Why statistical tests validate hypotheses in R Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a t-test on the sample data.

R Programming

result <- t.test([1])

Drag options to blanks, or click blank then click option'

Asample

Bdata$values

Cvalues

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the whole data frame instead of the numeric vector.

The t.test() function requires a numeric vector as input. Using data$values correctly passes the values column from the data frame.

2fill in blank

medium

Complete the code to check if the p-value is less than 0.05.

R Programming

if (result$p.value [1] 0.05) {
  print("Reject null hypothesis")
} else {
  print("Fail to reject null hypothesis")
}

Drag options to blanks, or click blank then click option'

A<

B>

C==

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using > instead of <, which reverses the logic.

We reject the null hypothesis if the p-value is less than 0.05, indicating significant evidence against the null.

3fill in blank

hard

Fix the error in the code to correctly extract the confidence interval lower bound.

R Programming

lower_bound <- result$conf.int[[1]]

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A1

B2

C0

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 as index, which is invalid in R.

The confidence interval is a vector of length 2. The first element (index 1) is the lower bound.

4fill in blank

hard

Fill both blanks to create a subset of data where the group is 'A' and values are greater than 5.

R Programming

subset_data <- data[data$group [1] 'A' & data$values [2] 5, ]

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A==

B>

C<

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using = instead of == for comparison.

Using < instead of > for filtering values.

Use == to check equality for group 'A' and > to filter values greater than 5.

5fill in blank

hard

Fill all three blanks to create a named vector of means for groups 'A' and 'B' where mean is greater than 10.

R Programming

means <- tapply(data$values, data$group, mean)
filtered_means <- means[means [1] 10]
names_filtered <- names(filtered_means)
result <- setNames(filtered_means, [2])
print(result[[3]])

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A>

Bnames_filtered

Cfiltered_means

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > for filtering.

Using wrong variable names for naming or printing.

We filter means greater than 10 using >, then assign names using names_filtered, and print the filtered means.