Question 1: Gas expenses and mileage The Human Resources Department of a financial consultancy firm tracks the expenses paid by its employees when they use their personal cars to meet clients to ensure that employees are properly reimbursed. The following data on gas expenses and mileage is collected from 10 employees. Employee Gas expense Miles 1 120 1000 100 800 3 80 650 4 150 1500 130 1300 92 720 7 115 850 8 122 1200 9. 110 900 10 90 700 Instructions: Using the Excel worksheet that contains the same data and is attached below, answer the following questions. You may include your answers on the same worksheet that contains your Excel output. Be sure to be clear with answers to each part. After you are done, you will attach your Excel worksheet here. Questions: a. What linear regression equation best predicts the gas expenses of the employees based on the number of miles driven? b. If an employee drove 2000 miles, what gas expense would she incur? Are there any limitations on this prediction? c. How well does the regression equation fit the data? d. What other variables do you think would be appropriate to add to your model to make it better at predicting the gas expense?

Question 1: Gas expenses and mileage The Human Resources Department of a financial consultancy firm tracks the expenses paid by its employees when they use their personal cars to meet clients to ensure that employees are properly reimbursed. The following data on gas expenses and mileage is collected from 10 employees. Employee Gas expense Miles 1 120 1000 100 800 3 80 650 4 150 1500 130 1300 92 720 7 115 850 8 122 1200 9. 110 900 10 90 700 Instructions: Using the Excel worksheet that contains the same data and is attached below, answer the following questions. You may include your answers on the same worksheet that contains your Excel output. Be sure to be clear with answers to each part. After you are done, you will attach your Excel worksheet here. Questions: a. What linear regression equation best predicts the gas expenses of the employees based on the number of miles driven? b. If an employee drove 2000 miles, what gas expense would she incur? Are there any limitations on this prediction? c. How well does the regression equation fit the data? d. What other variables do you think would be appropriate to add to your model to make it better at predicting the gas expense?

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.3: Modeling Data With Power Functions

Problem 6E: Urban Travel Times Population of cities and driving times are related, as shown in the accompanying...

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