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Machine Learning & AI Foundations: Linear Regression

May 30, 2018 • Keith McCormick

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About this course

Expand your data science skills by learning how to leverage the concepts of linear regression to solve real-world problems.



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Linear regression for machine learning

39s
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What you should know

1m 55s
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Using the exercise files

1m 8s
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Building effective scatter plots in Chart Builder

7m 11s
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Adding labels and spikes to a scatter plot

3m 24s
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Create a 3D scatter plot

2m 57s
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Create a bubble chart

3m 11s
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Residuals and R2

4m 27s
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Calculating and interpreting regression coefficients

7m 23s
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Challenges and assumptions of multiple regression

8m 5s
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Checking assumptions visually

9m
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Checking assumptions with Explore

9m 55s
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Checking assumptions: Durbin-Watson

1m 55s
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Checking assumptions: Levine's test

4m 15s
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Checking assumptions: Correlation matrix

4m 31s
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Checking assumptions: Residuals plot

6m 23s
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Checking assumptions: Summary

3m 59s
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Creating dummy codes

8m 4s
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Dummy coding with the R extension

1m 50s
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Detecting variable interactions

5m 1s
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Creating and testing interaction terms

4m 33s
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Three regression strategies and when to use them

2m 45s
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Understanding partial correlations

3m 54s
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Understanding part correlations

3m 40s
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Visualizing part and partial correlations

5m 11s
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Simultaneous regression: Setting up the analysis

2m 43s
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Simultaneous regression: Interpreting the output

7m 55s
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Hierarchical regression: Setting up the analysis

5m 5s
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Hierarchical regression: Interpreting the output

7m 20s
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Creating a train-test partition in SPSS

4m 30s
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Stepwise regression: Setting up the analysis

3m 24s
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Stepwise regression: Interpreting the output

4m 5s
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Collinearity diagnostics

6m 30s
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Dealing with multicollinearity: Factor analysis/PCA

4m 17s
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Dealing with multicollinearity: Manually combine IVs

3m 15s
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Diagnosing outliers and influential points

7m 21s
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Dealing with outliers: Studentized deleted residuals

5m 49s
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Dealing with outliers: Should cases be removed?

6m 48s
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Detecting curvilinearity

5m 20s
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Regression options

5m 20s
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Automatic linear modeling

6m 37s
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Regression trees

6m 19s
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Time series forecasting

4m 30s
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Categorical regression with optimal scaling

6m 9s
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Comparing regression to Neural Nets

4m 31s
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Logistic regression

4m 54s
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SEM

4m 23s
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What is the extension hub?

2m 31s
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Ridge regression

3m 15s
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Lasso and elastic net

5m 17s
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What's next

1m 49s