Fitting a straight line to a set of data yields

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Reference no: EM13173354

1. Fitting a straight line to a set of data yields the following prediction line:

i = 2 + 5Xi

a. Interpret the meaning of the Y intercept,

b. Interpret the meaning of the slope,

c. Predict the value of Y for X=3

2. The marketing manager of a large supermarket chain has the business objective of using

shelf space most efficiently. Toward that goal, she would like to use shelf space to predict the sales of pet food. Datais collected from a random sample of 12 equal-sized stores, with the following results (stored in Petfood

a. Construct a scatter plot. For these data, and

b. Interpret the meaning of the slope, in this problem.

c. Predict the weekly sales of pet food for stores with 8 feet of shelf space for pet food.

3. An agent for a residential real estate company has the business objective of developing more accurate estimates of the monthly rental cost for apartments. Toward that goal, the agent would like to use the size of an apartment, as defined by square footage to predict the monthly rental cost. The

agent selects a sample of 25 apartments in a particular residential neighborhood and collects the following data (stored in Rent

a. Construct a scatter plot.

b. Use the least-squares method to determine the regression coefficients and

c. Interpret the meaning of and in this problem.

d. Predict the monthly rent for an apartment that has 1,000 square feet.

4. rating to predict the cost of a restaurant meal (stored in ). For those data, and

a. Determine the coefficient of determination, and interpret its meaning.

b. Determine the standard error of the estimate.

c. How useful do you think this regression model is for predicting audited sales?

5. an agent for a real estate company wanted to predict the monthly rent for apartments, based on the size of the apartment (stored in ).Using the results of that problem,

a. determine the coefficient of determination, and interpret its meaning.

b. determine the standard error of the estimate.

c. How useful do you think this regression model is for predicting the monthly rent?

d. Can you think of other variables that might explain the variation in monthly rent?

Reference no: EM13173354

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