R is widely used for statistical analysis. You can calculate summary statistics, perform hypothesis tests, and create models to analyze data.
Summary Statistics
# Sample data
scores <- c(85, 90, 78, 92, 88)
# Mean, median, and standard deviation
mean(scores)
median(scores)
sd(scores)
Mean: The arithmetic average of the values. In this case, it gives the average test score across all students.
Median: The middle value when the data is ordered. It shows the “typical” score and is less sensitive to outliers than the mean.
Standard Deviation (sd): A measure of how spread out the scores are around the mean. A small standard deviation means scores are close to the average, while a larger one means more variability.
Basic Hypothesis Testing
# One-sample t-test
t.test(scores, mu = 80)
t.test(): This performs a one-sample t-test, which compares the average of your data (scores) against a hypothesized mean (in this case, 80).
Null hypothesis (H₀): The true mean score is 80.
Alternative hypothesis (H₁): The true mean score is different from 80. The test returns a t statistic, p-value, and a confidence interval to help determine whether the observed average is significantly different from 80.
R makes it easy to explore data distributions, relationships, and test hypotheses for informed decision-making.