Basic Statistical Methods for Data Analysis
Statistical analysis helps you summarize data, test hypotheses, and build models. R is designed for statistics, so it has powerful built-in tools for descriptive and inferential analysis.
In this tutorial, you will learn how to:
Compute descriptive statistics
Analyze relationships between variables
Perform hypothesis testing
Build simple statistical models
1. Descriptive Statistics
Start by summarizing your data:
summary(data) mean(data$income, na.rm = TRUE) median(data$income, na.rm = TRUE) sd(data$income, na.rm = TRUE)
2. Frequency Tables
table(data$gender) prop.table(table(data$gender))
3. Correlation Analysis
cor(data$age, data$income, use = "complete.obs")
Visualize correlation:
plot(data$age, data$income)
4. Hypothesis Testing
One-sample t-test
t.test(data$income, mu = 5000)
Two-sample t-test
t.test(income ~ gender, data = data)
ANOVA
anova_result <- aov(income ~ city, data = data) summary(anova_result)
5. Linear Regression
model <- lm(income ~ age + education, data = data) summary(model)
6. Model Diagnostics
par(mfrow=c(2,2)) plot(model)
7. Confidence Intervals
confint(model)
Conclusion
R makes statistical analysis accurate, reproducible, flexible.