3. Consider the dataset in Table 2. Without using any computer software, calculate the 2-fold cross-validation mean square error (MSE) for a regular ordinary least squares (OLS) linear regression with- out intercept of the response yi on the predictor Xi. Table 2: Dataset for #3 Yi 1 2 3 4 5 6 5 2 8 8 5 5 4 6 2 0 4 2 2 5 X i Use the following split of the dataset observations: subset 1 contains observations {1,2,3} and subset 2 contains observations {4,5,6}. NOTE: recall that for a simple linear regression without intercept of yi on the predictor Xi when the sample size is n, the parameter estimate is 21-1 yili = n i=1 n m i=1 21- x 3. Consider the dataset in Table 2. Without using any computer software, calculate the 2-fold cross-validation mean square error (MSE) for a regular ordinary least squares (OLS) linear regression with- out intercept of the response yi on the predictor Xi. Table 2: Dataset for #3 Yi 1 2 3 4 5 6 5 2 8 8 5 5 4 6 2 0 4 2 2 5 X i Use the following split of the dataset observations: subset 1 contains observations {1,2,3} and subset 2 contains observations {4,5,6}. NOTE: recall that for a simple linear regression without intercept of yi on the predictor Xi when the sample size is n, the parameter estimate is 21-1 yili = n i=1 n m i=1 21- x