How deeply does Statistics Slam Dunk cover regression coefficient?

Interprets coefficients inside a fitted linear equation and pairs them with significance tests and concrete predictions.

Regression coefficient

As explained in Statistics Slam Dunk

A regression coefficient is a fitted parameter quantifying the change in a response associated with a one-unit change in a predictor while other predictors are held constant.

A regression coefficient measures how a predictor contributes to a predicted response within a fitted linear equation. Each coefficient multiplies one predictor while the other terms remain in the equation. section 5.7.3

In the NBA wins model, the coefficients show how predicted wins change with hustle statistics:

Deflections has a coefficient of 2.23, so each additional average deflection contributes 2.23 predicted wins. section 5.7.3
Loose balls recovered has a coefficient of 5.38, while contested shots has a negative coefficient of -0.24. section 5.7.3
Coefficients with p-values at or below 0.05 are treated as statistically significant influences on wins. section 5.7.3

Predicting Miami’s wins

Substituting Miami’s 2016–17 hustle statistics into the fitted equation produces 39 predicted wins, compared with 41 actual wins. Increasing loose balls recovered from 7.2 to 8.2 changes the rounded prediction to 44 wins.

How Statistics Slam Dunk covers it

liveBook manning.com

Read Statistics Slam Dunk on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Statistics Every Programmer NeedsA regression coefficient is a parameter quantifying the modeled relationship between a predictor and a response, conditional on the other model terms.
liveBook manning.com

Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA regression coefficient is a parameter multiplying an input variable that determines its signed contribution to a linear regression model's prediction.
liveBook manning.com

Starting Data Analytics with Generative AI and PythonA regression coefficient is an estimated parameter expressing the change in a modeled outcome associated with a one-unit change in a predictor while other predictors are held constant.
liveBook manning.com

Causal Inference for Data ScienceA regression coefficient is a parameter quantifying the change in a modeled outcome associated with a one-unit change in a predictor while other predictors are held fixed.

Compare how 5 books explain regression coefficient →

Related concepts

logitmeasures · A logit is a real-valued quantity representing an outcome’s log-odds or, in machine learning, an unnormalized model score used to derive probabilities.
parameterkind of · A model parameter is a numerical value, such as a weight or bias, that is learned or set to determine how a model computes outputs.
linear regressionproduced by · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
maximum likelihood estimationproduced by · Maximum likelihood estimation is a statistical method that selects parameter values maximizing the likelihood of observed data under a specified probabilistic model.
treatment effectcontrasts with · A treatment effect is the difference in an outcome attributable to receiving an intervention or exposure rather than a comparison condition.
direct effectequivalent to · A direct effect is the portion of a causal effect transmitted along a specified direct causal path from a cause to an outcome, excluding effects through mediators.

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.