Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Obs. Intercept Xx HN Part I: Regression Basics SUMMARY OUTPUT A 1 2 3 Regression Statistics st 4 X556 8 0.97688814 df 0.939080583 3.277208147 5 1 3 4 Data Y 23 16 25 28 SS 672.9797 32.22028 Y hat Standard Error Coefficients t Stat P-value 2.531468531 3.604616 0.702285 0.533087 3.43006993 0.433317 7.915839 0.004203

MATLAB: An Introduction with Applications
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Part I: Regression Basics
SUMMARY OUTPUT A
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Obs.
1
2
Regression Statistics
Intercept
X
3
4
X5560 00
8
0.97688814
df
0.939080583
3.277208147
5
1
3
4
Data
Y
23
16
25
28
SS
672.9797
32.22028
Y hat
Standard
Error
Coefficients
t Stat P-value
2.531468531 3.604616 0.702285 0.533087
3.43006993 0.433317 7.915839 0.004203
Transcribed Image Text:Part I: Regression Basics SUMMARY OUTPUT A Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Obs. 1 2 Regression Statistics Intercept X 3 4 X5560 00 8 0.97688814 df 0.939080583 3.277208147 5 1 3 4 Data Y 23 16 25 28 SS 672.9797 32.22028 Y hat Standard Error Coefficients t Stat P-value 2.531468531 3.604616 0.702285 0.533087 3.43006993 0.433317 7.915839 0.004203
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Data given

Coefficient of intercept=2.531468531 

Coefficient of X =3.43006993 

 

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