Statistical Analysis Commands in R

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Questions and Answers

What is the purpose of the 'chisq.test(a$target,a$sex)' function in the provided code snippet?

To calculate the mean value of the target variable for different genders.
To test the relationship between the target variable and gender. (correct)
To create a scatter plot between the target variable and gender.
To impute missing values in the 'sex' column.

What does the 'anov()' function with 'target~(resting.bp.s)' as an argument do?

Calculates the correlation coefficient between the target variable and resting blood pressure.
Filters out observations in the dataset based on resting blood pressure values.
Creates a new dataset named 'resting.bp.s'.
Performs analysis of variance on the target variable and resting blood pressure data. (correct)

In the given code, what is the purpose of 'ifelse(testset$predicted_probability>0.50,1,0)'?

Setting all predicted probabilities to 0.50 in the test set.
Classifying test data based on predicted probabilities. (correct)
Calculating the mean probability for the target variable greater than 0.50.
Calculating the median of predicted probabilities.

What information does the 'table(testset$target,testset$binary)' provide?

It displays a contingency table of observed and predicted target values. (A) Signup and view all the answers

Why is 'sample.split(a$target,SplitRatio = 0.80)' used in the code?

To split the dataset into training and testing sets for model evaluation. (D) Signup and view all the answers

What does 'model=randomForest(target~.,data=trainingset)' achieve in the code snippet?

Fits a random forest model predicting the target variable using all other variables. (A) Signup and view all the answers

What does 'sum(is.na(a))' in the code snippet do?

Calculates the sum of logical NA values in the dataframe a (B) Signup and view all the answers

What is the purpose of 'chisq.test(a$target,a$chest.pain.type)' in the code snippet?

Conducts a chi-squared test between target and chest pain type (C) Signup and view all the answers

What does 'testset$predicted_probability>0.50' determine in the code snippet?

Predicted probability greater than 0.50 in testset (A) Signup and view all the answers

What information does 'table(a$resting.ecg)' provide in the code snippet?

Creates a contingency table for resting ECG values (B) Signup and view all the answers

What is the purpose of 'anova=aov(target~(age),data=a)' in the code snippet?

Conducts an analysis of variance (ANOVA) for target with respect to age (D) Signup and view all the answers

What does 'model=randomForest(target~,.,data=trainingset)' achieve in the code snippet?

Fits a random forest model with all predictors to predict the target variable (C) Signup and view all the answers

Study Notes

R Script Steps

Set working directory to C:/Users/nishitha1/Desktop/231BCADA23
Read CSV file newppt.csv into a with na.strings set to empty string
Calculate sum of NA values in a using sum(is.na(a))
Create frequency table for a$resting.ecg using table(a$resting.ecg)

Handling Missing Values

Replace NA values in a$resting.ecg with 0

ANOVA Models

Create ANOVA model for target vs age using aov function
Summarize ANOVA model using summary function
Create ANOVA models for target vs resting.bp.s, cholesterol, max.heart.rate, and oldpeak

Chi-Square Tests

Perform chi-square tests for target vs sex, chest.pain.type, fasting.blood.sugar, resting.ecg, exercise.angina, and ST.slope

Random Forest Model

Split data into training set (80%) and test set (20%) using sample.split function
Create random forest model using randomForest function with target as response variable
Summarize random forest model using summary function
Predict probabilities for test set using predict function
Create binary predictions using ifelse function

Confusion Matrix

Create confusion matrix for test set using table function

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Description

Learn how to perform statistical analysis using R commands such as reading CSV files, handling missing values, conducting ANOVA tests, and performing chi-squared tests. Explore different variables like age, resting blood pressure, cholesterol levels, and heart rate in a dataset.