This is R programming - stat/machine learning question. My codeshould give me the mean and standard deviation of the RSME but onlymy mean is correct, my sd of 0.1180 or 0.12 appears to beincorrect. Thank you for helping.

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Create a data set using the following code: set.seed (1) n < 100 Sigma <- 9*matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2) dat <- MASS::mvrnorm(n = 100, c(69, 69), Sigma) %>% data.frame() %>% setNames (c("x", "y")) We will build 100 linear models using the data above and calculate the mean and standard deviation of the combined models. First, set the seed to 1. Within a replicate loop, (1) partition the dataset into test and training sets of equal size using datsy to generate your indices, (2) train a linear model predicting y from x, (3) generate predictions on the test set, and (4) calculate the RMSE of that model. Then, report the mean and standard deviation of the RMSEs from all 100 models. Mean: 2.48 2.48 SD: 0.118 0.118 120 121 f <-function(data) { 122- 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 rmse <- replicate (100, { 142 143 144 }) 138 } 139 set.seed (1) 140 141 test_index <- createDataPartition (datasy, list = FALSE) train set <- data [-test_index,] data[test_index,] test set < ## Train a linear model fit - 1m(yx, data = train_set) y_hat <- predict (fit, test_set) ## Squared loss... sqrt (mean((y_hat - test_setsy) ^ 2)) structure(c(mean (rmse), sd(rmse)), names = c('mean', 'sd')) n - 100 Sigma < 9 matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2) dat < MASS::mvrnorm (n = 100, c(69, 69), Sigma) %>% data.frame() %>% setNames (c("x", "y")) 145 results < f (dat) 146 results 147 148 Create a data set using the following code: set.seed (1) n < 100 Sigma <- 9*matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2) dat <- MASS::mvrnorm(n = 100, c(69, 69), Sigma) %>% data.frame() %>% setNames (c("x", "y")) We will build 100 linear models using the data above and calculate the mean and standard deviation of the combined models. First, set the seed to 1. Within a replicate loop, (1) partition the dataset into test and training sets of equal size using datsy to generate your indices, (2) train a linear model predicting y from x, (3) generate predictions on the test set, and (4) calculate the RMSE of that model. Then, report the mean and standard deviation of the RMSEs from all 100 models. Mean: 2.48 2.48 SD: 0.118 0.118 120 121 f <-function(data) { 122- 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 rmse <- replicate (100, { 142 143 144 }) 138 } 139 set.seed (1) 140 141 test_index <- createDataPartition (datasy, list = FALSE) train set <- data [-test_index,] data[test_index,] test set < ## Train a linear model fit - 1m(yx, data = train_set) y_hat <- predict (fit, test_set) ## Squared loss... sqrt (mean((y_hat - test_setsy) ^ 2)) structure(c(mean (rmse), sd(rmse)), names = c('mean', 'sd')) n - 100 Sigma < 9 matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2) dat < MASS::mvrnorm (n = 100, c(69, 69), Sigma) %>% data.frame() %>% setNames (c("x", "y")) 145 results < f (dat) 146 results 147 148

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