BCOR Stats Project 2

BCOR Stats Project 2 - R 2 = 0.5104(500.00-500.00 1,000.00...

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Collin Mitchell Business Statistics Rec #: After viewing the scatter plot made with the data there are a few interesting characteristics. The scatter plot shows a clear downward slope. Most of the data is centralized and appears to be following the slope. There do not seem to be very many outliers. (12.32) The regression line that is given seems to be fairly credible. The r squared value is fairly high being that it is .5104. I would say that this regression model fits the scatter plot well. (12.33) The slope of the line fits the data well. It seems to pass evenly through the data. Most data points are really close to the line. The intercept in this case doesn’t make sense. The general trend of this scatter plot says that as square footage goes down annual revenue increases. It doesn’t make any sense to have a store with 0 square feet. (12.34) Annual Revenue per Square Feet y = -0.8784x + 3339
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Unformatted text preview: R 2 = 0.5104 (500.00)-500.00 1,000.00 1,500.00 2,000.00 2,500.00 3,000.00 500 1000 1500 2000 2500 3000 3500 4000 4500 Square Feet Annual Revenue Based on the R squared and the ANOVA table I can say that the fit of the regression line is fairly significant. The R squared value is .5104 which is high enough to say that the variability isn’t to large. The p-value for the regression is 8.83E-13. Since the p-value is less the alpha(.05) we can say that there is significant evidence that this regression line fits it well. I would say that this model has good enough fit to be of practical value. (12.37) There are two unusual data points. Those points are observation 2 and observation 30. There are no outliers in this data. One reason for these unusual data points may be due to the place where the store is. These two observation may be in a more populated area....
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BCOR Stats Project 2 - R 2 = 0.5104(500.00-500.00 1,000.00...

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