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 22s
    Meet the instructor and learn about what will be taught in this course. FREE ACCESS
  • 5m 25s
    Define linear regression in the population and in the sample. Learn about the best linear predictor. FREE ACCESS
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    3.  Assessment Of Prediction Quality
    4m 38s
    Learn 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
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    4.  Inference For Linear Regression
    5m 36s
    Learn the answer to the inference question posited in previous videos. FREE ACCESS
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    5.  Other Types Of Regression
    3m 24s
    Understand regressions using nonlinear forms as well as regressions that result from replacing the squared loss function by other loss functions FREE ACCESS
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    6.  Modern Linear Regression For High-Dimensional Data
    4m 47s
    Learn the two motivations for using high-dmensional regressors in prediction. FREE ACCESS
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    7.  High-Dimensional Sparse Models And Lasso
    5m 55s
    Learn about high-dimensional sparse models and a penalized regression method called the Lasso. FREE ACCESS
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    8.  Inference With Lasso
    3m 22s
    Find out how to use loss to answer the inference question FREE ACCESS
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    9.  Other Penalized Regression Methods: Cross-Validation
    6m 15s
    Learn other penalized regression methods. Also learn what cross-validation is. FREE ACCESS
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    10.  Trees, Random Forests, and Boosted Trees
    7m 33s
    Learn how to use trees as a predictor for data. FREE ACCESS
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    11.  Neural Networks
    5m 39s
    Find out what a neural network is. FREE ACCESS
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    12.  Causality: Can It Be Established From Regression?
    6m 24s
    Learn about causality. Find out when you can and cannot establish it from regression. FREE ACCESS

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