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5 slides powerpoint. Integrate the data analysis I have in hand using RStudio and excel into powerpoint slides. We analyze the freethrow percentage in NBA with few model to determine corelation and other models. Please put all the things we have into a few slides based on the instruction I will attach.

If you know how to use Rstudio this should be a 10 min work for you.

setwd(“C:/Users/kaziz/Desktop”)
library(readr)
Basketball <- read_csv(“C:/Users/kaziz/Desktop/MKTG 480N Basketball analytics.csv”)

# to help generate correlation plots
install.packages(“PerformanceAnalytics”, repos = “http://cran.us.r-project.org)

library(PerformanceAnalytics)

# to help visualize correlation in color
install.packages(“corrplot”, repos = “http://cran.us.r-project.org)

library(corrplot)

#See some Descriptive statistics about our Basketball dataset
summary(Basketball)

attach(Basketball)
plot(Basketball\$FTP,Basketball\$AST)

“`{r, results=’hide’}
# Using the function chart.Correlation from “PerformanceAnalytics” package,
# we can create a correlation matrix easily, much easier than built in functions

# However, before that, we need to pick out the numerical variables
# because we cannot run correlation matrix with categorical data or missing data
Basketball.num = sapply(Basketball, is.numeric) # label TRUE FALSE for numerical variables
num = Basketball[,Basketball.num] # selecting only numerical variables
“`

chart.Correlation(num)

correlation = cor(num, use = “complete.obs”)
corrplot(correlation, type=”upper”)

Basketball\$Post= ifelse(Basketball\$Pos==”PG”,1,ifelse(Basketball\$Pos==”SG”,2,ifelse(Basketball\$Pos==”SF”,3,ifelse(Basketball\$Pos==”PF”,4,5))))

“`{r}
# first load package “caTools”
library(caTools)

# based on probability 70% training data / 30% test data split.
# We create an item variable called “indicator”, where indicator = TRUE takes up 70% of data
indicator = sample.split(Basketball, SplitRatio = 0.7)

# Extract out the data based on whether indicator variable is TRUE or FALSE
testing = Basketball[!indicator,] # getting 30% of the data as testing
training = Basketball[indicator,] # getting 70% of the data as training

# Attach training data first
attach(training)

# To build a linear regression model, give this model a name “linear”:
linear = lm(FTP~ Post + FGP + `3PP` + AST + TRB+ TOV+BLK+`PS/G`+ MP)

# To see the result of model:
summary(linear)

plot(linear)

# To predict the gross of data from testing dataset using the linear model we built
testing\$linear_prediction = predict(linear, newdata = testing)

# To see the accuracy of prediction:
accuracy = testing\$linear_prediction – testing\$FTP
percent = accuracy/testing\$FTP
mean(accuracy,na.rm = TRUE) # to see how much percentage away from the actual