Basic Business Statistics
14th Edition
ISBN: 9780134684840
Author: BERENSON, Mark L., Levine, David M., Szabat, Kathryn A.
Publisher: Pearson,
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Chapter 14, Problem 81PS
To determine
Find the appropriate multiple regression model and test whether all three independent variables should be included in the model.
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In a study of housing demand, the county assessor is interested in developing a regression model to estimate the market value (i.e., selling price) of residential property within his jurisdiction. The assessor feels that the most important variable affecting selling price (measured in thousands of dollars) is the size of house (measured in hundreds of square feet). He randomly selected 15 houses and measured both the selling price and size, as shown in the following table.
OBSERVATIONi
SELLING PRICE (× $1,000)Y
SIZE (× 100 ft2 )X
1
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3
4
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9
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265.2
279.6
311.2
328.0
352.0
281.2
288.4
292.8
356.0
263.2
272.4
291.2
299.6
307.6
320.4
12.0
20.2
27.0
30.0
30.0
21.4
21.6
25.2
37.2
14.4
15.0
22.4
23.9
26.6
30.7
a. Plot the data.b. Determine the estimated regression line. Give an economic interpretation of the estimated slope (b) coefficient.c. Determine if size is a statistically significant variable in estimating selling price.d. Calculate the coefficient…
Identify a characteristic of the data that is ignored by the regression line.
A mail-order business selling personal computer supplies, software and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect the warehouse distribution costs.
Data collected over 24 random months contain the warehouse’s distribution cost (in thousands of Rands), the sales (in thousands of Rands) and the number of orders received. A multiple linear regression model was fitted to the data by using Stat1.2. Use the output to answer the questions that follow by typing only the letter of the correct option in the answer boxes.
Variablesy: Warehouse Distribution Costx1: Salesx2: Number of Orders
Model Fitting StatisticsR2 = 0.8504Adj R2: ?
Regression Coefficients Beta Parameter Standard b Parameter Standard Estimates…
Chapter 14 Solutions
Basic Business Statistics
Ch. 14 - For this problem, use the following multiple...Ch. 14 - For this problem, use the following multiple...Ch. 14 - A nonprofit analyst seeks to determine which...Ch. 14 - Profitability remains a challenge for banks and...Ch. 14 - The production of wine is a multibillion-dollar...Ch. 14 - Human resource managers face the business problem...Ch. 14 - Prob. 7PSCh. 14 - Prob. 8PSCh. 14 - The following ANOVA summary table is for a...Ch. 14 - The following ANOVA summary table is for a...
Ch. 14 - A financial analyst engaged in business valuation...Ch. 14 - In Problem 14.3 on page 541, you predicted...Ch. 14 - In Problem 14.5 on page 542, you used the...Ch. 14 - In Problem 14.4 on page 541, you used efficiency...Ch. 14 - In Problem 14.7 on page 542, you used the weekly...Ch. 14 - Prob. 16PSCh. 14 - Prob. 17PSCh. 14 - Prob. 18PSCh. 14 - In Problem 14.5 on page 542, you used the...Ch. 14 - Prob. 20PSCh. 14 - Prob. 21PSCh. 14 - Prob. 22PSCh. 14 - Prob. 23PSCh. 14 - Prob. 24PSCh. 14 - In Problem 14.3 on page 541, you predicted...Ch. 14 - In Problem on page 541, you used efficiency ratio...Ch. 14 - Prob. 27PSCh. 14 - In Problem 14.6 on page 542, you used full-time...Ch. 14 - Prob. 29PSCh. 14 - Prob. 30PSCh. 14 - The following is the ANOVA summary table for a...Ch. 14 - The following is the ANOVA summary table for a...Ch. 14 - In Problem 14.5 on page 542, you used alcohol...Ch. 14 - In Problem 14.4 on page 541, you used efficiency...Ch. 14 - Prob. 35PSCh. 14 - In Problem 14.6 on page 542, you used full-time...Ch. 14 - Prob. 37PSCh. 14 - Suppose X1 is a numerical variable and X2 is a...Ch. 14 - The chair of the accounting department plans to...Ch. 14 - A real estate association in a suburban community...Ch. 14 - In Problem 14.5 on page 542, you developed a...Ch. 14 - In mining engineering, holes are often drilled...Ch. 14 - The owner of a moving company typically has his...Ch. 14 - Prob. 44PSCh. 14 - Zagat’s publishes restaurant rating for various...Ch. 14 - In Problem 14.6 on page 542, you used full-time...Ch. 14 - In Problem 14.5 on page 542, the percentage of...Ch. 14 - Prob. 48PSCh. 14 - The director of a training program for a large...Ch. 14 - Prob. 50PSCh. 14 - Prob. 51PSCh. 14 - Prob. 52PSCh. 14 - Prob. 53PSCh. 14 - Prob. 54PSCh. 14 - Prob. 55PSCh. 14 - Prob. 56PSCh. 14 - Prob. 57PSCh. 14 - An automotive insurance company wants to predict...Ch. 14 - A marketing manager wants to predict customer with...Ch. 14 - A local supermarket manager wants to use two...Ch. 14 - Prob. 61PSCh. 14 - Prob. 62PSCh. 14 - Prob. 63PSCh. 14 - Prob. 64PSCh. 14 - Prob. 65PSCh. 14 - Prob. 66PSCh. 14 - Prob. 67PSCh. 14 - Prob. 68PSCh. 14 - Prob. 69PSCh. 14 - Prob. 70PSCh. 14 - Prob. 71PSCh. 14 - The owner of a moving company typically has his...Ch. 14 - Professional basketball has truly become a sport...Ch. 14 - A sample of 61 house recently listed for sale in...Ch. 14 - Measuring the height of a California redwood tree...Ch. 14 - A sample of 61 houses recently listed for sale in...Ch. 14 - Prob. 77PSCh. 14 - Referring to Problem 14.77, Suppose that an...Ch. 14 - Prob. 79PSCh. 14 - Prob. 80PSCh. 14 - Prob. 81PSCh. 14 - Prob. 82PSCh. 14 - Prob. 83PS
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- Explain why the interpretation of the regression coefficients is difficult when the predictor variables are highly intercorrelated.arrow_forwardIn a study of housing demand, the county assessor develops the following regression model to estimate the market value (i.e., selling price) of residential property within his jurisdiction. The assessor suspects that important variables affecting selling price (YY, measured in thousands of dollars) are the size of a house (X1X1, measured in hundreds of square feet), the total number of rooms (X2X2), age (X3X3), and whether or not the house has an attached garage (X4X4, No=0, Yes=1No=0, Yes=1). Y=α+β1X1+β2X2+β3X3+β4X4+εY=α+β1X1+β2X2+β3X3+β4X4+ε Now suppose that the estimate of the model produces following results: a=166.048a=166.048, b1=3.459b1=3.459, b2=8.015b2=8.015, b3=−0.319b3=−0.319, b4=1.186b4=1.186, sb1=1.079sb1=1.079, sb2=5.288sb2=5.288, sb3=0.789sb3=0.789, sb4=12.252sb4=12.252, R2=0.838R2=0.838, F-statistic=12.919F-statistic=12.919, and se=13.702se=13.702. Note that the sample consists of 15 randomly selected observations. According to the estimated model, holding all…arrow_forwardWhat is the relationship between diamond price and carat size? 307 diamonds were sampled and a straight-line relationship was hypothesized between y = diamond price (in dollars) and x = size of the diamond (in carats). The simple linear regression for the analysis is shown below: Least Squares Linear Regression of PRICE Interpret the standard deviation of the regression model. a) We expect most of the sampled diamond prices to fall within $1117.56 of their least squares predicted values. b) We can explain 89.25% of the variation in the sampled diamond prices around their mean using the size of the diamond in a linear model. c) For every 1-carat increase in the size of a diamond, we estimate that the price of the diamond will increase by $1117.56. d) We expect most of the sampled diamond prices to fall within $2235.12 of their least squares predicted values.arrow_forward
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