Regression, Applied Statistics

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The 4 assumptions of regression:

1.       Variables are normally distributed

2.       Linear relationship between the independent and dependent variables

3.       Homoscedasticity

4.       Variables are measured without error

Multiple Regression Model

Y = β0 - β1x1 + β2x2 - β3x3 + ε (error)

The regression equation is

wfood = 0.378 - 0.00120 totexp - 0.000076 income + 0.00167 age + 0.0295 nk

The above is a Multiple Regression Equation which indicates that there are two or more variables involved. Y in this case is known as wfood which is the dependant variable; however x is known as totexp, income, nk, age which are the independent variables.  In the regression equation the coefficient is the slope of line and standard error coefficient informs how far off the coefficient is from its actual figure.

Predictor         Coef           SE Coef          T           P       VIF

Constant      0.37794        0.01369        27.61  0.000

totexp        -0.00119745   0.00006058  -19.77 0.000  1.272

income       -0.00007625   0.00004302   -1.77  0.077  1.282

age             0.0016660     0.0003076     5.42    0.000  1.062

nk               0.029515       0.004765       6.19    0.000  1.005


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