Chapter 12, Problem 12.74CRP

The general manager of an engineering firm wants to know whether a technical artist's experience influences the quality of his or her work. A random sample of 24 artists is selected and their years of work experience and quality rating (as assessed by their supervisors) recorded. Work experience (EXPER) is measured in years and quality rating (RATING) takes a value of 1 through 7, with 7 = excellent and 1 = poor. The simple regression model RATING = ẞ1 + ẞ₂EXPER+ € is proposed. The least squares estimates of the model, and the standard errors of the estimates, are RATING= 3.204 +0.076EXPER (se) (0.709) (0.044) (a) Interpret the coefficient of EXPER. (b) Construct a 95% confidence interval for B2, the slope of the relationship between quality rating and experience. In what are you 95% confident? (c) Test the null hypothesis that ẞ2 is zero against the alternative that it is not using a two-tail test and the α = 0.05 level of significance. What do you conclude? (d) Test the null…

Bill wants to explore factors affecting work stress. He would like to examine the relationship between age, number of years at the workplace, perceived social support, and work stress. He collects data on the variables from 100 employees (males and females) working in banks. The research question is How accurately can work stress be predicted from linear combination of the predictors (age, social support, number of years at the workplace)? Conduct a multiple regression analysis to answer the following questions: What is the regression equation for all the predictors? Write a results section based on your analysis that answers the research question.

Bill wants to explore factors affecting work stress. He would like to examine the relationship between age, number of years at the workplace, perceived social support, and work stress. He collects data on the variables from 100 employees (males and females) working in banks. The research question is How accurately can work stress be predicted from linear combination of the predictors (age, social support, number of years at the workplace)? Conduct a multiple regression analysis to answer the following questions: What is the relationship of age, number of years, and social support with work stress? Is the regression significant? If yes, what does it indicate?

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 2 3 4 5 6 7 8 9 10 11 12 13 14 15 265.2 279.6 311.2 328.0 352.0 281.2 288.4 292.8 356.0 263.2 272.4 291.2 299.6 307.6 320.4 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…

In 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…

The St. Lucian Government is interested in predicting the number of weekly riders on the public buses using the following variables: • • • • Price of bus trips per weekThe population in the cityThe monthly income of ridersAverage rate to park your personal vehicle Determine the multiple regression equation for the data.  What is the predicted value of the number of weekly riders if: price of bus trips per week = $24; population = $2,000,000; the monthly income of riders = $13,500; and average rate to park your personal vehicle = $150.  Interpret the coefficient of determination.

Is the number of calories in a beer related to the number of carbohydrates​ and/or the percentage of alcohol in the​ beer? The accompanying table has data for 35 beers. The values for three variables are​ included: the number of calories per 12​ounces, the alcohol​ percentage, and the number of carbohydrates​ (in grams) per 12 ounces. a.  Perform a multiple linear regression​ analysis, using calories as the dependent variable and percentage alcohol and number of carbohydrates as the independent variables.   Let X1 represent alcohol percentage and let X2 represent the number of carbohydrates. ​(Round to four decimal places as​ needed.)

Five pairs of data are used in determining a regression line ŷ = -3 + 4x. If the five values of the explanatory variable are 28, 45, 16, 41, and 20, respectively, what is the mean of the five values of the response variable?

a. Develop the least squares estimated regression equation that relates labor hours to house square footage and type of flooring. b. Use the regression equation developed in part (a) to predict labor hours when the house size is 3350 square feet and the type of flooring is wood.