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Measures of central tendency

In statistical analysis, measures of central tendency help us grasp the center or average of a dataset. In R, we commonly use three main measures: mean, median, and mode. Let’s delve into each.

Mean in R

The mean, or average, is calculated by summing up all values and dividing by the number of observations. In R, you can compute the mean using the mean() function. Here’s a practical example:

# Calculate mean in R
data <- c(10, 15, 20, 25, 30)
mean_value <- mean(data)

Median in R

The median is the middle value of a dataset when it is ordered. In R, you can find the median using the median() function. Consider the following example:

# Calculate median in R
data <- c(10, 15, 20, 25, 30)
median_value <- median(data)

Mode in R

The mode is the value that appears most frequently in a dataset. In R, there isn’t a built-in function for mode, but you can create a custom function to find it. Here’s a simple implementation:

# Calculate mode in R
get_mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]

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

Practical Applications

Understanding central tendency is crucial for making sense of data. Whether you’re analyzing test scores, sales figures, or any other dataset, these measures provide valuable insights into the data’s typical or central value.

Challenges and Considerations

While mean, median, and mode offer valuable insights, it’s essential to consider the nature of your data. Outliers can significantly impact the mean, making it a less robust measure in certain situations. Always evaluate the distribution and characteristics of your dataset before choosing the appropriate measure.