5. Confounding variables and multiple regressions Suppose you are interested in studying the effect of square footage on the price of houses. You propose the following regression of housing prices on square footage: price = Bo+P sqft + u where price the price of the house sqft the square footage of the house residual Failing to account for the number of bathrooms in a house, which may influence housing prices and may be positively correlated with square footage, could lead to estimate of the effect . This issue of confounding variables would likely bias the estimate of

5. Confounding variables and multiple regressions Suppose you are interested in studying the effect of square footage on the price of houses. You propose the following regression of housing prices on square footage: price = Bo+P sqft + u where price the price of the house sqft the square footage of the house residual Failing to account for the number of bathrooms in a house, which may influence housing prices and may be positively correlated with square footage, could lead to estimate of the effect . This issue of confounding variables would likely bias the estimate of

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 given information on the use of public transportation in the data set within the image. Number of weekly riders y -variable Price per week (of using public transportation) x-variable Population of city x-variable Monthly income of riders x variable Average parking rates per month x-variable (a) Run a regression of the Number of weekly riders on Price per week, Population of city, Monthly income of riders, Average parking rates per month. Please copy and paste your regression results and do not submit your EXCEL worksheet. (b) If the average parking rates increase by $1, what will be the change in the number of weekly riders? Will this change be a statistically significant change? Please explain. (c) If the price per week (of using public transportation) increases by $1, what will be the change in the number of weekly riders? Will this change be…
What is the maximum weekly profit? What is the wholesale price per sleeping bag that should be charged to realize the maximum weekly profit?
Other Companies (Part 2) You directed your research department to do some research on the demand for Tesla sedans. They selected BMW i3 Sedans and Chevy Bolts as comparative offerings. Using regression analysis, the research department comes up the following estimate for yearly demand. Qx = -40,000 -1*Px +0.02*M +2*PB +2*PC Where: Px = $70,000, M = $150,000, PB = $65,000, PC = $40,000 a. Is the own price elasticity of demand for Tesla sedans at the point defined above elastic or inelastic? If Mr. Musk decides to raise his prices, what will happen to his revenue. b. PB and PC represent the price for i3 sedans and Bolt sedans respectively. Are these items compliments or substitutes when compared to Teslas? Give evidence to support your answer. c. Is the demand for Tesla sedans elastic or inelastic to price changes of BMW i3 and Bolt at the price points given in the problem? Interpret the result you find and explain what it means.
Show your work/thought process: a) Manually calculate the Sum of Squared Errors for this model. b) The coefficient on the “With Seagal” variable is 3.35. Explain what this means in simple language. c) Assume that Seagal will star in “The Expendables, Part 4”, and that pre-release audiences give the movie a Rotten Tomatoes rating of 52. Predict how much money the movie will earn.
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. Based on Addressing Heteroscedasticity list one test you would employ to test this potential issue?
7. The Stata data set "college_gpa" has data on students' college GPAS, high school GPAS, ACT scores, lectures skipped during the academic year, and other characteristics. We wish to examine the predictors of college GPAS. You must submit your do-file (commands). a) Regress college GPA on high school GPA and write the estimated regression line. b) Interpret the intercept of your regression in a sentence. Does this coefficient make sense?n Explain c) Interpret the coefficient on high school GPA in a sentence. Does this coefficient make sense? Explain. reg colGPA hsGPA Source df MS Number of obs 141 %3D F(1, 139) 33.80 Model 5.58478164 1 5.58478164 Prob > F e.0000 Residual 22.9668501 139 .165229138 R-squared Adj R-squared 0.1956 %3D 0.1898 %3D Total 28.5516318 140 .203940227 Root MSE .40648 colGPA Coef. Std. Err. t P>|t| [95% Conf. Interval] hsGPA .6242948 .1073816 5.81 0.000 .4119822 .8366073 _cons 890254 3669263 2.43 0.017 647755
Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the es3mated coefficients for each of the variables from part b? Be specific.
5. Choose the correct answer!