For the test, we will need SSR,SST,and SSE. SSR= S xiyi− (Sx i)(S yi) n 2 S x2 i− (Sx i) 2 n = (355−(90)(50) 10) 2 1086−8100 10 =32.699 SST= P y2 i− (S yi) 2 n = 296−2500 10 =46.0 Recall: SST= SSR+SSE ⇒SSE= SST−SSR=46.0−32.699 = 13.301 Next, we need an estimator for σ. σ≈s= √ MSE= q SSE n−2 = q 13.301 10−2 =1.2894 The Chi-Square test of independence is used to determine if there is a significant relationship between two nominal (categorical) variables. The frequency of each category for one nominal variable is compared across the categories of the second nominal variable. The codes below was done in our regression laboratory class. Here, we run first the data in SPSS, and take the ANOVA output where we can find the computed values of SSR, SSE, and SST.By simple calculation, you can find that SST = SSR + SSE, aka the total variation in observed dependent variables is the sum of variation explained by the regression model and variation unexplained. In the example, SST = (5–14.25)² + (9–14.25)² + (18–14.25)² + (25–14.25)² =242.75.