Linear and Logistic Regression in R|Linear Regression|Logistic Regression

Linear and Logistic Regression in R
Linear Regression
Logistic Regression

Linear Regression
Used to predict the continuous dependent variable using a given set of independent variables.
The outputs produced must be a continuous value, such as price and age.
Logistic Regression
Used to predict the categorical dependent variable using a given set of independent variables.
The outputs produced must be Categorical values such as 0 or 1, Yes or No.

# Generate random IQ values with mean = 30 and sd =2
IQ = rnorm(40, 30, 2)

# Sorting IQ level in ascending order
IQ = sort(IQ)

# Generate vector with pass and fail values of 40 students
result = c(0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 1, 1, 0, 0, 1, 0,
0, 0, 1, 0, 0, 1, 1, 0, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 0, 1)

# Data Frame

# Print data frame
print(df)

# Plotting IQ on x-axis and result on y-axis
plot(IQ, result, xlab = "IQ Level",
ylab = "Probability of Passing")

# Create a linear model
Ln = glm(result~IQ, data= df)
summary(Ln)

# Create a logistic model
Lg= glm(result~IQ, family=binomial, data= df)

# Summary of the regression model
summary(Lg)

# Create a curve based on prediction using the linear model

# Create a curve based on prediction using the regression model

plot(lgp)

or 1, Yes or No.