Model met evenwijdige hellingen

Laten we nu een "evenwijdige hellingen"-model fitten met de numerieke verklarende/voorspellende variabele log10_size en de categorische, in dit geval binaire, variabele waterfront. De visualisatie die bij dit model hoort, staat hieronder:

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Modelleren met data in de Tidyverse

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Oefeninstructies

Fit een multiple regression van log10_price met log10_size en waterfront als predictors. Denk eraan dat de data frame met deze variabelen house_prices heet.

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Fit model
model_price_4 <- lm(___, 
                    data = ___)

# Get regression table
get_regression_table(model_price_4)

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Deze oefening maakt deel uit van de cursus

Modelleren met data in de Tidyverse

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This chapter will introduce you to some background theory and terminology for modeling, in particular, the general modeling framework, the difference between modeling for explanation and modeling for prediction, and the modeling problem. Furthermore, you'll start performing your first exploratory data analysis, a crucial first step before any formal modeling.

Exercise 1: Background on modeling for explanation Exercise 2: Exploratory visualization of age Exercise 3: Numerical summaries of age Exercise 4: Background on modeling for prediction Exercise 5: Exploratory visualization of house size Exercise 6: Log10 transformation of house size Exercise 7: The modeling problem for explanation Exercise 8: EDA of relationship of teaching & "beauty" scores Exercise 9: Correlation between teaching and "beauty" scores Exercise 10: The modeling problem for prediction Exercise 11: EDA of relationship of house price and waterfront Exercise 12: Predicting house price with waterfront

Equipped with your understanding of the general modeling framework, in this chapter, we'll cover basic linear regression where you'll keep things simple and model the outcome variable y as a function of a single explanatory/ predictor variable x. We'll use both numerical and categorical x variables. The outcome variable of interest in this chapter will be teaching evaluation scores of instructors at the University of Texas, Austin.

Exercise 1: Explaining teaching score with age Exercise 2: Plotting a "best-fitting" regression line Exercise 3: Fitting a regression with a numerical x Exercise 4: Predicting teaching score using age Exercise 5: Making predictions using "beauty score"Exercise 6: Computing fitted/predicted values & residuals Exercise 7: Explaining teaching score with gender Exercise 8: EDA of relationship of score and rank Exercise 9: Fitting a regression with a categorical x Exercise 10: Predicting teaching score using gender Exercise 11: Making predictions using rank Exercise 12: Visualizing the distribution of residuals

In the previous chapter, you learned about basic regression using either a single numerical or a categorical predictor. But why limit ourselves to using only one variable to inform your explanations/predictions? You will now extend basic regression to multiple regression, which allows for incorporation of more than one explanatory or one predictor variable in your models. You'll be modeling house prices using a dataset of houses in the Seattle, WA metropolitan area.

Exercise 1: Huizenprijs uitleggen met bouwjaar en grootte Exercise 2: EDA van relatie Exercise 3: Een regressie fitten Exercise 4: Huizenprijs voorspellen met bouwjaar en grootte Exercise 5: Voorspellen met oppervlakte en slaapkamers Exercise 6: Residuen interpreteren Exercise 7: Huizenprijs verklaren met grootte en staat Exercise 8: Model met evenwijdige hellingen

Huidige oefening

Exercise 9: Het parallelle hellingenmodel interpreteren Exercise 10: Huisprijs voorspellen met grootte en staat Exercise 11: Voorspellingen maken met grootte en waterfront Exercise 12: Voorspellingen automatiseren voor "nieuwe" huizen

In the previous chapters, you fit various models to explain or predict an outcome variable of interest. However, how do we know which models to choose? Model assessment measures allow you to assess how well an explanatory model "fits" a set of data or how accurate a predictive model is. Based on these measures, you'll learn about criteria for determining which models are "best".

Exercise 1: Model selection and assessment Exercise 2: Refresher: sum of squared residuals Exercise 3: Which model to select?Exercise 4: Assessing model fit with R-squared Exercise 5: Computing the R-squared of a model Exercise 6: Comparing the R-squared of two models Exercise 7: Assessing predictions with RMSE Exercise 8: Computing the MSE & RMSE of a model Exercise 9: Comparing the RMSE of two models Exercise 10: Validation set prediction framework Exercise 11: Fitting model to training data Exercise 12: Predicting on test data Exercise 13: Conclusion - Where to go from here?