Define the null hypothesis in mathematical terms and in words Define the alternative hypothesis in mathematical terms and in words Provide your conclusion and interpretation of the test. Should the null hypothesis be rejected? Why or why not?
Define the null hypothesis in mathematical terms and in words Define the alternative hypothesis in mathematical terms and in words Provide your conclusion and interpretation of the test. Should the null hypothesis be rejected? Why or why not?
Trigonometry (MindTap Course List)
8th Edition
ISBN:9781305652224
Author:Charles P. McKeague, Mark D. Turner
Publisher:Charles P. McKeague, Mark D. Turner
Chapter4: Graphing And Inverse Functions
Section: Chapter Questions
Problem 6GP: If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use...
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Define the null hypothesis in mathematical terms and in words
Define the alternative hypothesis in mathematical terms and in words
Provide your conclusion and interpretation of the test. Should the null hypothesis be rejected? Why or why not?
![OLS Regression Results
R-squared:
Adj. R-squared:
Dep. Variable:
0.821
mpg
OLS
Model:
0.808
Method:
Least Squares
F-statistic:
61.98
Prob (F-statistic) :
Log-Likelihood:
Date:
Wed, 07 Oct 2020
8.10e-11
Time:
11:19:54
-70.463
No. Observations:
30
AIC:
146.9
Df Residuals:
27
BIC:
151.1
Df Model:
2
Covariance Type:
nonrobust
coef
std err
P>lt|
[0.025
0.975]
Intercept
37.1838
1.650
22.540
0.000
33.799
40.569
wt
-3.8810
0.658
-5.902
0.000
-5.230
-2.532
hp
-0.0309
0.010
-3.201
0.003
-0.051
-0.011
Omnibus:
4.159
Durbin-Watson:
2.606
Prob (Omnibus) :
0.125
3.100
Jarque-Bera (JB) :
Prob (JB) :
Skew:
0.784
0.212
Kurtosis:
3.147
Cond. No.
561.
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ffe3e5ed4-a864-47ba-ae80-e9432e0c9ca5%2Ffa37a984-f64d-48b6-8066-b84db7708bc4%2F698k2yl_processed.png&w=3840&q=75)
Transcribed Image Text:OLS Regression Results
R-squared:
Adj. R-squared:
Dep. Variable:
0.821
mpg
OLS
Model:
0.808
Method:
Least Squares
F-statistic:
61.98
Prob (F-statistic) :
Log-Likelihood:
Date:
Wed, 07 Oct 2020
8.10e-11
Time:
11:19:54
-70.463
No. Observations:
30
AIC:
146.9
Df Residuals:
27
BIC:
151.1
Df Model:
2
Covariance Type:
nonrobust
coef
std err
P>lt|
[0.025
0.975]
Intercept
37.1838
1.650
22.540
0.000
33.799
40.569
wt
-3.8810
0.658
-5.902
0.000
-5.230
-2.532
hp
-0.0309
0.010
-3.201
0.003
-0.051
-0.011
Omnibus:
4.159
Durbin-Watson:
2.606
Prob (Omnibus) :
0.125
3.100
Jarque-Bera (JB) :
Prob (JB) :
Skew:
0.784
0.212
Kurtosis:
3.147
Cond. No.
561.
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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