Regression
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Regression. Population Covariance and Correlation. Sample Correlation. Sample Correlation. -.04. .98. -.79. Linear Model. DATA. REGRESSION LINE. (Still) Linear Model. DATA. REGRESSION CURVE. Parameter Estimation. Minimize SSE over possible parameter values.

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Sample correlation1
Sample Correlation

-.04

.98

-.79


Linear model
Linear Model

DATA

REGRESSION LINE


Still linear model
(Still) Linear Model

DATA

REGRESSION CURVE


Parameter estimation
Parameter Estimation

Minimize SSE over possible parameter values



Fitting a linear model in r1
Fitting a linear model in R

Intercept parameter is significant at .0623 level


Fitting a linear model in r2
Fitting a linear model in R

Slope parameter is significant at .001 level, so reject


Fitting a linear model in r3
Fitting a linear model in R

Residual Standard Error:


Fitting a linear model in r4
Fitting a linear model in R

R-squared is the correlation squared, also % of variation

explained by the linear regression





Multiple regression
Multiple Regression

Example: we could try to predict change in diameter

using both change in height as well as starting height

and Fertilizer


Multiple regression1
Multiple Regression

  • All variables are significant at .05 level

  • The Error went down and R-squared went up (this is good)

  • Can even handle categorical variables



Regression w machine learning point of view1
Regression w/ Machine Learning point of view

Music Year

Timbre (90 attributes)

http://archive.ics.uci.edu/ml/datasets/YearPredictionMSD

  • Let’s “train” (fit) different models to a training data set

  • Then see how well they do at predicting a different “validation” data set (this is how ML competitions on Kaggle work)


Regression w machine learning point of view2
Regression w/ Machine Learning point of view

  • Create a random sample of size 10000 from original 515,345 songs

  • Assign first 5000 to training data set, second 5000 are saved for validation


Regression w machine learning point of view3
Regression w/ Machine Learning point of view

  • Fit linear model and generalized boosting regression model (other popular choices include random forests and neural networks)

  • The period after the tilde denotes we will use all 91 variables for training, the –V1 throws out V1 (since this is what we’re predicting)


Regression w machine learning point of view4
Regression w/ Machine Learning point of view

  • Next we make predictions for the validation data set

  • We compare the models by calculating the sum of squares error (SSE) for each model



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