Conclusion - Where to go from here?

Modeling with Data in the Tidyverse

Albert Y. Kim

Assistant Professor of Statistical and Data Sciences

R source code for all videos

Available at http://bit.ly/modeling_tidyverse

Other Tidyverse courses

Available here and here

Tidyverse Fundamentals Track

Intermediate Tidyverse Toolbox Track

Refresher: General modeling framework

In general: $y = f(\vec{x}) + \epsilon$
Linear regression models: $y = \beta_0 + \beta_1 \cdot x_1 + \epsilon$

Parallel slopes model

Polynomial model

Tree models

DataCamp courses using other models

Courses with different $f()$ in $y = f(\vec{x}) + \epsilon$:

Refresher: Regression table

# Fit model:
model_score_1 <- lm(score ~ age, data = evals)

# Output regression table:
get_regression_table(model_score_1)

# A tibble: 2 x 7
  term      estimate std_error statistic p_value lower_ci upper_ci
  <chr>        <dbl>     <dbl>     <dbl>   <dbl>    <dbl>    <dbl>
1 intercept    4.46      0.127     35.2    0        4.21     4.71 
2 age         -0.006     0.003     -2.31   0.021   -0.011   -0.001

ModernDive: Online textbook

Uses tidyverse tools: ggplot2 and dplyr
Expands on the regression models from this course
Uses evals and house_prices datasets (and more)
Goal: Statistical inference via data science
Available at ModernDive.com

Good luck!

Modeling with Data in the Tidyverse