MAT243 Quiz 5
The ols() method in statsmodels is used to fit a simple linear regression model using
"Exam4" as the response variable and "Exam3" as the predictor variable. The output is
shown below. A text version is available. What is the correct regression equation based
on this output? Is this model statistically significant at 10% level of significance (alpha =
0.10)? Select one.
(Hint: Review results of F-statistic)
Question options:
Exam4 = 68.9576 + 0.1028 Exam3, model is not statistically significant
Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant
Exam4 = 76.85 + 0.206 Exam3, model is not statistically significant
Exam4 = 76.85 + 0.206 Exam3, model is statistically significant - ANS-I think:
Exam4 = 68.9576 + 0.1028 Exam3, model is statistically significant
tried and failed:
Exam4 = 68.9576 + 0.1028 Exam3, model is not statistically significant
Which of the following correctly represents the coefficient of determination in terms of
the variance that is an output from the analysis of variance table? Select one.
Question options:
The numerator is explained variance + unexplained variance / total variance.Explained
variance / total variance
unexplained variance / total variance
1 / total variance - ANS-Explained variance / total variance
The linregress() method in scipy module is used to fit a simple linear regression model
using "Reaction" (reaction time) as the response variable and "Drinks" as the predictor
variable. The output is shown below. What is the correct regression equation based on
this output? Is this model statistically significant at 5% level of significance (alpha =
0.05)? Select one.
Question options:
Reaction = 3.9999 + 6.0000 Drinks, model is statistically significant
Reaction = 6.0000 + 3.9999 Drinks, model is statistically significant
Reaction = 6.0000 + 3.9999 Drinks, model is not statistically significant
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