Question

Concur Technologies, Inc., is a large expense-management company located in Redmond, Washington.  The Wall Street Journal asked Concur to examine the data from 8.3 million expense reports to provide insights regarding business travel expenses. Their analysis of the data showed that New York was the most expensive city, with an average daily hotel room rate of $198 and an average amount spent on entertainment, including group meals and tickets for shows, sports, and other events, of $172. In comparison, the U.S. averages for these two categories were $89 for the room rate and $99 for entertainment. The table in the Excel Online file below shows the average daily hotel room rate and the amount spent on entertainment for a random sample of 9 of the 25 most visited U.S. cities (The Wall Street Journal, August 18, 2011). Construct a spreadsheet to answer the following questions.

City	Hotel Room Rate ($)	Entertainment ($)
Boston	144	163
Denver	100	104
Nashville	93	102
New Orleans	111	140
Phoenix	89	100
San Diego	105	121
San Francisco	134	165
San Jose	87	140
Tampa	82	97

1. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables?

   The scatter diagram indicates a  _________negativepositive linear relationship between the hotel room rate and the amount spent on entertainment.

2. Develop the least squares estimated regression equation.

        (to 4 decimals)

3. Provide an interpretation for the slope of the estimated regression equation (to 3 decimals).

   The slope of the estimated regression line is approximately  . So, for every dollar _________increasedecrease in the hotel room rate the amount spent on entertainment increases by $.

4. The average room rate in Chicago is $128, considerably higher than the U.S. average. Predict the entertainment expense per day for Chicago (to whole number).

Answer 1

A).

Consider the following fig shows the scatter plot of two variables “Hotel Rate” and “Entertainment”.

So, the above fig shows the positive relationship between the two variables.

B).

So, let’s assume that “X=Hotel Rate” and “Y=Entertainment”, => the following table shows the regression analysis of the given problem.

So, here the regression model is, => Y = b0 + b1*X, => the estimated model is given by.

=> Y = 14.7508 + 1.0574*X.

C).

Here the slope coefficient is given by “1.0574”, => for every dollar increase in “hotel rate” the “Entertainment” increases by “$1.06”.

D).

For average room rate of “$128” the predicted entertainment expense is given by.

=> Y = 14.7505 + 1.0574*X = 14.7505 + 1.0574*128 = 150.0977, => the predicted entertainment expense is given by, “$150.0977”.