Publications by Sai Dheeraj
lab8
Complete the following exercises from Introduction to Statistical Learning Chapter 7: (6), (7), (9), and (11) Note: For problem 6, the hypothesis testing anova referring to the process used on page 285 of the book, where the function is used with argument set to âFâ. This is the usual statistical inference manner in which degree would be se...
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lab7
Complete the following exercises from Introduction to Statistical Learning Chapter 6: (8) and (9) ## a) set.seed(1) X = rnorm(100) epsilon = rnorm(100) b) b0 = 2 b1 = 5 b2 = 1 b3 = 3 Y = b0 + b1 * X + b2 * X^2 + b3 * X^3 + epsilon c) library(leaps) ## Warning: package 'leaps' was built under R version 4.3.3 full_data_set = data.frame(Y, X)...
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lab7
Complete the following exercises from Introduction to Statistical Learning Chapter 6: (8) and (9) ## a) set.seed(1) X = rnorm(100) epsilon = rnorm(100) b) b0 = 2 b1 = 5 b2 = 1 b3 = 3 Y = b0 + b1 * X + b2 * X^2 + b3 * X^3 + epsilon c) library(leaps) ## Warning: package 'leaps' was built under R version 4.3.3 full_data_set = data.frame(Y, X)...
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Lab6
Complete the following exercises from Introduction to Statistical Learning Chapter 5: (5), (6), (7), and (9) Lab 6 importing libraries library(ISLR) ## Warning: package 'ISLR' was built under R version 4.3.2 library(ggplot2) ## Warning: package 'ggplot2' was built under R version 4.3.2 library(dplyr) ## ## Attaching package: 'dplyr' ## The fol...
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lab 4
Question 8 library(ISLR) ## Warning: package 'ISLR' was built under R version 4.3.2 model <- lm(mpg ~ horsepower, data=Auto) summary(model) ## ## Call: ## lm(formula = mpg ~ horsepower, data = Auto) ## ## Residuals: ## Min 1Q Median 3Q Max ## -13.5710 -3.2592 -0.3435 2.7630 16.9240 ## ## Coefficients: ##...
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Da Assignment 1
Loading data # Reading the data auto_data <- read.csv("C:/Users/dell/Downloads/Auto.csv") a) Which of the predictors are quantitative, and which are qualitative? # Function to classify predictors classify_predictors <- function(df) { quantitative <- character() qualitative <- character() for (col_name in names(df)) { if (class(df...
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Project writeup
R Markdown data <- read.csv('C:/Users/dell/Downloads/Cleaned_Ball_By_Ball.csv') summary(data) ## MatcH_id Over_id Ball_id Innings_No ## Min. : 335987 Min. : 1.00 Min. :1.000 Min. :1.000 ## 1st Qu.: 419154 1st Qu.: 5.00 1st Qu.:2.000 1st Qu.:1.000 ## Median : 548382 Median :10.00 Medi...
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week 13
Tasks To Be Performed Selecting a week to critique Analyzing issues Providing solutions The week we have selected is Week 7 Group Members - Prerana, Sharmitha Yazhini, Sai Dhanush Doddapaneni, Chaitanya, Sai Dheeraj Kanaparthi Week 7 critiques Loading Libraries and Data Critique 1 : No information on how to select a proper alpha level and po...
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week 12 datadive
my_data <- read.csv('C:/Users/dell/Downloads/Cleaned_Ball_By_Ball.csv') # Convert MatchDateSK from an integer in the format YYYYMMDD to a Date type my_data$MatchDateSK <- as.Date(as.character(my_data$MatchDateSK), format="%Y%m%d") # Check the structure to confirm the change str(my_data) ## 'data.frame': 150451 obs. of 46 variables: ## $ M...
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datadive_week11
R Markdown T my_data <- read.csv('C:/Users/dell/Downloads/Cleaned_Ball_By_Ball.csv') Including Plots # Convert categorical variables to factors my_data$Team_Batting <- as.factor(my_data$Team_Batting) my_data$Team_Bowling <- as.factor(my_data$Team_Bowling) my_data$Extra_Type <- as.factor(my_data$Extra_Type) # Building the linear model # Let's...
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