Thius excel worksheet was based on specific independent and dependent variables. Think of another independent variable that you think would be a better predictor of the dependent variable. Give the name of that variable and explain why you think this would be a better predictor than the current independent variable.
Thius excel worksheet was based on specific independent and dependent variables. Think of another independent variable that you think would be a better predictor of the dependent variable. Give the name of that variable and explain why you think this would be a better predictor than the current independent variable.
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Thius excel worksheet was based on specific independent and dependent variables. Think of another independent variable that you think would be a better predictor of the dependent variable. Give the name of that variable and explain why you think this would be a better predictor than the current independent variable.

Transcribed Image Text:35
30
25
20
15
10
5
0
At Bats
51
67
77
44
55
39
45
0
10
SUMMARY OUTPUT
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
20
30
Regression Statistics
Hits
19
25
30
20
23
16
18
Hits
40
SLOPE
0.340215
y = 0.3402x + 3.1998..
60
70
80
YIntercept
3.199795
90
Correlation
Regression
0.967625
0.954306
Field: At Bats and Field: Hits appear highly correlated.
35
30
25
20
15
10
5
0
20
30
40
50
60
70
80
Hits
At Bats
50
0.976885985
0.954306228
0.942882786
1.218625281
6
df
SS
MS
F
ignificance F
1 124.0598 124.0598 83.53928 0.000795
4
5.94019 1.485048
5
130
Coefficients andard Erro t Stat P-value Lower 95% Upper 95% ower 95.0%Jpper 95.0%
3.726325329 2.06028 1.80865 0.144774 -1.99393 9.446581 -1.99393 9.446581
0.335296783 0.036685 9.139983 0.000795 0.233444 0.43715 0.233444 0.43715
51
0
10
90

Transcribed Image Text:ANOVA
Regression
Residual
Total
Intercept
RESIDUAL OUTPUT
Observation
51
1
2
3
4
5
6
df
SS
MS
F
ignificance F
1 124.0598 124.0598 83.53928 0.000795
4
5.94019 1.485048
5
130
Coefficients
andard Erro t Stat P-value Lower 95% Upper 95% ower 95.0%Jpper 95.0%
3.726325329 2.06028 1.80865 0.144774 -1.99393 9.446581 -1.99393 9.446581
0.335296783 0.036685 9.139983 0.000795 0.233444 0.43715 0.233444 0.43715
Predicted 19
Residuals
26.19120979 -1.19121
29.54417762 0.455822
18.47938378 1.520616
22.16764839 0.832352
16.80289986 -0.8029
18.81468056 -0.81468
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