Question 1. This question is the continuation of the data generation we did in class. We generate data set with two explanatory variables x1 and x2 from multinomial Normal distribution with covariance matrix = c(1, .2, .2, 9) in two classes with mean for Class 0 is (3,7) and Class 1 is (6,10). For Class 0, we generate 20 observations and for Class 1, 10 observations. While generating this data, use the set.seed("6559"). Find the linear discriminant function weights. Plot the data with two colors and draw the discriminant function for classification. Also plot the 4 test data (3.68, 5.65), (3.28, 5.20), (3.57, 8.82), (4.64, 7.98) and predict the test data. Use the R program also to predict the test data.

I have generated the data now I need the codes for how :

draw the discriminant function for classification. Also plot the 4 test data (3.68, 5.65), (3.28, 5.20), (3.57, 8.82), (4.64, 7.98) and predict the test data. Use the R program also to predict the test data.