Question 1
This histogram shows residuals from a simple linear regression:
It appears that:
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there is a dependence among the errors.
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the mean of the error distribution is not zero.
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the error distribution is not normal.
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the error variance is not constant.
Question 2
We record the amount of time (x in minutes) per day that a group of 12 high school students spend using their cell phone (texting, talking, and web use). We also record the overall GPA (y) of each student. Following a regression summary for these data:
The regression equation is
GPA = 4.01 - 0.0153 CellTime
Predictor Coef SE Coef T P
Constant 4.0084 0.2486 6.12 0.000
CellTime -0.015334 0.003226 -4.75 0.001
S = 0.472864 R-Sq = 69.3% R-Sq(adj) = 66.3%
Analysis of Variance
Source DF SS MS F P
Regression 1 5.0519 5.0519 22.59 0.001
Residual Error 10 2.2360 0.2236
Total 11 7.2879
Does there appear to be a significant linear relationship between theamount of cell phone use and GPA? (Use α = 0.01.)
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No, because ther2value is too low.
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Yes, because the regression equation is defined.
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Yes, because thep-value from the test for significance is 0.000.
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Yes, because thep-value from the test for significance is 0.001.
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