Suppose you decide to estimate a student consumption function. After you run an OLS regression on your data set with 36 observations, you obtain the following. The estimated regression, along with standard errors and t-statistics, CO = - 47.143 + 0.9714 YD (se) (2.0307) (0.157) (t) ( ) (6.187) Where, CO : the average annual consumption expenditures of the students on items other than tuition and room. YD : the average annual disposable income (including gifts) of the students a) Interpret the slope and the intercept. b) Compute the test statistics ( t value and critical t ) for the intercept of the regression. Note that significance level is 0.10. c) Suppose that disposable income is increased by 1000 dollars on average. What would be the predicted consumption expenditures?

Suppose you decide to estimate a student consumption function. After you run an OLS regression on your data set with 36 observations, you obtain the following. The estimated regression, along with standard errors and t-statistics, CO = - 47.143 + 0.9714 YD (se) (2.0307) (0.157) (t) ( ) (6.187) Where, CO : the average annual consumption expenditures of the students on items other than tuition and room. YD : the average annual disposable income (including gifts) of the students a) Interpret the slope and the intercept. b) Compute the test statistics ( t value and critical t ) for the intercept of the regression. Note that significance level is 0.10. c) Suppose that disposable income is increased by 1000 dollars on average. What would be the predicted consumption expenditures?

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 1E

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1. Consider a linear regression model y = XB + € with E(e) = 0. The bias of the ridge estimator of 3 obtained by minimizing Q(B) = (y — Xß)¹ (y — Xß) + r(BTB), for some r > 0, is ——(X²X + r1)-¹8 1 (X¹X +rI)-¹3 r -r(XTX+rI) ¹8 r(X¹X+r1) ¹3
As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…
Suppose you decide to estimate a student consumption function. After you run an OLS regression on your data set with 36 observations, you obtain the following. The estimated regression, along with standard errors and t-statistics, CO = - 47.143 + 0.9714 YD (se) (2.0307) (0.157) (t) ( ) (6.187) Where, CO : the average annual consumption expenditures of the students on items other than tuition and room. YD : the average annual disposable income (including gifts) of the students Suppose that disposable income is increased by 1000 dollars on average. What would be the predicted consumption expenditures?
Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is Estimated College GPA = 0.84 + 0.531 (High School GPA). GPAs College GPA High School GPA 2.11 2.73 3.84 4.35 2.17 2.30 2.73 4.99 3.84 4.80 2.47 3.66 Copy Data Step 1 of 3: Compute the sum of squared errors (SSE) for the model. Round your answer to four decimal places.
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3. To explain what determine the price of air conditioners, B.T. Ratchford, obtained the following regression results based on a sample of 19 air conditioners: R²=0.84 y=-68.236 +0.023x, +19.729x3, +7.653x (0.005) (8.992) (3.082) se= Where y the price, in dollars x2= the BTU rating of air conditioner x3 the energy efficiency ratio x, the number of settings se standard errors a) Interpret the regression results. b) Do the results make economic sense? c) At 5% significance level, test the hypothesis that the BTU rating has no effect on the price of an air conditioner vs. that it has a positive effect. d) Would you accept the null hypothesis that the three explanatory variables explain a substantial variation in the prices of air conditioners? Show clearly all your calculations.
QUESTION 1 Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. This table is used for Questions 1-6. Dependent variable: In(Price) Regressor (1) (2) (3) (4) (5) 0.00042 (0.000038) Size In(Size) 0.57 (2.03) 0.69 0.68 0.69 (0.055) (0.054) (0.087) In(Size)² 0.0078 (0.14) Bedrooms 0.0036 (0.037) Рol 0.082 0.071 0.071 0.071 0.071 (0.032) (0.034) (0.034) (0.036) (0.035) 0.037 0.027 0.026 0.027 0.027 (0.030) View (0.029) (0.028) (0.026) (0.029) Pool x View 0.0022 (0.10) 0.12 (0.035) Condition 0.13 0.12 0.12 (0.035) 0.12 (0.045) (0.035) (0.036) 6.63 (0.53) Intercept 10.97 6.60 7.02 6.60 (0.069) (0.39) (7.50) (0.40) Summary Statistics SER 0.102 0.098 0.099 0.099 0.099 R? 0.72 0.74 0.73 0.73 0.73 Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0…
True or False For a linear regression model including only an intercept, the OLS estimator of that intercept is equal to the sample mean of the independent variable.