Testing The Regression Model I

So we have established that we are testing our hypothesis whether giving people more money leads to them liking us more.

Remember how we used lm() and summary() before to see the R squared of our model? Well we're going to use these functions again to find our t-value, and associated p-value. No manual calculations necessary - pretty great, right?

The lm() function takes the format lm(response variable ~ predictor variable) (however of course you would write your own variable names here).

The summary() function takes the object you would like a summary of as its first argument. In this case it's going to be your regression model.

This exercise is part of the course

Inferential Statistics

View Course

Exercise instructions

In your script, assign your regression model to the variable mod1 using the function lm().
In your script, use the summary() function to find the summary of mod1, and assing this to sum1.
Hit 'Submit' and have a look at the output!

Hands-on interactive exercise

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

# Vector containing the amount of money you gave participants
money  <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

# Vector containing the amount the participants liked you
liking <- c(2.2, 2.8, 4.5, 3.1, 8.7, 5.0, 4.5, 8.8, 9.0, 9.2)

# Assign regression model to variable "mod1"
mod1 <-

# Assign the summary of 'mod1' to 'sum1'
sum1 <-

# Print 'sum1'
sum1

Edit and Run Code