Question 4: Bike Data – Prediction (4a) 2 pts – Predict bike…

Question 4: Bike Data – Prediction (4a) 2 pts – Predict bikes for the test set (bike_data_test) using model1. Display the first six predicted values. (4b) 2 pts – Calculate and display the mean squared prediction error (MSPE) for model1. List one limitation of using this metric to evaluate prediction accuracy. (4c) 1 pt – Refit model1 on bike_data_full, and call it model2. Display the summary table for the model. (4c.1) 3 pts – Estimate the 10-fold and leave-one-out cross validation mean prediction squared error (MSPE) for model2. Hint: cv.glm() from the boot package uses MSPE as the default cost function. (4c.2) 1 pt – How do these two MSPEs compare to the model1 MSPE from 4b? Apply your knowledge of cross validation to explain your results.

5.3 Citizenship can be defined as (1)   A.     the…

5.3 Citizenship can be defined as (1)   A.     the status of a person recognized under the law of a country of belonging to thereof.     B.     a legal identification of a person in international law, establishing the person as a subject, a national, of a sovereign state.     C.    the act of finding out who someone is or what something is.     D.    a distinct territorial body or political entity  

Read Data Read the data and answer the questions below. Assu…

Read Data Read the data and answer the questions below. Assume a significance threshold of 0.05 for hypothesis tests unless stated otherwise. # Load relevant libraries (add other libraries here if needed)library(car)library(CombMSC)library(aod)library(bestglm)library(boot)library(corrplot)library(caret)library(glmnet)# Ensure that the sampling type is correctRNGkind(sample.kind=”Rejection”)# Set seed (please do not change for consistency of the results)set.seed(0)###### Read and process the data: Bike data ######## Read databike_data_full = read.csv(“brooklyn_bridge_bike_counts.csv”, header=TRUE)# Convert month and day columns to categorical variablesbike_data_full$month