Chapter 12, Problem 12.16AC

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?

Demand 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 2.00

bThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.

If you travel 100 miles in two hours, then your average speed for the trip is Average speed=_________=________

It 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.

Suppose 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) - 23.015

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.

A 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 X

The 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.