lm(formula =Length ~ Height) Coefficients: Estimate Std. Error t value Pr(>|t|)
ID: 3157793 • Letter: L
Question
lm(formula =Length ~ Height)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 222.1093 9.6527 27.050 0.0324
Height 0.257 0.1429 -1.804 0.0713
Residual standard error: 29.22 on 2587 degrees of freedom
Multiple R-squared: 0. 1256, Adjusted R-squared: 0.08704
4a: Using the information from the output, write the least squares regression line for this data
4b: Is the intercept significant? Is the Height slope significant? Use alpha=.05
4c: How much of the variation of this dataset can be attributed to Height?
lm(formula = Length~ Weight)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 450.308 2.46451 101.565 <.0001
Weight 0.1137 0.04144 -2.746 0.0407
Residual standard error: 29.2 on 2587 degrees of freedom
Multiple R-squared: 0.2907, Adjusted R-squared: 0.2521
5a: Using the information from the output, write the least squares regression line for this data
5b: Is the intercept significant? Is the Weight slope significant? Use alpha=.05
5c: How much of the variation of this dataset can be attributed to Weight?
5d: Using your least squares equation, what would the predicted Length be for a Weight of 24.4?
Explanation / Answer
4a:
the least squares regression line for this data is
Length = 222.1093 + 0.257 Height
4b:
The p-value corresponding to intercept is 0.0324. Since p-value is less than alpha=.05 so intercept is significant to the model.
The p-value corresponding to slope is 0.0713 . Since p-value is not less than alpha=.05 so slope is not significant to the model.
4c:
R-sqaure is 0. 1256 so 12.56% variation of this dataset can be attributed to Height.
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