CS计算机代考程序代写 # BS1033 Lecture 1 Analysis Part 2
# BS1033 Lecture 1 Analysis Part 2
# Author: Chris Hansman
# Email: chansman@imperial.ac.uk
# Date : 07/01/20
# Installing Packages
#install.packages(“tidyverse”)
# Loading Libraries
library(tidyverse)
# Loading Ames Data
#Reading Data
ames_training<-read_csv("ames_training.csv")
ames_testing<-read_csv("ames_testing.csv")
#Scatter Plot
ggplot(data = ames_training ) +
geom_point(aes(x = Year.Built, y = log_price))
#Building Simple Linear Model
ames_model_1 <- lm(log_price ~ Year.Built, data=ames_training)
summary(ames_model_1)
#Predicting and Computing Mean Squared Error
log_price_pred1=predict(ames_model_1, newdata=ames_testing)
model_1_mse <- mean((log_price_pred1-ames_testing$log_price)^2)
#Scaling Variables
# Scaling x
ols_basics_scale <- ols_basics %>%
mutate(X_over_12=X/12) %>%
mutate(Y_times_1000=Y*1000)
# Basic Regression
ols_v1<-lm(Y~X, data= ols_basics)
# Scaled X
ols_v2<-lm(Y~X_over_12, data= ols_basics_scale)
# Scaled Y
ols_v3<-lm(Y_times_1000~X, data= ols_basics_scale)
# Summarizing All
summary(ols_v1)
summary(ols_v2)
summary(ols_v3)
