Regression and Prediction
Everyone
- 12 videos | 1h 1m 18s
- Includes Assessment
Learn the basics of regression for prediction and inferential purposes. Also, gain an understanding of modern linear and non-linear regression for prediction and inferential purposes. Finally, get practical experience of using classical and modern regression methods for prediciton and inferential purposes.
WHAT YOU WILL LEARN
-
Gain a basic understanding of what will happen in the course
Know how to use linear regression for prediction
Understand anova in the sample and in the population and know how to assess the output of sample predictive performance
Be able to describe why beta 1 is the regression coefficient and know why this is useful for understanding the regression coefficient
Learn about nonlinear regression forms. also learn about regressions that result from replacing the squared loss function by other loss functions.
Know when to use high-dimensional regressors in prediction -
Explain approximate sparsity and lasso
Know how to estimate the target regression estimate in the high dimensional regression problem
Be able to use cross validation
Understand tree based prediction rules and ways to improve them by bagging or boosting
Know what neural networks are and how they work
Know when causality can and cannot be established from regression
IN THIS COURSE
-
2m 22sMeet the instructor and learn about what will be taught in this course. FREE ACCESS
-
5m 25sDefine linear regression in the population and in the sample. Learn about the best linear predictor. FREE ACCESS
-
3. Assessment Of Prediction Quality4m 38sLearn how to understand the analysis of variance, also known as ANOVA. Also, learn how to assess the output of sample predictive performance of the sample linear regression FREE ACCESS
-
4. Inference For Linear Regression5m 36sLearn the answer to the inference question posited in previous videos. FREE ACCESS
-
5. Other Types Of Regression3m 24sUnderstand regressions using nonlinear forms as well as regressions that result from replacing the squared loss function by other loss functions FREE ACCESS
-
6. Modern Linear Regression For High-Dimensional Data4m 47sLearn the two motivations for using high-dmensional regressors in prediction. FREE ACCESS
-
7. High-Dimensional Sparse Models And Lasso5m 55sLearn about high-dimensional sparse models and a penalized regression method called the Lasso. FREE ACCESS
-
8. Inference With Lasso3m 22sFind out how to use loss to answer the inference question FREE ACCESS
-
9. Other Penalized Regression Methods: Cross-Validation6m 15sLearn other penalized regression methods. Also learn what cross-validation is. FREE ACCESS
-
10. Trees, Random Forests, and Boosted Trees7m 33sLearn how to use trees as a predictor for data. FREE ACCESS
-
11. Neural Networks5m 39sFind out what a neural network is. FREE ACCESS
-
12. Causality: Can It Be Established From Regression?6m 24sLearn about causality. Find out when you can and cannot establish it from regression. FREE ACCESS
