Get the data.
Second step involves arranging the X1 values from lowest to highest order. Then, divide the series into two groups, n1 and n2 and remember n1, and n2 must less than N/2. n1,n2 being the size of first and second group respectively and N is the size of the original group. Here, N= 30, n1 and n2 = 13.
Run two regression using the values from the two groups. Check for their Residual Sum of Squares (RSS) and find out the ratios between them.
The ratio = 1328.9651/ 353.411 = 3.760
Compare the calculated F-Statistics(F-Cal) with critical F-value(F-Crit) with the degrees of freedom and significance level.
Step 5: Decision Rules
If F-Cal > F-Crit,
If F-Cal < F-Crit
This calculated value is larger than the critical F – value at 11 degrees of freedom with a 5 % level of significance. Reject the null hypothesis.
Run regression using the above model. Get the residuals and squared residuals.
Run an auxiliary regression using the following model:
Find the R-square from this auxiliary regression. Then, use it to compare with the following formula.
If Chi-Cal < Chi-Crit
If Chi-Cal > Chi-Crit
If the Chi-Cal value is greater than the Chi-Crit value then reject the null hypothesis.
Both used for testing heteroscedasticity.
For G-Q test, its necessary to rearrange the X values and know the X having the most impact on the dependent variable. While its not necessary for white test. Assumption of normality is necessary for both.