Research Methods Multiple Regression Essay example

1733 WordsDec 9, 20137 Pages
Executive Summary This research provides statistical analysis for gross monthly sales in 60 stores using five key measures within a 10km vicinity: number of competitors, population in ‘000’s, average population income, average number of cars owned by households, and median age of dwellings. These quantitative variables are the key determinants, which will provide substance for descriptive statistics and the multiple linear regression model. This research reports mainly on statistical analysis, providing a direct interpretation of the research results. This process quantitates subjective judgments, while offering a scientific method of selecting location when chain convenience store enterprises such as GStore expand their scale.…show more content…
4 The final factor, the median age of dwellings is a good variable to look into. Whether or not the population consists of older or younger generations collecting this data is insightful as it helps determine their marketing activities and product offerings. 5 4.0 Data Analysis 4.1 Methodology To analyze the gross monthly sales of G-Store, a study between the five variables is conducted. Statistical approaches that are relevant i.e. descriptive statistics and multiple regression analysis using linear equation, ANOVA (F-Test), T-Test, and R2 analysis are used to analyze the data. 4.2 Descriptive Statistics Gross Monthly Sales Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Value 124,440.05 10,849.72 113,658.53 #N/A 84,041.59 7,062,988,237.33 -0.86 0.55 284,829.08 12,191.88 297,020.96 7,466,403.18 60 Figure 1.0 In Figure 1.0, we can conclude that the total monthly sales for the 60 stores are $7,466,403.18 and on average, a store earns $124,440.05 a month. The lowest earning store has only $12,191.88 while the most profitable store earns $297,020.96 a month. The standard deviation is high relative to the mean, at $84,041.58. This high standard deviation is because there is too much variation in the data. This can be eliminated by removing extreme
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