The gas station owners want to understand the patterns of gasoline sales in Louisville KY. Examining the historical sales data for the past few years gave them great insights but they were unable to quantify these insights. Frustrated, they hired MBA students from UofL to use their business analytics skills and determine some of the trends in the data. The MBA students created three dummy variables (Q1, Q2, Q3) to represent four quarters and a time variable to represent the time in quarters. The results of the regression are as below:


1- Is there a seasonal pattern in the data? Why/why not?

Variables Entered/Removeda
Variables
Removed
Model
Variables
Entered
Q3, Time, Q2,
01
Method
Enter
1
a. Dependent Variable: A

a) Yes, the time variable is significant

b) No, the time variable is significant

c) No, the dummy variables for the quarters are not significant

d) Yes, the dummy variables for the quarters are not significant

2- This predictive model has a good fit?

True

False

3- What is the relationship between time and actual gasoline sales?

Controlling for seasonality, gasoline sales increase by 4.089 every quarter

Controlling for seasonality, gasoline sales increase by .598 every quarter

Controlling for seasonality, gasoline sales increase by 14.14 every quarter

Controlling for seasonality, gasoline sales increase by 57.83 every quarter