Applied Statistics in Business and Economics
5th Edition
ISBN: 9780077837303
Author: David Doane, Lori Seward Senior Instructor of Operations Management
Publisher: McGraw-Hill Education
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Textbook Question
Chapter 12.2, Problem 7SE
(a) Interpret the slope of the fitted regression HomePrice = 125,000 + 150 SquareFeet. (b) What is the prediction for HomePrice if SquareFeet = 2,000? (c) Would the intercept be meaningful if this regression applies to home sales in a certain subdivision?
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Students have asked these similar questions
In constructing the regression equation for predicting
electricity bills (BILLS) from number of people in an area
(AREA), it was calculated that the slope is 57.35 and it was
known that the mean BILL is PhP 1,327.21 and the mean
NUMBER is 6.31. What is the value of the intercept?
O None of the Choices
O PhP 768.08
O PhP 965.33
O PhP 611.10
O PhP 884.55
What are the interpretations of the Y intercept and the slopes in a multiple regression model?
A regression analysis of 117 homes for sale produced the following model, where price is in thousands of dollars and size is in square feet.
Price = 47.82 +0.067(Size)
a) Explain what the slope of the line says about housing prices and house size.
b) What price would you predict for a 3500-square-foot house in this market?
c) A real estate agent shows a potential buyer a 1100-square-foot house, saying that the asking price is $6500 less than what one would expect to pay for a house of this size. What is the
asking price, and what is the $6500 called?
a) Explain what the slope of the line says about housing prices and house size.
C
O A. For every additional square foot of area of a house, the price is predicted to increase by $67.
OB. For every $1000 increase in price of a house, the size is predicted to increase by 0.067 square feet.
O C. For every $1 increase in price of a house, the size is predicted to increase by 67 square feet.
O D. For every additional square foot of area of a…
Chapter 12 Solutions
Applied Statistics in Business and Economics
Ch. 12.1 - Prob. 1SECh. 12.1 - Prob. 2SECh. 12.1 - Prob. 3SECh. 12.1 - Prob. 4SECh. 12.1 - Prob. 5SECh. 12.1 - Prob. 6SECh. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.2 - Prob. 9SECh. 12.2 - (a) Interpret the slope of the fitted regression...
Ch. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.3 - Prob. 12SECh. 12.3 - Prob. 13SECh. 12.3 - The regression equation Credits = 15.4 .07 Work...Ch. 12.3 - Below are fitted regressions for Y = asking price...Ch. 12.3 - Refer back to the regression equation in exercise...Ch. 12.3 - Refer back to the regression equation in exercise...Ch. 12.4 - Instructions for exercises 12.18 and 12.19: (a)...Ch. 12.4 - Instructions for exercises 12.18 and 12.19: (a)...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.5 - Instructions for exercises 12.23 and 12.24: (a)...Ch. 12.5 - Prob. 24SECh. 12.5 - A regression was performed using data on 32 NFL...Ch. 12.5 - A regression was performed using data on 16...Ch. 12.6 - Prob. 27SECh. 12.6 - Prob. 28SECh. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.7 - Refer to the Weekly Earnings data set below. (a)...Ch. 12.7 - Prob. 33SECh. 12.8 - Prob. 34SECh. 12.8 - Prob. 35SECh. 12.9 - An estimated regression for a random sample of...Ch. 12.9 - An estimated regression for a random sample of...Ch. 12.9 - Prob. 38SECh. 12.9 - Prob. 39SECh. 12 - (a) How does correlation analysis differ from...Ch. 12 - (a) What is a simple regression model? (b) State...Ch. 12 - (a) Explain how you fit a regression to an Excel...Ch. 12 - (a) Explain the logic of the ordinary least...Ch. 12 - (a) Why cant we use the sum of the residuals to...Ch. 12 - Prob. 6CRCh. 12 - Prob. 7CRCh. 12 - Prob. 8CRCh. 12 - Prob. 9CRCh. 12 - Prob. 10CRCh. 12 - Prob. 11CRCh. 12 - Prob. 12CRCh. 12 - (a) What is heteroscedasticity? Identify its two...Ch. 12 - (a) What is autocorrelation? Identify two main...Ch. 12 - Prob. 15CRCh. 12 - Prob. 16CRCh. 12 - (a) What is a log transform? (b) What are its...Ch. 12 - Prob. 40CECh. 12 - Prob. 41CECh. 12 - Prob. 42CECh. 12 - Prob. 43CECh. 12 - Prob. 44CECh. 12 - Prob. 45CECh. 12 - Prob. 46CECh. 12 - Prob. 47CECh. 12 - Prob. 48CECh. 12 - Prob. 49CECh. 12 - Prob. 50CECh. 12 - Prob. 51CECh. 12 - Prob. 52CECh. 12 - Prob. 53CECh. 12 - Prob. 54CECh. 12 - Prob. 55CECh. 12 - Prob. 56CECh. 12 - Prob. 57CECh. 12 - Prob. 58CECh. 12 - Prob. 59CECh. 12 - In the following regression, X = weekly pay, Y =...Ch. 12 - Prob. 61CECh. 12 - In the following regression, X = total assets (...Ch. 12 - Prob. 63CECh. 12 - Below are percentages for annual sales growth and...Ch. 12 - Prob. 65CECh. 12 - Prob. 66CECh. 12 - Prob. 67CECh. 12 - Simple regression was employed to establish the...Ch. 12 - Prob. 69CECh. 12 - Prob. 70CECh. 12 - Prob. 71CECh. 12 - Below are revenue and profit (both in billions)...Ch. 12 - Below are fitted regressions based on used vehicle...Ch. 12 - Below are results of a regression of Y = average...
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- The regression equation that predicts the movie film gross amount earned (in millions of dollars) from the number of books (in millions) of the same name sold is: Predicted Film Gross = 37.50 + 1.57 Sales. What does the slope mean in the context of this problem?arrow_forwardIllustration 15.35. In trying to evaluate the effectiveness of its advertising campaign, a firm compiled the following data: Year: 1973 1974 1975 1976 1977 1978 1979 1980 48 Expenditure ('000): 12 Sale (lacs): 15 15 23 24 38 42 5.0 5.6 5.8 7.0 7.2 8.8 9.2 9.5arrow_forwardA grocery store is interested in how customers' income affects the amount of money they spend each week. A regression analysis is performed to test this analysis. The results of the analysis are that the intercept is $17.80 and the slope is 0.0012. The residual of one customer's weekly spending was $54.12. Is this household an example of over or under prediction and expenditures inverse or direct and is the relationship between income under-prediction, inverse under-prediction, direct over-prediction, direct over-prediction, inverse evidence of diet equity for this grocery store's customersarrow_forward
- The regression line for a data set giving sales S, in dollars, t years after 2016 is S = 210t + 2700. (a) Explain in practical terms the meaning of the slope. The slope of 210 dollars per year means that sales for the pertinent period are decreasing by $210 each year.The slope of 2700 dollars per year means that sales for the pertinent period are decreasing by $2700 each year. The slope of 2700 dollars means the initial sales are 2700 dollars in 2016.The slope of 2700 dollars per year means that sales for the pertinent period are increasing by $2700 each year.The slope of 210 dollars per year means that sales for the pertinent period are increasing by $210 each year. (b) What sales are indicated for 2020? $arrow_forwardAt the end of the semester the lecturer runs a regression using the student's final grade as the dependent variable and a male dummy (equals one if the student is male and equals zero otherwise) as the sole explanatory variable. If the estimated intercept is 65 and the estimated coefficient on the male dummy is 3 then what is the estimated mean mark for males in this course?arrow_forwardA researcher is creating a regression model The mean of the X variable is 38.5 and the mean of the Y variable is 18.2 If the slope is -3.5 what is the Y intercept? Round your answer to the nearest hundredtharrow_forward
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