Basic Regression Models in R

Regression analysis helps understand relationships between variables. In R, you can create simple linear regression models to predict a dependent variable based on one or more independent variables.

Simple Linear Regression

# Sample data
height <- c(150, 160, 170, 180, 190)
weight <- c(50, 60, 65, 75, 80)

# Create linear model
model <- lm(weight ~ height)

# View model summary
summary(model)

• Linear regression model (lm): This function fits a straight line through the data that best explains the relationship between the independent variable (height) and the dependent variable (weight).
• The model finds the intercept (the weight when height = 0) and the slope (how much weight changes, on average, for each unit increase in height).
• summary(model) shows the estimated equation, statistical significance of the relationship, and how well the model fits the data (R-squared value).

Making Predictions

# Predict weight for a new height
predict(model, data.frame(height = 175))

• Prediction with predict(): Once the line of best fit is created, you can use it to estimate new values.
• In this example, when height = 175, the model plugs 175 into the regression equation (slope × 175 + intercept) to predict the expected weight.
• This allows you to estimate outcomes for new data points based on the trend captured in your dataset.

Regression is a powerful tool for exploring relationships and making predictions based on data trends.