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Many statistics courses cover a topic called multiple regression. This provides a means to predict the value of a dependent variable
The gas mileage
a. Use the data create a model of the form
b. Use the model from part (a) to predict the gas mileage of a vehicle that is
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- Cellular Phone Subscribers The table shows the numbers of cellular phone subscribers y in millions in the United States from 2008 through 2013. Source: CTIA- The Wireless Association Year200820092010201120122013Number,y270286296316326336 (a) Find the least squares regression line for the data. Let x represent the year, with x=8 corresponding to 2008. (b) Use the linear regression capabilities of a graphing utility to find a linear model for the data. How does this model compare with the model obtained in part a? (c) Use the linear model to create a table of estimated values for y. Compare the estimated values with the actual data.arrow_forwardHOW DO YOU SEE IT? Discuss how well a linear model approximates the data shown in each scatter plot.arrow_forwardWe have data on Lung Capacity of persons and we wish to build a multiple linear regression model that predicts Lung Capacity based on the predictors Age and Smoking Status. Age is a numeric variable whereas Smoke is a categorical variable (0 if non-smoker, 1 if smoker). Here is the partial result from STATISTICA. b* Std.Err. of b* Std.Err. N=725 of b Intercept Age Smoke 0.835543 -0.075120 1.085725 0.555396 0.182989 0.014378 0.021631 0.021631 -0.648588 0.186761 Which of the following statements is absolutely false? A. The expected lung capacity of a smoker is expected to be 0.648588 lower than that of a non-smoker. B. The predictor variables Age and Smoker both contribute significantly to the model. C. For every one year that a person gets older, the lung capacity is expected to increase by 0.555396 units, holding smoker status constant. D. For every one unit increase in smoker status, lung capacity is expected to decrease by 0.648588 units, holding age constant.arrow_forward
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- A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y = 74.80 + 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with O rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…arrow_forwardIn a linear regression model, the dependent variable is "Final exam score (%) for WPC 300" and the independent variable is "Hours studied". A coefficient of 4 could be interpreted as for every one % increase in the final exam score, the expected hours of study is 4. four hours of additional study, the expected increase in the final exam score is 1%. four hours of additional study, the final exam score is expected to increase by 4%. hour of additional study, the expected final exam score increases by 4%.arrow_forwardBill 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.arrow_forward
- 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?arrow_forwardPart I. Run two regressions in Excel using the provided Excel file “Layoffs”.The Excel file Layoffs provides data on 50 manufacturing workers who lost their jobs due to layoffs. The data includes the following list of variables:Weeks – the number of weeks a manufacturing worker has been without a jobAge – the age of the workerEducation – the number of years of education of the workerMarried – a dummy variable, equal to 1 if the worker is married, 0 otherwiseHead – a dummy variable, equal to 1 if the worker is a head of household, 0 otherwiseTenure – the number of years on the previous jobManager – a dummy variable, equal to 1 if the worker had a management occupation, 0 otherwise Sales – a dummy variable, equal to 1 if the worker had an occupation in sales, 0 otherwise 1. Run a simple regression with a dependent variable Weeks and an independent variable Age. Create the regular and standardized residual plots for the simple regression. 2. Run a multiple regression with a dependent…arrow_forwardInterest rates for home mortgages have, in general, declined during recent months. With the apparent favorable influence for new-home building, there seems to be a clear relationship between x = the prevailing mortgage interest rate and y = the number of new houses being built per month in a Midwestern city over a period of 18 months. A scatterplot of the data collected shows that the linear model is appropriate. The equation of the least-squares regression line is Number of new houses = 672.89- 30.65 x Interest rate and ² = 0.49. What is the correlation coefficient between Interest rate and Number of new houses being built? (Give your answer to one decimal place.)arrow_forward
- Elementary Linear Algebra (MindTap Course List)AlgebraISBN:9781305658004Author:Ron LarsonPublisher:Cengage Learning