4-1 Data Set 2 (Correlation and Regression)

4-1 Data Set 2 (Correlation and Regression) - = 6.298850575...

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QSO-510 Professor: A Duhon 4-1 Correlation and Regression (Data Set 2) In predicting the number of defective flash drives over time (in weeks), we want our regression equation to show how the y variable (number of defective flash drives) changes as the week (x variable) increases by one. It’s the week number that affects the number of defective flash drives and not the other way around. In this case, we will regress the week number on the x-axis (independent variable) against the number of defective flash drives (dependent variable) on the y-axis. Below is my summary output. Ŷ i = 6.298850575 + 0.047385984 (Week i ) ^ Number of Defective Flash Drives
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Unformatted text preview: = 6.298850575 + 0.047385984 (Week) InterpretaTon : As week goes up by 1, the predicted number of defecTve Fash drives goes up by 0.047385984, ceteris paribus. InterpretaTon of R-Squared (R 2 ) = 0.0.091812141 R 2 = 0.091812141 = 9.18% The value of R 2 means that 9.18% of the variation in Number of defective flash drives can be explained by the variations in Week Number. This is a very low number and as such, it can be concluded that the number of defective flash drives is not significantly influenced by the week number, but rather by another lurking variable or variables....
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