Applied Statistics in Business and Economics
5th Edition
ISBN: 9781259329050
Author: DOANE
Publisher: MCG
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Textbook Question
Chapter 13, Problem 42CE
In a model of Ford’s quarterly revenue TotalRevenue = 0 + 1 CarSales + β2 TruckSales + 3 SUVSales + , the three predictors are measured in number of units sold (not dollars). (a) Interpret each slope. (b) Would the intercept be meaningful? Explain.
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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.
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If the independent variable in a line of best fit y = - 0.008x + 14 is credit score, and the dependent variable is the interest rate on a used-car loan, then the slope is interpreted as follows: “If credit score increases by 1 point,the interest rate will _______ (increase/decrease) by _________percent, on average.”
Chapter 13 Solutions
Applied Statistics in Business and Economics
Ch. 13.1 - Observations are taken on net revenue from sales...Ch. 13.1 - Observations are taken on sales of a certain...Ch. 13.1 - Prob. 3SECh. 13.1 - A regression model to predict Y, the...Ch. 13.2 - Refer to the ANOVA table below. (a) State the...Ch. 13.2 - Refer to the ANOVA table below. (a) State the...Ch. 13.2 - Prob. 7SECh. 13.2 - Refer to the ANOVA table below. (a) State the...Ch. 13.3 - Observations are taken on net revenue from sales...Ch. 13.3 - Observations are taken on sales of a certain...
Ch. 13.3 - Prob. 11SECh. 13.3 - A regression model to predict Y, the state...Ch. 13.4 - A regression of accountants starting salaries in a...Ch. 13.4 - An agribusiness performed a regression of wheat...Ch. 13.5 - Prob. 15SECh. 13.5 - A regression model to predict the price of...Ch. 13.5 - Prob. 17SECh. 13.5 - Prob. 18SECh. 13.6 - Prob. 19SECh. 13.6 - Prob. 20SECh. 13.7 - Prob. 21SECh. 13.7 - Using the Metals data, construct a correlation...Ch. 13.8 - Prob. 23SECh. 13.8 - Which violations of regression assumptions, if...Ch. 13 - (a) List two limitations of simple regression. (b)...Ch. 13 - (a) What does represent in the regression model?...Ch. 13 - Prob. 3CRCh. 13 - Prob. 4CRCh. 13 - Prob. 5CRCh. 13 - Prob. 6CRCh. 13 - Prob. 7CRCh. 13 - Prob. 8CRCh. 13 - Prob. 9CRCh. 13 - (a) State the formula for the standard error of...Ch. 13 - (a) What is a categorical predictor? (b) Why is a...Ch. 13 - Prob. 12CRCh. 13 - Prob. 13CRCh. 13 - (a) What is multicollinearity? (b) What are its...Ch. 13 - Prob. 15CRCh. 13 - (a) State the formula for a variance inflation...Ch. 13 - Prob. 17CRCh. 13 - Prob. 18CRCh. 13 - Prob. 19CRCh. 13 - Prob. 20CRCh. 13 - (a) Name two ways to detect autocorrelated...Ch. 13 - (a) What is a lurking variable? How might it be...Ch. 13 - Instructions for Data Sets: Choose one of the data...Ch. 13 - Note: Exercises marked are based on optional...Ch. 13 - Prob. 27CECh. 13 - Note: Exercises marked are based on optional...Ch. 13 - Note: Exercises marked are based on optional...Ch. 13 - Prob. 30CECh. 13 - Prob. 31CECh. 13 - Prob. 32CECh. 13 - Prob. 33CECh. 13 - Prob. 34CECh. 13 - Prob. 35CECh. 13 - Note: Exercises marked are based on optional...Ch. 13 - Note: Exercises marked are based on optional...Ch. 13 - Note: Exercises marked are based on optional...Ch. 13 - Prob. 39CECh. 13 - Prob. 40CECh. 13 - Prob. 41CECh. 13 - In a model of Fords quarterly revenue TotalRevenue...Ch. 13 - In a study of paint peel problems, a regression...Ch. 13 - A hospital emergency room analyzed n = 17,664...Ch. 13 - Prob. 45CECh. 13 - A researcher used stepwise regression to create...Ch. 13 - A sports enthusiast created an equation to predict...Ch. 13 - An expert witness in a case of alleged racial...Ch. 13 - Prob. 50CECh. 13 - Prob. 51CECh. 13 - Prob. 52CECh. 13 - Which statement is correct concerning one-factor...Ch. 13 - Prob. 2ERQCh. 13 - Prob. 3ERQCh. 13 - Prob. 4ERQCh. 13 - Prob. 5ERQCh. 13 - Prob. 6ERQCh. 13 - Prob. 7ERQCh. 13 - Prob. 8ERQCh. 13 - Prob. 9ERQCh. 13 - Prob. 10ERQCh. 13 - Prob. 11ERQCh. 13 - Prob. 12ERQCh. 13 - Prob. 13ERQCh. 13 - Prob. 14ERQCh. 13 - Prob. 15ERQ
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- Find the slope ofthe line in Figure l.arrow_forwardAn insurance company determines that a linear relationship exists between the cost of fire damage in major residential fıres and the distance from the house to the nearest fire station. A sample of 15 recent fires in a large suburb of a major city was selected. For each fire, the following variables were recorded: x= the distance between the fire and the nearest fire station (in miles) y= cost of damage (in dollars) The distances between the fire and the nearest fire station ranged between 0.7 miles and 6.1 miles. The correlation between cost and distance is 0.961. Test if the correlation is significant at a=.10. O No, the correlation is not significant because 0.961 does not exceed the critical value. O Yes, the correlation is significant because 0.961 exceeds the critical value. No, the correlation is not significant because 0.961 exceeds the critical value. O Yes, the correlation is significant because 0.961 does not exceed the critical value. O No, the correlation is not…arrow_forward(d) Interpret the meaning of the slope of the regression line.arrow_forward
- In a regression analysis, if the slope is -2.23and the y-intercept is 1.25, which of the following should be the least regression equation? A. y = -2.23x + 1.25 B. y = 1.25x - 2.23 C. y = 2.23x - 1.25 D. y = -1.25x + 2.23arrow_forwardA frequent flyer was interested in the relationship between dollars spent on flying and the distance flown. She sampled 20frequent flyers of a certain airline. She collected the number of miles flown in the previous year and the total amount of money the flyer spent. A regression line of distance flown on money spent was fit to the data, and the intercept and slope were calculated to be ?=24,000 b=10 . A person who spent $2000 is predicted to have flown: 44000.miles 34000miles 54000miles 24000milesarrow_forwardYou have to examine the relationship between the age and price for used instrument sold in the last year by a company. Here is the table of the data: a. Determine the degree of relationship between in instument’s age and the price. b. Identify the equation of linear regression. c. How much will a used instrument worth after 6 years? 12 years? 15 years? d. Explain the predicting capability of the equation.arrow_forward
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