Question;Executive SummaryJennie Garcia is store manager of coffee shop. She is in charge of operation andplanning for the companys southern region. And she tracks store revenue and anticipatescoffee demand. As the company grew, stores become various. There are many different typesof coffee shop now. So she want to figure out why there are revenue differences in stores..AnalysisThe scatter plot of coffee shop is shown below.Here x axis represents store size which is my Independent variable. And y axis representsweekly sales which is dependent variable. An upward trend is observed for store size (X) andweekly sales (Y) which indicates that there is positive correlation between the two variables.Thus, as value of store size (X) increases, value of weekly sales (Y) also increases. Itscorrelation coefficient is 0.8101, implying there is strong positive correlation coefficientbetween the two variables.Performing regression analysis, the following output is obtained.Regression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservations0.8101070.6562730.649533277.115553ANOVARegressionResidualTotalInterceptStore Size(Sq. Ft.)df15152Coefficients1612.4673.110897SS7477596391644411394040StandardError294.34070.315258t Stat5.4782329.867795MS747759676793.02Pvalue1.33E-062.03E-13F97.37338Lower95%1021.5532.477991Upper95%2203.383.743803Significance F2.03E-13Lower95.0%1021.5532.477991Upper95.0%2203.383.743803R12 = 0.6562 implies that 65.62% of the variation of weekly sales (Y) around its mean(y-bar) is explained by the independent variables Price for store size (X). Thus, the fitted lineis a good fit to the data and the model seems to be accurate.Regression Equation: Y = 1612.467 + 3.110897 (X)Intercept = 1612.467, which tells me the initial value of Weekly sales (Y) when storesize (X) is zero. Slope = 3.11 which tells the us that there is 3.11 units increase in weeklysales (Y) when there is unit increase in store size (X).Hypothesis Testing:Ho1: 1=0, 1 is not significantv/s H11: 10, 1 is significant.p-value = 0.0000Since p-value < 0.05, reject H01 at 5% level of significance and conclude that 1 is significant.Thus, Store Size (X) should be included in the model.From the data analysis, y = 1612.467 + 3.1109 x. Weekly store sales are expected to average$5 per square foot. 1000 square foot store have average weekly sales of 5000. From thisequation if x = 1000, y = 4723.367. Even there is difference between expected value andobserved value. I think it is reasonable.ConclusionThere is strong linear positive relationship between store size and weekly sales.Large store size causes high weekly sales.Even there are differences between observed sales and expected sales, it is acceptable.
Paper#61445 | Written in 18-Jul-2015Price : $27