The statsmodels ols() method is used on an exam scores dataset to fit a multiple regression model using Exam4 as the response variable. Exam1, Exam2, and Exam3 are used as predictor variables. The general form of this model is: Y = Bo + B,Examl+ B2Exam2 + B3Exam3 If the level of significance, alpha, is 0.10, based on the output shown, is Exam 1 statistically significant in the multiple regression model shown above? Select one. A text version of this output is available.

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The statsmodels ols() method is used on an exam scores dataset to fit a multiple
regression model using Exam4 as the response variable. Exam1, Exam2, and Exam3
are used as predictor variables. The general form of this model is:
Y = Po + P,Examl+ B2Exam2 + B3Exam3
If the level of significance, alpha, is 0.10, based on the output shown, is Exam1
statistically significant in the multiple regression model shown above? Select one.
A text version of this output is available.
OLS Regression Results
Dep. Variable:
Model:
Method:
R-squared:
Adj. R-squared:
F-statistic:
Prob (F-statistic):
Log-Likelihood:
AIC:
BIC:
0.178
0.125
3.329
0.0276
-169.85
347.7
355.4
Exam4
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
OLS
Least Squares
Sun, 18 Aug 2019
10:59:12
50
46
3
nonrobust
coef
std err
t
P>|t|
[0.025
0.975]
Intercept
Examl
Exam2
Exam3
46.2612
0.1742
0.1462
0.0575
10.969
0.120
0.078
0.053
4.217
1.453
1.873
1.085
0.000
0.153
0.067
0.284
24.181
-0.067
-0.011
-0.049
68.341
0.416
0.303
0.164
Omnibus:
Prob(Omnibus):
skew:
Kurtosis:
0.886
0.642
0.290
2.868
Durbin-Watson:
Jarque-Bera (JB):
Prob(JB):
Cond. No.
1.530
0.738
0.691
1.41e+03
Transcribed Image Text:The statsmodels ols() method is used on an exam scores dataset to fit a multiple regression model using Exam4 as the response variable. Exam1, Exam2, and Exam3 are used as predictor variables. The general form of this model is: Y = Po + P,Examl+ B2Exam2 + B3Exam3 If the level of significance, alpha, is 0.10, based on the output shown, is Exam1 statistically significant in the multiple regression model shown above? Select one. A text version of this output is available. OLS Regression Results Dep. Variable: Model: Method: R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: AIC: BIC: 0.178 0.125 3.329 0.0276 -169.85 347.7 355.4 Exam4 Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: OLS Least Squares Sun, 18 Aug 2019 10:59:12 50 46 3 nonrobust coef std err t P>|t| [0.025 0.975] Intercept Examl Exam2 Exam3 46.2612 0.1742 0.1462 0.0575 10.969 0.120 0.078 0.053 4.217 1.453 1.873 1.085 0.000 0.153 0.067 0.284 24.181 -0.067 -0.011 -0.049 68.341 0.416 0.303 0.164 Omnibus: Prob(Omnibus): skew: Kurtosis: 0.886 0.642 0.290 2.868 Durbin-Watson: Jarque-Bera (JB): Prob(JB): Cond. No. 1.530 0.738 0.691 1.41e+03
Null Hypothesis Ho: B1 = 0
Alternative Hypothesis H;: B1 # 0
P-value = 0.153
Since the P-value is greater than the level of significance, alpha, reject the null
hypothesis. Conclude that the estimate for the Exam1 parameter is non-zero.
Therefore, Exam1 is statistically significant in the multiple regression model.
Null Hypothesis H,: B1 = 0
Alternative Hypothesis H1: B1 + 0
P-value = 0.1742
%3D
Since the P-value is greater than the level of significance, alpha, reject the null
hypothesis and conclude that the estimate for the Exam1 parameter is non-zero.
Therefore, Exam1 is statistically significant in the multiple regression model.
Null Hypothesis Ho: B1 = 0
Alternative Hypothesis H1: B1 + 0
P-value = 0.153
%3D
Since the P-value is greater than the level of significance, alpha, do not reject the
null hypothesis. Conclude that the estimate for the Exam1 parameter is zero.
Therefore, Exam1 is not statistically significant in the multiple regression model..
Null Hypothesis Ho: B1 = 0
Alternative Hypothesis H1: B1 + 0
P-value = 0.1742
Since the P-value is greater than the level of significance, alpha, do not reject the
null hypothesis. Conclude that the estimate for the Exam1 parameter is zero.
Therefore, Exam1 is not statistically significant in the multiple regression model.
Transcribed Image Text:Null Hypothesis Ho: B1 = 0 Alternative Hypothesis H;: B1 # 0 P-value = 0.153 Since the P-value is greater than the level of significance, alpha, reject the null hypothesis. Conclude that the estimate for the Exam1 parameter is non-zero. Therefore, Exam1 is statistically significant in the multiple regression model. Null Hypothesis H,: B1 = 0 Alternative Hypothesis H1: B1 + 0 P-value = 0.1742 %3D Since the P-value is greater than the level of significance, alpha, reject the null hypothesis and conclude that the estimate for the Exam1 parameter is non-zero. Therefore, Exam1 is statistically significant in the multiple regression model. Null Hypothesis Ho: B1 = 0 Alternative Hypothesis H1: B1 + 0 P-value = 0.153 %3D Since the P-value is greater than the level of significance, alpha, do not reject the null hypothesis. Conclude that the estimate for the Exam1 parameter is zero. Therefore, Exam1 is not statistically significant in the multiple regression model.. Null Hypothesis Ho: B1 = 0 Alternative Hypothesis H1: B1 + 0 P-value = 0.1742 Since the P-value is greater than the level of significance, alpha, do not reject the null hypothesis. Conclude that the estimate for the Exam1 parameter is zero. Therefore, Exam1 is not statistically significant in the multiple regression model.
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