6. (a) Suppose that n = 100 i.i.d observations for (Y, X,) yield the following results where SER standsfor the standard error of the regression:Ý32.1+66.8X, SER=15.1, R2 = 0.81(15.1) (12.2)Another researcher is interested in the same regression, but he makes an error when he enters thedata into his regression program: He enters each observation twice, so he has 200 observations.Using these 200 observations, what results will be produced by his program? Write it in thefollowing format:=9+X, SER=(b) Suppose that a random sample of 200 twenty-year-old-men is selected from a population and thatthese mens height and weight are recorded. A regression of weight on height yields Weight-99.41 +3.94 x Height. Suppose further that instead of measuring weight and height in poundsand inches, these variables are measured in centimeters and kilograms. What are the regressionestimates from this new centimeter-kilogram regression? (1 inch= 2.54 cm, 1 pound=0.45 kg)=

The regression equation is given as follows. Y = 32.1 + 66.8X SER = 15.1R2 = 0.81n =100

6. (a) Suppose that n 100 i.i.d observations for (Y., X.) yield the following results where SER stands for the standard error of the regression: Ŷ 32.1+66.8X, SER=15.1, R20.81 (15.1) (12.2) Another researcher is interested in the same regression, but he makes an error when he enters the data into his regression program: He enters each observation twice, so he has 200 observations. Using these 200 observations, what results will be produced by his program? Write it in the following format: O 9+X, SER= ,R² (-) (-) (b) Suppose that a random sample of 200 twenty-year-old-men is selected from a population and that these mens height and weight are recorded. A regression of weight on height yields Weight -99.41 +3.94 x Height. Suppose further that instead of measuring weight and height in pounds. and inches, these variables are measured in centimeters and kilograms. What are the regression estimates from this new centimeter-kilogram regression? (1 inch 2.54 cm, 1 pound-0.45 kg)

6. (a) Suppose that n 100 i.i.d observations for (Y., X.) yield the following results where SER stands for the standard error of the regression: Ŷ 32.1+66.8X, SER=15.1, R20.81 (15.1) (12.2) Another researcher is interested in the same regression, but he makes an error when he enters the data into his regression program: He enters each observation twice, so he has 200 observations. Using these 200 observations, what results will be produced by his program? Write it in the following format: O 9+X, SER= ,R² (-) (-) (b) Suppose that a random sample of 200 twenty-year-old-men is selected from a population and that these mens height and weight are recorded. A regression of weight on height yields Weight -99.41 +3.94 x Height. Suppose further that instead of measuring weight and height in pounds. and inches, these variables are measured in centimeters and kilograms. What are the regression estimates from this new centimeter-kilogram regression? (1 inch 2.54 cm, 1 pound-0.45 kg)

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…
Suppose you are the manager of a firm that produces good X in Ghana In order to make informed decision, you engaged an economist to estimate the demand equation for your product. Using data from 30 supermarkets around the country for the month of April, 2021, the estimated linear regression result for your product is shown in the table below: Variable Parameter Estimates Standard error Constant -164.0 20.24 Price of good X (P) Price of good Y (P,) -3.50 1.55 2.50 0.28 Per capita Income () 0.45 0.52 R-squared Adjusted R-squared 0.8672 0.8132 F-statistic 15.6893 a) Suppose the average price of 3 units of good X is GH¢12, price of 2 units of goodY is GH¢60, the per capita income of Ghana is GH¢420. Write down the estimated demand equation for your firm's product and interpret 1. the parameter estimates. Determine the quantity of good X sold. Estimate the own price elasticity of demand and state the type of demand curve 1. 11 your firm has? What would be the effect of a price increase on…
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).
Help!
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
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%]
Suppose we estimated our multiple linear regression, all of the variables have p values below 0.05 (so they are statistically different from zero) but the intercept has p-value equal to p=0.343. What does it mean?
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
QUESTION 10 Answer questions 10 to 16 based on the regression outputs given in Table 1& 2. Table 1 DATA4-1: Data on single family homes in University City community of San Diego, in 1990. price - sale price in thousands of dollars (Range 199. 9 505) sqft - square feet of living area (Range 1065 - 3000) Table 2 Model 1: OLS, using observations 1-14 Dependent variable: price coefficient std. error t-ratio p-value 52. 3509 0.138750 37. 2855 0.0187329 0. 1857 8. 20e-06 *** const sqft 7. 407 Me dependent var Sun squared resid R-squared F(1, 12) Log-likelihood Schwarz criterion 317. 4929 18273. 57 0. 820522 54. 86051 -70. 08421 145. 4465 Hannan-Quinn S.D. dependent var S.E. of regression Adjusted R-squared P-value (F) Akaike criterion 88. 49816 39. 02304 0. 805565 8. 20e-06 144. 1684 144. 0501 There are observations included in this dataset. It is a. data. O 12; cross-sectional 13; time-series data 14; cross-sectional In this regression model, sale price of a single-family house is the. the…
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.