Calculating Various Statistical Measures using R

R provides a comprehensive set of functions for calculating various statistical measures.

Measures of Central Tendency:

Mean: The average of a dataset.

    data <- c(10, 15, 20, 25, 30)
    mean_value <- mean(data)
    print(mean_value)

Median: The middle value of a dataset when ordered.

    data <- c(10, 15, 20, 25, 30)
    median_value <- median(data)
    print(median_value)

Mode: The most frequently occurring value. R does not have a built-in mode() function, but it can be calculated using a custom function or packages like DescTools.

Measures of Variability (Dispersion):

Standard Deviation: Measures the spread of data points around the mean.

    data <- c(10, 15, 20, 25, 30)
    std_dev <- sd(data)
    print(std_dev)

Variance: The square of the standard deviation.

    data <- c(10, 15, 20, 25, 30)
    variance_value <- var(data)
    print(variance_value)

Range: The difference between the maximum and minimum values.

    data <- c(10, 15, 20, 25, 30)
    range_value <- range(data) # Returns a vector with min and max
    print(range_value)

Other Descriptive Statistics:

Quantiles (Percentiles): Divide the data into equal parts.

    data <- c(10, 15, 20, 25, 30)
    quartiles <- quantile(data, probs = c(0.25, 0.5, 0.75))
    print(quartiles)

Summary Statistics: The summary() function provides a quick overview of key descriptive statistics for a vector or data frame, including min, max, mean, median, and quartiles.

    data <- c(10, 15, 20, 25, 30)
    summary(data)