Suppose you are a manager for a carnival game at the local fair. You want to estimate how many prizes you will need to have in stock to give out, based on the number of tickets purchased before the fair begins. You examine the past 24 fair openings to make your regression model, and find the following information. Σ(x − x)² = 791.5 Σ e = 223.1 and ŷ = 294. 21 +0. 186x, where is the number of tickets purchased before the fair begins, and ŷ is the predicted demand for prizes during the first day of the fair. What is the standard error of the slope of the regression line? 3.184 0.024 8.1 1.654 0.054

Suppose you are a manager for a carnival game at the local fair. You want to estimate how many prizes you will need to have in stock to give out, based on the number of tickets purchased before the fair begins. You examine the past 24 fair openings to make your regression model, and find the following information. Σ(x − x)² = 791.5 Σ e = 223.1 and ŷ = 294. 21 +0. 186x, where is the number of tickets purchased before the fair begins, and ŷ is the predicted demand for prizes during the first day of the fair. What is the standard error of the slope of the regression line? 3.184 0.024 8.1 1.654 0.054

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

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 3E

See similar textbooks

Similar questions

Suppose the Sherwin-Williams Company has developed the following multiple regression model, with paint sales Y (x 1,000 gallons) as the dependent variable and promotional expenditures A (x $1,000) and selling price P (dollars per gallon) as the independent variables. Y=α+βaA+βpP+εY=α+βaA+βpP+ε Now suppose that the estimate of the model produces following results: α=344.585α=344.585, ba=0.102ba=0.102, bp=−11.192bp=−11.192, sba=0.173sba=0.173, sbp=4.487sbp=4.487, R2=0.813R2=0.813, and F-statistic=11.361F-statistic=11.361. Note that the sample consists of 10 observations. 1.) According to the estimated model, holding all else constant, a $1,000 increase in promotional expenditures decrease or increase sales by approximately 102,813 or 11,192 gallons. Similarly, a $1 increase in the selling price decrease or increase sales by approximately 813,11,192 or 102 gallons. 2.)Which of the independent variables (if any) appears to be statistically significant (at the 0.05…
(Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?
Suppose you have run four regression models: A, B, C, and D. You are going to make a decision on which one to use just based on the adjusted r² value. Here are the adjusted r² values for each model: A: 0.71 B: 0.57 C: 0.65 D: 0.76 Which regression model would you choose based on the adjusted r²? OD since it has the highest adjusted r² value B since it has the lowest adjusted r² OC since it has an adjusted r² between the adjusted r² of regressions B and D. Either B or C since they have the lowest adjusted r²
A realtor was investigating the price of real estate based on the size of the house in square feet x1 and if the house was within walking distance of an "A" rated public school. The indicator variable is defined as x = 1 if the house is within walking distance of an "A" rated public school and x = 0 if the house is NOT within walking distance of an "A" rated public school. If there was interaction in the regression problem, an appropriately fit regression model would have…? a) A different slope and different y-intercept for those within walking distance and those not. b) A different y-intercept for those that were within walking distance and those that were not; the slope would not change. c) A different slope, but not a different y-intercept for those within walking distance and those not. d) Cannot be determined
For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X) Sales (Y) 2001 10 44 2002 9 40 2003 11 42 2004 12 46 2005 11 48 2006 12 52 2007 13 54 2008 13 58 2009 14 56 2010 15 60 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. a) Specify the estimation model you will use (the econometric form).b) Re-write the specified model in (a) with values from the regression results and interpret the coefficients.
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.
You have data on all individuals in Sweden, including their family size (i.e. their number of children) and their earnings. You want to estimate the effect of family size on earnings, but you suspect your regression will be biased as you do not have information on preferences for how many children people want to have. a) You have information on gender of all children. You have heard that peoplewho have two children of the same gender are more likely to have a third child. Would you be able to use gender composition of the first two children as an instrument for family size? Discuss the assumptions needed for such a model (assume treatment effects are constant). Set up the assumptions mathematically and explain in words what they mean. b) How would you test if the assumptions in b) hold? Is it likely that they hold? (Assume gender composition of the first two children is the only instrument you have access to.)
Suppose that an economist has been able to gather data on the relationship between demand and price for a particular product. After analyzing scatterplots and using economic theory, the economist decides to estimate an equation of the form Q= aPb, where Q is quantity demanded and P is price. An appropriate regression analysis is then performed, and the estimated parameters turn out to be a = 1000 and b = - 1.3. Now consider two scenarios: (1) the price increases from $10 to $12.50; (2) the price increases from $20 to $25. a. Do you predict the percentage decrease in demand to be the same in scenario 1 as in scenario 2? Why or why not? b. What is the predicted percentage decrease in demand in scenario 1? What about scenario 2? Be as exact as possible.
Literacy rate reflects the educational facilities and quality of education available in a country, and mass communication plays a large part in the educational process. To relate the literacy rate of a country to various mass communication outlets, a demographer has proposed to relate literacy rate to the following variables: News = number of daily newspaper copies (per 1000 population) Radio = number of radios (per 1000 population) TV = number of TV sets (per 1000 population). The regression model to estimate the literacy rate (?̂ ) is below: ?̂= 0.5149 + 0.0005*News - 0.0003* TV + 0.002* Radio Answer the following questions: 4.1) Interpret the coefficient value of the TV variable in the model. 4.2) Predict the literacy rate for a country that has 200 daily newspaper copies (per 1000 in the population), 800 radios (per 1000 in the population), and 250 TV sets (per 1000 in the population). Show a detailed solution to support your answer.
Question Three For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X) Sales (Y) 2001 10 44 2002 9 40 2003 11 42 2004 12 46 2005 11 48 2006 12 52 2007 13 54 2008 13 58 2009 14 56 2010 15 60 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. Specify the estimation model you will use (the econometric form). Identify the independent and the dependent variables. Use an econometric package of your choice (EViews, SPSS, STATA etc) to estimate the model. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). Re-write the specified model in…
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?
2. One topic from this module is heteroskedasticity.A. Convince me that you know what heteroskedasticity is.B. How can you use a scatterplot of the residuals from a regression model to look for heteroskedasticity?C. Explain briefly how to conduct a Park test for heteroskedasticity.