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 = β_{0} - β_{1}x_{1 + }β_{2}x_{2 - }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