personality_scores
datasetLoad the dataset reprores::personality_scores.
personality_scores <- NULL
Use ggplot2 to visualise the relationship between extraversion (Ex
) on the horizontal axis and neuroticism (Ne
) on the vertical axis.
ggplot()
Run a regression model that predicts neuroticism from extraversion, and store the model object in the variable personality_mod
. End the block by printing out the summary of the model.
personality_mod <- NULL
Make a histogram of the residuals of the model using ggplot2.
residuals <- NULL
Write code to predict the neuroticism score for the minimum, mean, and maximum extraversion scores. Store the vector of predictions in the variable personality_pred
.
personality_pred <- NULL
NOTE: You can knit this file to html to see formatted versions of the equations below (which are enclosed in $
characters); alternatively, if you find it easier, you can hover your mouse pointer over the $
in the code equations to see the formatted versions.
Write code to randomly generate 10 Y values from a simple linear regression model with an intercept of 3 and a slope of -7. Recall the form of the linear model:
\(Y_i = \beta_0 + \beta_1 X_i + e_i\)
The residuals (\(e_i\)s) are drawn from a normal distribution with mean 0 and variance \(\sigma^2 = 4\), and \(X\) is the vector of integer values from 1 to 10. Store the 10 observations in the variable Yi
below. (NOTE: the standard deviation is the square root of the variance, i.e. \(\sigma\); rnorm()
takes the standard deviation, not the variance, as its third argument).
X <- NULL
err <- NULL
Yi <- NULL