{{ message }}
/ DSCI_561_regr-1 Public

# UBC-MDS/DSCI_561_regr-1

Switch branches/tags
Nothing to show

## Files

Failed to load latest commit information.
Type
Name
Commit time

# DSCI 561: Regression I

Inference on a numeric response, in the presence of predictors.

## Course Learning Outcomes

By the end of the course, students are expected to:

• Fit a linear regression model using R and `broom`.
• Interpret how predictors influence a response using a fitted linear regression model.
• Identify whether a linear regression model is appropriate for a given dataset.
• Identify use cases of linear regression
• Evaluate the fit of a regression model using residual plots
• Evaluate the fit of a regression model using appropriate measures of model goodness (MSE and R-squared), and drawing the connection back to the null model.
• Quantify estimation error vs. prediction error in the presence of predictors, and understand the decomposition of error in each case.
• Understand the effect of multicollinearity on an OLS estimate.
• Convert categorical predictors for use in a linear regression model.

## Assessments

This is an assignment-based course. You'll be evaluated as follows:

Lab Assignment 1 15% Saturday, Nov 24 at 18:00 Github
Lab Assignment 2 15% Saturday, Dec 1 at 18:00 Github
Lab Assignment 3 15% Saturday, Dec 8 at 18:00 Github
Lab Assignment 4 15% Wed, Dec 12 at 18:00 Github
Quiz 1 20% Monday, Sept 24, 14:00-14:30 TBD (aiming for Canvas)
Quiz 2 20% Thursday, December 13 TBD (aiming for Canvas)

## Lecture Schedule

Lecture Topic
1 Review of statistical inference, connection between 2-samples t-test, ANOVA and linear regression
2 Linear model in general matrix notation, different type of predictors, interpretation of coefficients and parametrizations, estimation and inference
3 Continuous and categorical predictors, interaction term, interpretation of coefficients, estimation and inference
4 Least squares estimation, fitted values, residuals, confidence intervals
5 Multiple linear regression, out-of-sample predictions, prediction intervals
6 Goodness of fit, estimation error, prediction error
7 Transformations, multicollinearity, diagnostics, unusual and influential data
8 Bootstrapping

## Annotated Resources

1. Intro to Statistical Learning (ISLR), especially Chapter 3.
• A modern and approachable take on statistics / machine learning.
2. R for Data Science (r4ds), especially Part IV.
• Practical and approachable book on the use of R for data science.
3. Linear Models with R
• Comprehensive book on linear models.
4. OpenIntro Statistics
• Fairly accessible, seems to lean towards a traditional approach. Chapters 7 & 8 are relevant for linear regression.

## 6. Policies

Please see the general MDS policies.

No description or website provided.

## Releases

No releases published

## Packages 0

No packages published