5 slides powerpoint. Integrate the data analysis I have in h

5 slides powerpoint. Integrate the data analysis I have in h.
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 plotsinstall.packages( PerformanceAnalytics , repos = )library(PerformanceAnalytics)# to help visualize correlation in colorinstall.packages( corrplot , repos = )library(corrplot)#See some Descriptive statistics about our Basketball datasetsummary(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 dataBasketball.num = sapply(Basketball, is.numeric) # label TRUE FALSE for numerical variablesnum = 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$Pos== PF ,4,5)))) `{r}# first load package caTools library(caTools)# based on probability 70% training data / 30% test data split.# We create an item variable called indicator , where indicator = TRUE takes up 70% of dataindicator = sample.split(Basketball, SplitRatio = 0.7)# Extract out the data based on whether indicator variable is TRUE or FALSEtesting = Basketball[!indicator,] # getting 30% of the data as testingtraining = Basketball[indicator,] # getting 70% of the data as training# Attach training data firstattach(training)# To build a linear regression model, give this model a name linear :linear = lm(FTP~ Post + FGP + `3PP` + AST + TRB+ TOV+BLK+`PS/G`+ MP)# To see the result of model:summary(linear)plot(linear)# To predict the gross of data from testing dataset using the linear model we builttesting$linear_prediction = predict(linear, newdata = testing)# To see the accuracy of prediction:accuracy = testing$linear_prediction testing$FTPpercent = accuracy/testing$FTPmean(accuracy,na.rm = TRUE) # to see how much percentage away from the actual 5 slides powerpoint. Integrate the data analysis I have in h

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