The t distribution (2)

In the last exercise we calculated the critical value using the qt() function. However, we still do not know our t test statistics and whether this statistics is larger than the cut-off value. Let's calculate the t value in this exercise and see which p value is associated with it. The formula for the standard error is the following:

$$\sigma / \sqrt(n)$$

The formula for the t value is the same as the formula for the Z value: $$t = \frac{\bar{x} - \mu}{se}$$

This exercise is part of the course

Basic Statistics

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Exercise instructions

Using our example where we had a sample of 50 males with a mean height of 186.5 and a population standard deviation of 5 and population mean of 185, calculate the associated standard error, round this value to two digits and store it in the variable se.
Calculate the associated t value, round it to two digits and store it in the variable t_value. Remember to use the same formula as when calculating a z score.
Using the pt() function with lower.tail = FALSE, calculate the associated p value, round it to two digits and store it in a variable called p_value. Remember that we are doing a one-sided test.
print the variable p_value to the console.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# calculate the standard error and store it in the variable se


# calculate the t value and store it in a variable called t_value


# calculate the p value and store it in a variable called p_value


# print p_value to the console

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