Forecasting – Simple Linear Regression Applications

2335 words 10 pages
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
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SAVINGS = 23.14156 + 0.591446 INCOME - 0.341793 RENT - 1.119734 FOOD - 0.907868 ENTERT

b) What relationship exists between (i) SAVINGS and INCOME?, SAVINGS and RENT?, SAVINGS and FOOD expense, SAVINGS and ENTERTAINMENT expense?
There are no direct relationship between saving and income, savings and rent, savings and food expense, and savings and entertainment expense.

c) Which of the independent (explaining) variables are (is) significant in the multiple regression and which ones are (is) not significant (use α = 0.05 level of significance). Are the results in line with Maslow hierarchy of needs? Explain.

COMPUTER OUTPUT PART I

WEEKLY SAVINGS
REGRESSION FUNCTION & ANOVA FOR SAVINGS

SAVINGS = 23.14156 + 0.591446 INCOME - 0.341793 RENT - 1.119734 FOOD - 0.907868 ENTERT

R-Squared = 0.917562 Adjusted R-Squared = 0.870454 Standard error of estimate = 10.9635 Number of cases used = 12

Analysis of Variance p-value Source SS df MS F Value Sig Prob Regression 9364.86 4 2341.21 19.47795 0.000677 Residual 841.39 7 120.198 Total 10206.250 11

COMPUTER OUTPUT PART II

WEEKLY SAVINGS
REGRESSION COEFFICIENTS FOR SAVINGS

Two-Sided p-value Variable

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