Descriptive_Statistics.md
Descriptive statistics summarize and organize the features of a dataset, providing simple summaries about the sample and the measures.
1. Types of Descriptive Statistics
a. Measures of Central Tendency
Mean: Average of all values
Median: Middle value when data is ordered
Mode: Most frequently occurring value
b. Measures of Dispersion
Range: Difference between maximum and minimum values
Variance: Average of squared deviations from the mean
Standard Deviation (SD): Square root of variance
Interquartile Range (IQR): Range between the 25th and 75th percentiles
c. Shape of the Distribution
Skewness: Measure of asymmetry
Kurtosis: Measure of peakedness
2. Frequency Distribution
Organizes data into classes or intervals
Commonly visualized with histograms, bar charts, or frequency tables
3. Visualizing Descriptive Statistics
Histogram: Displays distribution of continuous variables
Boxplot: Shows median, quartiles, and outliers
Bar Chart: Visualizes categorical variable counts
# Example boxplot in R
boxplot(data$height)
# Histogram
hist(data$weight)4. Summary Statistics in R
# Basic summary
summary(data)
# Specific statistics
mean(data$score)
sd(data$score)
quantile(data$score, probs = c(0.25, 0.75))5. Summary
Descriptive statistics provide foundational insights into the data before applying more complex analyses. They are essential for understanding data distribution, detecting outliers, and informing appropriate statistical methods.
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