1. Connecting multiple independent variables
2. How to build statistical (regression) models that allow us to predict things?
3. What is a regression primer?
4. How to fit a model?
5. How to assess the model fit?
6. What can you do with the model?
7. What can you NOT do with the model?
8. Linear regression
9. Logistic regression

Building a regression model

1. Which model?
   • linear
   • Logistic model
   • Categorical variables

Linar model

1. fit a straight line through the data
   1. b0 + b1*score + e
      1. b0 = c
      2. b1 = m (slope)
   2. beta(e.g. IQ) + error = time taken

Categorical data

1. fit a mean to each category

Linear regression conditions

How to select features?

• step() in R
  1. add feature to list
  2. cross validation → remove if back
  3. list of features = best hypothesis
• manual
  • plot features

What’s the problem of overfitting?

How to choose the right model?

• enough data → Hold-out cross validation
  • train on 70% of data
  • validate with other 30%
  • retrain with 100% of data
• little data → k-fold cross validation
  • train on k-1 pieces
  • validate with remaining one
  • repeat k times
  • take average of all rounds

boolean values to predict → logistic model

what’s an ANOVA test

• t test for multiple groups
• compare means of multiple groups
• = special case of linear regression

what are the conditions for ANOVA?