| X1 | X2 | Y | |
| X1 | 1.000 | 0.454 | 0.585 |
| X2 | 0.454 | 1.000 | 0.342 |
| Y | 0.585 | 0.342 | 1.000 |
n=29
Calculated the adjusted R2
| Correlation Matrix | |||
| X1 | X2 | Y | |
| X1 | 1.00 | 0.45 | 0.59 |
| X2 | 0.45 | 1.00 | 0.34 |
| Y | 0.59 | 0.34 | 1.00 |
rx1 = correlation between Y and X1 = 0.59
rx2 = correlation between Y and X2 = 0.34
r12 = correlation between X2 and X1 = 0.45
R2 is given by, R2 = ( rx12 + rx22 - 2rx1rx2r12 ) / ( 1 - r122 )
= (0.592 + 0.342 - 2 *0.59*0.34*0.45) / ( 1 - 0.452 )
= 0.2775/ 0.7938 = 0.35
n = no of observations = 29
k = no of independents used = 2
Adjusted R2 = 1 - (1 - R2 )*[(n-1)/(n-(k+1))]
= 1 - (1 - 0.35)*[(29-1)/(29-(2+1))]
= 1 - (1 - 0.35)*[28/26]
= 1 - 0.7 = 0.3
X1 X2 Y X1 1.000 0.454 0.585 X2 0.454 1.000 0.342 Y 0.585 0.342 1.000 n=29...
Multiple regressions question
A multiple regression of y on x1, and x2 produces the following results: 4 +0.4x1 +0.9x2, R2-8/60 e'e= 520 n= 29 We also know that 29 0 0 XX 0 50 10 0 10 80 Test the hypothesis that the two slopes sum to 1.
A multiple regression of y on x1, and x2 produces the following results: 4 +0.4x1 +0.9x2, R2-8/60 e'e= 520 n= 29 We also know that 29 0 0 XX 0 50 10...
X1,X2 ~ N(0,1) Y1=X1/X2
show g(y)
1 (y) =- 0 <y < 20 (1+y)
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