multiple regression:
DESCRIPTION
Multiple regression:. Yi = B 0 + B 1 X 1 +B 2 X 2 + B 3 X 3 +e where each of the Betas estimate the effect of one independent variable. This allows the regression to "control" for each of the other factors simultaneously - PowerPoint PPT PresentationTRANSCRIPT
Yi = B0 + B1 X1 +B2 X2 + B3 X3 +e
◦ where each of the Betas estimate the effect of one independent variable.
This allows the regression to "control" for each of the other factors simultaneously◦ e.g., control for exercise, eating habits, AND fish
consumption on heart attacks.
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Coefficientsa
56.730 75.172 .755 .455
1.626 .791 .319 2.056 .047
8.E-02 .983 .013 .080 .937
.949 .656 .235 1.446 .156
(Constant)
EXAM_1
EXAM_2
REVIEW_1
Model1
BStd.Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: TOTALa.
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Model
Unstandardized CoefficientsStandardized Coefficients t Sig.
B Std. Error Beta B Std. Error1 (Constant) 1.872 414.389 .005 .997
Quiz1 -1.968 6.790 -.169 -.290 .779
Quiz2 1.210 5.805 .095 .209 .840
Quiz3 -3.165 14.973 -.192 -.211 .838
Quiz4 3.584 8.857 .252 .405 .696
Quiz5 7.511 18.768 .473 .400 .699
Proj1 1.249 5.050 .106 .247 .811
Part1 1.209 19.745 .727 .061 .953
Quiz6 -4.481 13.304 -2.212 -.337 .745
Quiz7 6.552 18.152 3.298 .361 .727
Quiz8 -2.229 8.059 -.947 -.277 .789
Proj2 -.800 3.431 -.091 -.233 .821
Quiz9 -4.854 10.021 -2.292 -.484 .641
Part2 1.778 16.205 1.043 .110 .915
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Model
Unstandardized CoefficientsStandardized Coefficients t Sig.
B Std. Error Beta B Std. Error1 (Constant)
59.229 24.653 2.403 .025
QUIZ
-1.356 .311 -.666 -4.363 .000
REST
.466 .430 .165 1.083 .290
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