In a regression model, if the variance of the dependent variable, y, conditional on anexplanatory variable, x, or Var(y/x), is not constant,a. the t statistics are invalid and the confidence intervals are valid for small samplesizes.b. the t statistics are valid and the confidence intervals are invalid for small samplesizes.c. the t statistics and the confidence intervals are valid no matter how large the samplesize.d. the t statistics and the confidence intervals are both invalid no matter how large thesample size.O a. aO b. bО с.C○ d. d

Option a: This option is incorrect because, Heteroscedasticity affects both t statistics and…

Answered: In a regression model, if the variance…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression you want to control for high school standing and so you run the following regression: GPA = 3.4 0.03 * HrsWrk - 0.7 * Frosh - 0.3 * Soph +0.1 * Junior (1.1) (0.013) (0.23) (0.14) (0.08) where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing. a) If you include a dummy variable for seniors, that would cause a Hint: type one word in each blank. For the rest of questions, type a number in one decimal place. b) The expected GPA of a Sophomore who works 10 hours per week is c) The expected GPA of a Senior who works 10 hours per week is d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a higher GPA than Sarah. e) Suppose you rewrite the regression as: problem. GPA = ₁HrsWrk + ß2Frosh + B2Soph +…
Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married respondents who are not female?
6) Suppose you have the following data on the price of orange and the quantity sold: Price per Pound (in Quantity Sold (in Dollars) Pounds) 0.50 0.75 1.00 1.25 1.50 10 7 699 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ + ε₁ Estimate the population regression coefficients. (Do not use Computer)
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q11-
The data for this question is given in the file 1.Q1.xlsx(see image) and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.
Define coefficients of the Linear Regression Model?
Problem 5. Based on a sample of 21 Monitoring the Future respondents (see table below), we present their racial/ethnic background and the number of school days missed in the past 4 weeks. White Black Hispanic 4 1 4 5 2 3 3 2 5 4 1 1 4 3 5 4 4 2 6 3 2 Complete the 5-step model for these data, and set alpha at 0.05. If alpha were set at 0.01, would your decision change? Explain.
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The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. Commute Time (minutes), x Well-Being Index Score, y 5 72 105 20 25 35 60 69.2 68.0 67.5 67.1 65.9 66.0 63.8 (a) Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the response variable. ŷ=x+ (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. First interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice. OA. For every unit increase in commute time, the index score falls by (Round to three decimal places as needed.) OB. For every unit increase in index score, the commute time falls by (Round to three decimal places as needed.) 1 D. For an index score of zero, the commute time is predicted to be (Round to three decimal places as needed.) on average. on average. OC. For a commute time…
Consider the following regression model and corresponding output for a dataset with n = 104 observations: y=ß₁+ß2x2+ß³¸*¸+4 3 4x4+u Variable β Std. Error t P>|t| X2 -0.012 0.006 -2.289 0.022 X3 0.596 0.014 41.139 0.000 X4 0.52 1.06 Constant 8.860 1.766 5.017 0.000 What is the marginal effect of x4 on y? (approximate at least to 3 decimal places)

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