Forecasting – Simple Linear Regression Applications
STATISTICS FOR MGT DECISIONS
FINAL EXAMINATION
Forecasting – Simple Linear Regression Applications
Interpretation and Use of Computer Output (Results)
NAME
SECTION A – REGRESSION ANALYSIS AND FORECASTING
1) The management of an international hotel chain is in the process of evaluating the possible sites for a new unit on a beach resort. As part of the analysis, the management is interested in evaluating the relationship between the distance of a hotel from the beach and the hotel’s average occupancy rate for the season. A sample of 14 existing hotels in the area is chosen, and each hotel reports its average occupancy rate. The management records the hotel’s distance (in miles) from the beach. The following set of data is obtained:
Distance (miles) 0.1 0.1 0.2 0.3 0.4 0.4 0.5 0.6 0.7
Occupancy (%) 92 95 96 90 89 96 90 83 85
Continue
Distance (miles) 0.7 0.8 0.8 0.9 0.9
Occupancy (%) 80 78 76 72 75
Use the computer output to respond to the following questions:
a) A simple linear regression was ran with the occupancy rate as the dependent (explained) variable and distance from the beach as the independent (explaining) variable
Occpnc = b[pic] + b[pic](Distncy)
What is the estimated regression equation?
The regression model is: Occpnc = b[pic] + b[pic](Distncy)
The estimated regression equation is: OCCUPNC = 99.61444 – 26.703 DISTNCY
b) Interpret the meaning behind the values you get for both coefficients b[pic] and b[pic]. b[pic]=99.61444, represent the y-intercept as well as the starting figure for the distance coverage. This is the amount of distance in miles that the hotel is from a beach. b[pic] = 26.703, represents the percentage of occupancy a hotel has depending on the distance of the hotel from a beach.
c) What sort of relationship exists between average hotel occupancy rate and the hotel’s distance from the beach? Does this relationship make sense to you? Why or why not?
FINAL EXAMINATION
Forecasting – Simple Linear Regression Applications
Interpretation and Use of Computer Output (Results)
NAME
SECTION A – REGRESSION ANALYSIS AND FORECASTING
1) The management of an international hotel chain is in the process of evaluating the possible sites for a new unit on a beach resort. As part of the analysis, the management is interested in evaluating the relationship between the distance of a hotel from the beach and the hotel’s average occupancy rate for the season. A sample of 14 existing hotels in the area is chosen, and each hotel reports its average occupancy rate. The management records the hotel’s distance (in miles) from the beach. The following set of data is obtained:
Distance (miles) 0.1 0.1 0.2 0.3 0.4 0.4 0.5 0.6 0.7
Occupancy (%) 92 95 96 90 89 96 90 83 85
Continue
Distance (miles) 0.7 0.8 0.8 0.9 0.9
Occupancy (%) 80 78 76 72 75
Use the computer output to respond to the following questions:
a) A simple linear regression was ran with the occupancy rate as the dependent (explained) variable and distance from the beach as the independent (explaining) variable
Occpnc = b[pic] + b[pic](Distncy)
What is the estimated regression equation?
The regression model is: Occpnc = b[pic] + b[pic](Distncy)
The estimated regression equation is: OCCUPNC = 99.61444 – 26.703 DISTNCY
b) Interpret the meaning behind the values you get for both coefficients b[pic] and b[pic]. b[pic]=99.61444, represent the y-intercept as well as the starting figure for the distance coverage. This is the amount of distance in miles that the hotel is from a beach. b[pic] = 26.703, represents the percentage of occupancy a hotel has depending on the distance of the hotel from a beach.
c) What sort of relationship exists between average hotel occupancy rate and the hotel’s distance from the beach? Does this relationship make sense to you? Why or why not?