LAB 4E: This Model Is Big Enough for All of Us

Lab 4E - This model is big enough for all of us!

Directions: Follow along with the slides and answer the questions in bold font in your journal.

So far, in the labs, we've learned how to make predictions using the line of best fit

– Which we also call linear models or regression models.
We've also learned how to measure our model's prediction accuracy by cross-validation.
In this lab, we'll investigate the following question:

Will including more variables in our model improve its predictions?

Start by loading the movie data and split it into two sets (See Lab 4C for help). Remember to use set.seed.

– A set named training that includes 75% of the data.

– A set named testing that includes the remaining 25%.
Create a linear model, using the training data, that predicts gross using runtime.

– Compute the MSE of the model by making predictions for the testing data.
Do you think that a movie's runtime is the only factor that goes into how much a movie will make? What else might affect a movie's gross?

Data scientists often find that including more relevant information in their models leads to better predictions.

– Fill in the blanks below to predict gross using runtime and reviews_num.

lm(_ ~ _ + ____, data = training)
Does this new model make more or less accurate predictions? Describe the process you used to arrive at your conclusion.
Write down the code you would use to include a 3rd variable, of your choosing, in your lm().

Write down which other variables in the movie data you think would help you make better predictions.

– Are there any variables that you think would not improve our predictions?
Create a model for all of the variables you think are relevant.

– Assess whether your model makes more accurate predictions for the testing
data than the model that included only runtime and reviews_num
With your neighbors, determine which combination of variables leads to the best predictions for the testing data.