A linear regression model is designed to predict service charges by a bank (indollars per month) based on sales revenues of 26 companies who use theservices (in millions of dollars). Partial output from Excel givesŷ = -5428 + 32.756x,, with SSE = 117600, and the p-value = 0.04022 forthe estimated slope. Interpret the standard error of the estimate.ABEApproximately 95% of the observed service charges fall within $117600 of theleast squares lineNone of the suggested answers are correctFor every $1 million increase in sales revenue, we expect a service charge toincrease by $117600Approximately 96% of the observed service charges fall within $140 of the leastsquares line.Approximately 95% of the observed service charges equal their correspondingpredicted values

Solution: Given information: n= 26 Observation The estimated regression equation is…

Answered: A linear regression model is designed…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Q8The manager of IT department at a large government agency is investigating the relationship between the age of a PC in years (x) and its monthly maintenance cost (in Dhs). He conducted a regression analysis on a sample of 60 PCs (range from new to 10 years old) and reported the regression equation as y with hat on top equals 110 plus 20 space x Which of the following is the correct interpretation of the slope? For every additional year of age, the monthly maintenance costs increase on average by 110 Dhs. The slope is meaningless in this case. For every additional year of age, the expected monthly maintenance cost of PCs increases by 20 Dhs. The expected monthly maintenance cost of a new PC is 110 Dhs.
Chris collects personal data on coffee consumption x (in cups) and the following night's sleep y (in hours). Fitting a linear regression to the data, Chris learns that the slope is -0.43. One evening Chris uses his regression and predicts 6.47 hours of sleep. Then Chris remembers that his "coffee count" was off in making this estimate; Chris drank in an atypical additional 2 cups of coffee over lunch break. What should Chris' predicted sleep be (in hours)? Answer to the nearest 0.01 hours. (There is insufficient information here to produce the equation of the least squares line. You may need to look back in the course notes to find out how the slope is interpreted, and how that can be used to solve this problem.)
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) mported by a country, for the last seven years. Construct and interpret a 95% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,508 thousand barrels per day. The equation of the regression line is y = - 1.120x + 15,839.271. Oil produced, x 5,684 5,654 5,452 5,157 5,061 5,030 5,826 9,680 10,041 10,154 10,121 10,060 Oil imported, y 9,304 9,105 Construct and interpret a 95% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,508 thousand barrels per day. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) and O A. We can be 95% confident that when the amount of oil produced is 5,508 thousand barrels, the…
Using the regression formula with a slope = .704 and an intercept = .719, what would the predicted college GPA be for a student whose current high school GPA = 3.2?
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last 7 years. Construct and interpret a 98% prediction interval for the amount of oil imported by the country and the amount of oil produced by the country is 5,483 thousand barrels per day. The equation of the regression line is y = -1.194x + 16,234.906
1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.
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