The following question refers to this regression equation (standard errors for each of the estimated coefficients are inparenthesis). Q=8,400-8" P+5" A+ 4** Px +0.05**1, (1,732) (2.29) (1.36) (1.75) (0.15) Q = Quantity demanded P =Price 1,100Advertising expenditures, in thousands = 20 P = price of competitor's good = 600/= averagemonthly income 10,000 What is the advertising elasticity of demand? Round your answer to two decimal places. YourAnswer:The t-statistic is computed bydividing the regression coefficient by the standard error of the coefficient.dividing the regression coefficient by the standard error of the estimate.dividing the standard error of the coefficient by the regression coefficient.dividing the R2 by the F-statistic.none of the specified answers are correct.

The t-statistic is calculated by dividing the regression coefficient by the standard error of the…

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ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Consider the following estimated regression for 220 home sales from a community in 2019 (estimated standard errors in parentheses) Price = 119.2 + 0.485BDR + 23.4Bath + 0.156Hsize + 0.002Lsize + 0.09Age (0.011) (2.61) (8.94) (0.00048) (0.311) R2 = 0.72 where Price denote the selling house price (measured in $1000s), BDR denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote the lot size (in square feet), and Age denote the age of the house (in years).
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A multiple OLS regression of maize output (Y) on improved maize seed (X1) and fertiliser (X2) inputs (all variables in kilograms) produced the following results: Y = -342 + 12.1X1 + 46X2 se = (17.23) (0.912) (8.713) R2 = 0.861 a) Calculate the t statistics associated with the constant, improved maize seed and fertiliser coefficients
A website that rents movies online recorded the age and the number of movies rented during the past month for some of their customers. The data are shown below for a random sample of 25 of their customers.The regression line for the data, with number of movie rentals as the response variable, provides an intercept = 18.87, and slope = -0.228. The standard error of the slope SE(b1) = 0.0827. Margin of error ME for a 99% Confidence Interval for the slope of the Population regression line is: 0.1161 0.2322 0.4644 0.3483
Please provide the correct answer along with the calculation. Do not use ChatGPT, otherwise I will give a downvote.
You estimated a regression with the following output. Source | SS df MS Number of obs = 411 -------------+---------------------------------- F(1, 409) = 4098.54 Model | 22574040.7 1 22574040.7 Prob > F = 0.0000 Residual | 2252702.97 409 5507.83122 R-squared = 0.9093 -------------+---------------------------------- Adj R-squared = 0.9090 Total | 24826743.7 410 60553.0334 Root MSE = 74.215 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.727341 .1050822 64.02 0.000 6.520772 6.933909 _cons | -.7552724 9.26027 -0.08 0.935 -18.95894 17.44839…
You estimate the following regression: In(Earn) = 5.349 + 0.0159*Education (2.755) (0.0092) What is the 99% confidence interval for the effect that an increase in education by 3 years has on earnings? OI-0.24%, 0.12%] OI-0.0078%, 0.040%] OI-0.78%, 3.96%] OI-0.64%, 10.18%] OI-2.35%, 11.89%]
An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41
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)
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.
This exercise refers to the drunk driving panel data regression summarized below. Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths Dependent variable: traffic fatility rate (deaths per 10,000). Regressor Beer tax Drinking age 18 Drinking age 19 Drinking age 20 Drinking age Mandatory jail or community service? Average vehicle miles per driver Unemployment rate Real income per capita (logarithm) Years State Effects? Time effects? (1) 0.41* (0.056) 1982-88 no no (2) (3) (4) -0.62** -0.76*** -0.42 (0.39) (0.33) (0.38) 0.023 (0.078) -0.014 (0.084) -0.023 -0.075 (0.053) (0.064) 0.034 -0.109*** (0.058) (0.058) no yes yes no yes Clustered standard errors? yes yes F-Statistics and p-Values Testing Exclusion of Groups of Variables Time effects=0 (5) -0.76** (0.36) 0.041 0.083 (0.111) (0.115) 0.006 0.015 (0.005) (0.011) -0.068* (0.016) 1.66* (0.66) 1982-88 1982-88 1982-88 1982-88 yes yes yes yes yes yes (6) -0.46 (0.39) -0.004 (0.022) 0.043 (0.101) 0.007 (0.005) -0.064*…

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