and X3, respectively. The researcher wants to check if neither X1 nor X2 have a significant effect on Y or at least one of them has a significant effect, keeping X3 constant. She calculates the value of the F-statistic for the test with the two restrictions (H0: β1=0, β2=0 vs. H1: β1≠0 and/or β2≠0) to be 3.00. The p-value for the test will be enter your response here?
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x,) and newspaper advertising (x,). The estimated regression equation was ý = 82.3 + 2.29x, + 1.90x2. The computer solution, based on a sample of eight weeks, provided SST = 25.1 and SSR = 23.415. (a) Compute and interpret R? and R 2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is 653 x . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R2 = 0.653 and R,2 = 0.595. Do you prefer the multiple regression results? Explain. Multiple regression analysis (is preferred since both R2 and R.2 show an increased v v…
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.982 b=31.084 r2=0.471969 r=-0.687 Use this to predict the number of situps a person who watches 13.5 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.328 b=37.715 r?=0.788544 r=-0.888 Use this to predict the number of situps a person who watches 0.5 hours of TV can do (to one decimal place)
The following estimated regression equation based on 10 observations was presented. ŷ= 29.1230+ 0.5806x + 0.4380x2 The values of SST and SSR are 6,725.125 and 6,223.375, respectively. (a) Find SSE. SSE= (b) Compute R2. (Round your answer to three decimal places.) R² = (c) Compute R. (Round your answer to three decimal places.) R2= (d) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The estimated regression equation did not provide a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation did not provide a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation provided a good fit…
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were y=ax+b a=-1.324 b=23.586 r2=0.887364 r=-0.942 Use this to predict the number of situps a person who watches 11.5 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of study per week (xx) and the test scores (yy).The results of the regression were:y=ax+b a=6.686 b=35.26 r2=0.6084 r=0.78 Use this to predict the final exam score of a student who studies 8.5 hours per week, and please round your answer to a whole number.
A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-0.629 b=39.045 r2=0.806404 r=-0.898 Use this to predict the number of situps a person who watches 6.5 hour(s) of TV can do, and please round your answer to a whole number.

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