Got it, let's wrap this up smoothly—you've already nailed the setup with your data matrix and row means, so we just need to add the variances and assemble the final table. Here's how to do it step by step:

Step 1: Compute Row-Wise Sample Variances #

R's default var() function calculates variances column-wise, so we'll use apply() to target each row instead. This gives us the standard sample variance (divided by (n-1), which is what you want for sample statistics) for each of your 30 samples:

# Your existing code to set up data and means
lab6data <- c(2,5,4,6,7,8,4,5,9,7,3,4,7,12,4,10,9,7,8,11,8, 6,13,9,6,7,4,5,2,3,10,13,4,12,9,6,7,3,4,2)
observations <- matrix(lab6data, 30, 5)
means <- rowMeans(observations)

# Calculate sample variance for each row
sample_vars <- apply(observations, MARGIN = 1, FUN = var)

Step 2: Assemble the Final Table #

Now we'll combine sample IDs, means, and variances into a clean data frame that displays as a structured table:

# Create the summary table data frame
sample_summary <- data.frame(
  sample = 1:30,  # Number each sample from 1 to 30
  mean = round(means, 2),  # Round to 2 decimals for readability
  variance = round(sample_vars, 2)
)

# View the full table (use head(sample_summary) to see just the first few rows)
sample_summary

Example Preview (First 5 Rows) #

If you run head(sample_summary), you'll get output like this:

Quick Tips #

• The apply() function with MARGIN = 1 tells R to run the var() function on every row of your matrix.
• Remove the round() calls if you need full precision for your results.
• You can easily export this table to a CSV file later with write.csv(sample_summary, "lab6_sample_summary.csv") if you need to share or analyze it outside R.

内容的提问来源于stack exchange，提问作者Sadie Kelsey