# BS1033 Lecture 1 Analysis Part 2
# Author: Chris Hansman
# Email: chansman@imperial.ac.uk
# Date : 07/01/19
# Installing Packages
#install.packages(“tidyverse”)
# Loading Libraries
library(tidyverse)
#Reading OLS Data
ols_basics<-read_csv("ols_basics.csv")
#Scatter Plot of OLS Example Data
ggplot(data = ols_basics ) +
geom_point(aes(x = X, y = Y)) +
ggsave("ols_scatter.pdf")
#Scatter Plot of OLS Example Data with Regression Line
ggplot(data = ols_basics ) +
geom_point(aes(x = X, y = Y))+
geom_smooth( aes(x = X, y = Y), method='lm',formula=y~x) +
ggsave("ols_scatter_regline.pdf")
# Example Linear Regression
ols_v1<-lm(Y~X, data= ols_basics)
summary(ols_v1)
#Scatter Plots for Non-Linear CEF with Regression Line:
ggplot(data = ols_basics ) +
geom_point(aes(x = X, y = Y_nl)) +
geom_smooth( aes(x = X, y = Y_nl), method='lm',formula=y~x) +
ggsave("ols_scatter_lfit_nl.pdf")
#Reading S and P Data
s_p_price<-read_csv("s_p_price.csv")
s_p_price %>%
summarize(mean(price))
#Conditional Means for IT Sector:
s_p_price %>%
group_by(IT) %>%
summarize(mean_price = sprintf(“%0.3f”,mean(price)))
#Regressions for IT Sector:
ols_it<-lm(price~IT, data= s_p_price)
summary(ols_it)
#Conditional Means for All Sectors:
s_p_price %>%
group_by(sector) %>%
summarize(mean_price = sprintf(“%0.3f”,mean(price)))
#Regressions for All Sectors:
ols_sector<-lm(price~as.factor(sector), data= s_p_price)
summary(ols_sector)