Consider the linear regression model: log(wage)=DB1+B2 MATH+B3 ARABIC + e where wage is the hourly wage and MATH is you scorein Math courses and ARABIC is your score in Arabic courses. You would like to test the hypothesis that MATH and ARABIC have thesame effect on log(wage) against the alternative that MATH has a greater weight on log(wage)a. Formally state the null and the alternative hypothesesb. The OLS results from a sample of 300 observations are reported belowlog(wage)= 2.55+ 0.85 MATH+2.75 ARABICR-square= 0.55)se (1.55) (0.035)(1.25)Test the hypothesis that you stated in (a) at 5% level given that the cov(b2,b3)=0.

The linear regression model: wage= The hourly wageMATH= Score is math coursesARABIC= Score in Arabic…

Answered: Consider the linear regression model:…

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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Where we observe multicollinearity in a multiple regression analysis, two of our independent variables, x-1 and x-4, are so highly correlated they’re almost indistinguishable with respect to their relationship with our dependent (y) variable. Why is that a problem?
(e) If a student were to study "extra hard" for exam 3 and increase his or her score on that exam by 14 points, what corresponding change would you expect on the final exam? (Assume that exams 1 and 2 remain "fixed" in their scores.) (Use 1 decimal place.)Test each coefficient in the regression equation to determine if it is zero or not zero. Use level of significance 5%. (Use 2 decimal places for t and 3 decimal places for the P-value.) t P-value β1 β2 β3 (f) Find a 90% confidence interval for each coefficient. (Use 2 decimal places.) lower limit upper limit β1 β2 β3 (g) This semester Susan has scores of 68, 72, and 75 on exams 1, 2, and 3, respectively. Make a prediction for Susan's score on the final exam and find a 90% confidence interval for your prediction (if your software supports prediction intervals). (Round all answers to nearest integer.) prediction lower limit upper limit
he following table shows the annual number of PhD graduates in a country in various fields. NaturalSciences Engineering SocialSciences Education 1990 70 10 60 30 1995 130 40 100 50 2000 330 130 280 140 2005 490 370 460 210 2010 590 550 830 520 2012 690 590 1,000 900 (a)With x = the number of social science doctorates and y = the number of education doctorates, use technology to obtain the regression equation. (Round coefficients to three significant digits.) y(x) = Use technology to obtain the coefficient of correlation r. (Round your answer to three decimal places.) r =
ruan Use wage1.txt data set for this question. Consider the following regression: logtwage)-Bo-Beduc-3.exper+u. Suppose we want to test whether the return of an additional year in school will pay more than spending an additional year in the labor market Which of the following is the correct set of hypotheses for this test? Ho: 1-2 HA: 1-20 31 Ho: 1-2 HA: 1-8 270 Ho: 1-220HA: 1-³ 20 Ho: 1-2 HA: 1-820 0.91
Which of the following is/are true for logistic regression model? I. The independent variable must be continuous. II. The dependent variable can only have 2 levels/categories. I only II only…
Consider the linear regression model Y; = Bo + B1 X¡ + U¡ for each i in $10,000) and Y; represents the home size (measured in square feet). We run an OLS regression and get: 1,..., n withn = 1,000. X; represents the annual income of individual i (measured Bin = 43.2, SE(§ „) = 10.2, Bon = 700, SE(Bom) = 7.4. Suppose that we want to test Ho : B1 O against H1 : ß1 # 0 at 1% significance level. Assuming that the sample size is large enough, which one of the following is true about the p-value of this test? 43.2 The p-value can be computed as P(-| ), where is the standard Normal CDF 10.2 а. b. None of the answers 43.2 The p-value can be computed as (- ), where O is the standard Normal CDF 10.2 С. 43.2 d. The p-value can be computed as 20(-- ), where O is the standard Normal CDF 10.2
The following estimated regression equation has been proposed to predict daily sales at a furniture store. ŷ = 12 − 5x1 + 8x2 + 17x3 where ŷ = estimated sales (in $1,000s) x1 = competitor's previous day's sales (in $1,000s) x2 = population within 1 mile (in 1,000s) x3 = 1 if any form of advertising was used; 0 otherwise (a) Fully interpret the meaning of the b3 coefficient (Give the answer in dollars.) Predict sales (in dollars) for the store with competitor's previous day's sale of $4,000, a population of 11,000 within 1 mile, and ... (b) no radio advertisements. $ (c) one radio advertisement. $ (d) eight radio advertisements. $
A popular board game manufacturer was interested in the relationship between the amount of time it takes to play a game and how well that game is rated among board game players. Information was collected on several board games and was used to obtain the regression equation ŷ = 27.273x + 18.182 where x represents the time it takes to play (in hours) and ŷ is the predicted rating of that game (in points). What is the predicted rating of a game that takes 1 hour to play? –0.63 points 9.091 points 45.455 points 1,654.562 points
Consider the simple linear regression model Wage = Bo + B1*Age + U. The error term U can capture the followings, with the exception of O A. possible measurement error in Wage. O B. the temperature in London Ontario tomorrow. O C. possible model misspecification, such as the nonlinear effect of Age on Wage. O D. other variables that affect the dependent variable, such as previous work experience.
A company studying the productivity of its employees on a new information system was interested in determingg if the age (X) of data entry opeertors influenced the number of completed entries made per hour (Y). The regression equation is y = 14.374 - 0.145x Suppose the acyual completed entries per hour for an operator who is 35 years old was 8. The residual is:
The following model is a simplified version of the multiple regression model used by Biddle and Hamermesh (1990) to study the tradeoff between time spent sleeping and working and to look at other factors affecting sleep: sleep = Bo + Bitotwrk + Bzeduc + Bzage + u, where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. (See also Computer Exercise C3 in Chapter 2.) (i) If adults trade off sleep for work, what is the sign of B,? (ii) What signs do you think B2 and Bz will have? (iii) Using the data in SLEEP75, the estimated equation is sleep = 3,638.25 – .148 totwrk – 11.13 educ + 2.20 age n = 706, R? = .113. If someone works five more hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ. (v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are…
A sports statistician was interested in the relationship between game attendance (in thousands) and the number of wins for baseball teams. Information was collected on several teams and was used to obtain the regression equation ŷ = 4.9x + 15.2, where x represents the attendance (in thousands) and ŷ is the predicted number of wins. What is the predicted number of wins for a team that has an attendance of 17,000? 83.3 wins 98.5 wins 258.4 wins 263.3 wins

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