Correlation and Regression (Deb The owner of Pizza, Ice Cream, and Coffee Palace) (Statistics Project Sample)
Deb is the owner of Deb\\\'s Pizza, Ice Cream, and Coffee Palace - an establishment that prides itself on providing essential survival supplies for college students. She is currently selling pizzas to Linfield College students in a small shop on 3rd St. in McMinnville, but is considering an expansion of her current business to other campuses and needs help making this decision. She\\\'s collected some data and asks you to help her analyze and present it to the Small Business Administration loan officer who will either reject her proposal or provide funding for the proposed expansion. Each of the following worksheets has a small data set and one or more questions. Analyze the data, answer the questions, and report your conclusions in the form of a business memorandum for Deb. Data calculations should not be placed in the memo since the Excel spreadsheet data page will be attached. Be sure to also upload the spreadsheet with your calculations along with your memo since you will answer a set of questions on multiple regression on this spreadsheet (or attach a separate Word doc if you wish.)source..
Correlation and Regression
To: Deb The owner of Pizza, Ice Cream, and Coffee Palace
SUBJECT: Expansion of the Business
Deb intended to get information on the current Pizza, Ice Cream, and Coffee Palace business’ performance and the possibility of an expansion. Consequently, a thorough research and analysis was conducted to confirm, or oppose the owner’s idea. First a linear regression analysis on cold calls versus the number of machines sold revealed that called calls is a good predictor of the number of machines sold. It was also clear that measurements weight, size of abdomen, and thighs of an individual were significant predictors of an individual’s fat content.
Cold Calls a Good Predictor of the Number of Machines Sold
Deb should appreciate that the number of cold calls used by sales representatives is an efficient predictor of the number of machines sold. This is highlighted by the evidence acquired after conducting a simple linear regression of the variables. It is notable that cold calls was the predictor variable while the number of machines sold was the response variable.
SUMMARY OUTPUT AT Alpha=0.05Regression StatisticsMultiple R0.7834R Square0.6137Adjusted R 0.5654Standard Error5.2078Observations10ANOVA dfSSMSFSignificance FRegression1344.6274344.627412.70680.0074Residual8216.972627.1216Total9561.6 CoefficientsStandard Errort StatP-value95%Upper 95%Intercept0.21436.27540.03410.9736-14.256914.6855X Variable 10.46520.13053.56470.00740.16430.7662The model was an overall fit because the significance of the F value (0.0074) was less than 0.05. Furthermore, the R squared was 0.614 (it is greater than 0.5), which implies that 61% of the variation in machines sold is explained by the variation in the cold calls. Therefore, the number of cold calls can predict the number of machines sold. Considering the regression equation, the data revealed the following equation could predict the number of machines sold.
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