a) State the hypothesis in words and mathematical notation. State the alpha reflected in the STATA output. H₁: B₁ doesn't = 0 (at least one of the coefficients inta 1 H₂: BK = 0 b) Fill in the missing variables. 1. 2. 3. 4. 5. df1 = ког df2= N-K-1 = 831 Means Squares Regression = 147489013/2 Mean Squares Residual = 2687917.95/831= Obtained F-value = 737.45 MSR 737.45 = 2,512.54 0.29 = > MSE 2,5₁2. 54. d) Make a decision about your hypothesis and state it fully.
a) State the hypothesis in words and mathematical notation. State the alpha reflected in the STATA output. H₁: B₁ doesn't = 0 (at least one of the coefficients inta 1 H₂: BK = 0 b) Fill in the missing variables. 1. 2. 3. 4. 5. df1 = ког df2= N-K-1 = 831 Means Squares Regression = 147489013/2 Mean Squares Residual = 2687917.95/831= Obtained F-value = 737.45 MSR 737.45 = 2,512.54 0.29 = > MSE 2,5₁2. 54. d) Make a decision about your hypothesis and state it fully.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
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I was wondering specifically about part D and how to interpret my findings.
![Q5. This regression analysis on the number of hours spent emailing for work (EMAILHRS) is the
dependent variable and years of education (EDUC) is the independent variable. Dang. Once again, STATA
forgot to report key values (or someone deleted them, hmmm). Using what is left, answer the questions
below.
regress EMAILHR EDUC, level (99)
Source |
Model | 1474.89013
Residual | 2087917.95
Total | 2089392.84
EMAILHR
1.
2.
3.
4.
5.
SS
EDUC | .4443524
cons | 3.190798
H1
Coef.
Ho: Bk.
ко 2
Вк doesn't
BK=0
b) Fill in the missing variables.
df
Std. Err.
5506034
7.833696
MS
2
831
833 2263.69755
737.45
t
Number of obs
F(,
)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.81 0.420
0.41 0.684
a) State the hypothesis in words and mathematical notation. State the alpha reflected in the STATA
output.
:
|| ||
-.9768499
-17.02932
こ
MSE
2,512. 54.
d) Make a decision about your hypothesis and state it fully.
834
[99% Conf. Interval]
1.865555
23.41092
0.0007
-0.0004
47.587
df1 =
df2=
N-K-1=831
Means Squares Regression = 147489013/2 = 737.45
Mean Squares Residual = 2087917.95/831= √2,512.54
Obtained F-value =
MSR
737.45
0.29
0 lat least one of the coefficients in 10)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fd951c126-d634-4f6d-a6e2-77543e6749ca%2Fce72fd73-f421-4138-8027-77dab0893262%2Fkr1al4p_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Q5. This regression analysis on the number of hours spent emailing for work (EMAILHRS) is the
dependent variable and years of education (EDUC) is the independent variable. Dang. Once again, STATA
forgot to report key values (or someone deleted them, hmmm). Using what is left, answer the questions
below.
regress EMAILHR EDUC, level (99)
Source |
Model | 1474.89013
Residual | 2087917.95
Total | 2089392.84
EMAILHR
1.
2.
3.
4.
5.
SS
EDUC | .4443524
cons | 3.190798
H1
Coef.
Ho: Bk.
ко 2
Вк doesn't
BK=0
b) Fill in the missing variables.
df
Std. Err.
5506034
7.833696
MS
2
831
833 2263.69755
737.45
t
Number of obs
F(,
)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.81 0.420
0.41 0.684
a) State the hypothesis in words and mathematical notation. State the alpha reflected in the STATA
output.
:
|| ||
-.9768499
-17.02932
こ
MSE
2,512. 54.
d) Make a decision about your hypothesis and state it fully.
834
[99% Conf. Interval]
1.865555
23.41092
0.0007
-0.0004
47.587
df1 =
df2=
N-K-1=831
Means Squares Regression = 147489013/2 = 737.45
Mean Squares Residual = 2087917.95/831= √2,512.54
Obtained F-value =
MSR
737.45
0.29
0 lat least one of the coefficients in 10)
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