EBK BUSINESS STATISTICS
7th Edition
ISBN: 8220102743984
Author: STEPHAN
Publisher: PEARSON
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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.
Imagine a regression line that relates y = average systolic blood pressure to x = age. The average blood pressure for people 40 years old is 90, while for those 55 years old the average is 130.
What is the estimated average systolic blood pressure for people who are 50 years old? (Round the answer to the nearest whole number.)
The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below.
What proportion of variation in the number of complaints can be explained by hourly wages?
From the results shown above, write the regression equation
If wages were increased by $1.00, what is the expected effect on the number of complaints received per employee?
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- Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300arrow_forwardTable 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?arrow_forwardDoes Table 1 represent a linear function? If so, finda linear equation that models the data.arrow_forward
- Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?arrow_forwardDemand for Candy Bars In this problem you will determine a linear demand equation that describes the demand for candy bars in your class. Survey your classmates to determine what price they would be willing to pay for a candy bar. Your survey form might look like the sample to the left. a Make a table of the number of respondents who answered yes at each price level. b Make a scatter plot of your data. c Find and graph the regression line y=mp+b, which gives the number of respondents y who would buy a candy bar if the price were p cents. This is the demand equation. Why is the slope m negative? d What is the p-intercept of the demand equation? What does this intercept tell you about pricing candy bars? Would you buy a candy bar from the vending machine in the hallway if the price is as indicated. Price Yes or No 50 75 1.00 1.25 1.50 1.75 2.00arrow_forwardbThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.arrow_forward
- If you travel 100 miles in two hours, then your average speed for the trip is Average speed=_________=________arrow_forwardIt seems logical that the more bank accounts there are, the more ATM withdrawals there would be. Using the data from Central Bank of Malaysia, a simple regression analysis for one year data have been done to predict the number of ATM withdrawals by the number of bank accounts: the following result is the regression output from the data.arrow_forwardSuppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package measured in ounces. You want to try to predict the cost of a typical shipment given package dimensions. If 10 packages in a city are sampled and the regression output is given below, report the regression equation. 1) (cost of delivery) = 1.468*(weight) - 23.015 2) (weight) = -23.015*(cost of delivery) + 1.468 3) (cost of delivery) = -23.015*(weight) + 1.468 4) (cost of delivery) = 1.468*(weight) 5) (weight) = 1.468*(cost of delivery) - 23.015arrow_forward
- Before buying a new car , a consumer wants to learn how the weight of a car affects highway gas mileage. Statistical software was used to conduct a simple linear regression about the relationship between the weight (in lbs) of a car and its highway mpg. The following equation for the regression line was given: mpg=49.5-0.0081weight If your car weighs 3200 lbs , what does the model predict in the highway mpg? Round to 1 decimal places.arrow_forwardA real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y = sales price (in thousands of dollars) x₁ = total floor area (in square feet) x₂ = number of bedrooms x3 distance to nearest high school (in miles) = The estimated model is as follows. =76+0.098x₁ +16x₂ - 8x3 Answer the questions below for the interpretation of the coefficient of X₂ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each additional bedroom in a house? dollars (b) Is this change an increase or a decrease? O increase O decrease Xarrow_forwardThe following regression model shows the relationship between selling price of a house(y) and the number of bedrooms(x); y=100+0.6x. Another separate regression between selling price of the house(y) and the level of crime per week in the neighborhood(x); y=100+0.3x. Draw the regression line for the first and second regression. Write an interpretation for the slope coefficient in the first and second regression. Be specific. What is the estimated value of the price of a house which has two bedrooms.arrow_forward
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