regression.

Automatic Removal of Outliers from Regression and GLMs
03 May 2020 | by Tim Bock

A well-known problem with linear regression, binary logit, ordered logit, and other GLMs, is that a small number of rogue observations can cause the results

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Creating Quad Maps in Displayr
07 February 2020 | by Tim Bock

In this post I describe how to quickly create a quad map in Displayr. The example uses a Shapley Regression to work out the relative

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Customer Satisfaction: General, Product, & Attribute Questions
14 December 2018 | by Madeleine Picard

Rather than just measuring one type of customer satisfaction, it's useful to measure these three aspects of customer satisfaction.

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How to Interpret Logistic Regression Coefficients
25 October 2018 | by Tim Bock

This post describes how to interpret the coefficients, also known as parameter estimates, from logistic regression (a.k.a. binary logit).

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Using Displayr
How to do Logistic Regression in Displayr
24 October 2018 | by Tim Bock

This is a practical guide to logistic regression. To get the most out of this post, I recommend you follow along with my instructions and

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How to Interpret Logistic Regression Outputs
24 October 2018 | by Tim Bock

Logistic regression (a.k.a. binary logit or binary logistic regression) is a predictive modeling technique used to predict outcomes involving two options

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What is Logistic Regression?
22 August 2018 | by Justin Yap

Logistic regression is a type of regression analysis used when the dependent variable is binary (i.e., has only two possible outcomes).

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time series chart with smoothing running line
Smoothing Time Series Data
16 July 2018 | by Carmen Chan

For time-series data, you'll want to separate long-term trends and seasonal changes from random fluctuations. Find out which time smoother to use.

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What are Variance Inflation Factors (VIFs)?
What are Variance Inflation Factors (VIFs)?
06 April 2018 | by Tim Bock

The variance inflation factor (VIF) quantifies the extent of correlation between one predictor and the other predictors in a model.

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Comparing Partial Least Squares to Johnson’s Relative Weights
19 June 2017 | by Tim Bock

Although PLS and Johnson's Relative Weights are both techniques for dealing with correlations between predictors, they give fundamentally different results.

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key driver analysis
The Problem with Using Multiple Linear Regression for Key Driver Analysis: a Case Study of the Cola Market
18 June 2017 | by Tim Bock

Why is Multiple Linear Regression the standard technique taught for Key Driver Analysis when it gets it so wrong? The better method is Johnson’s Relative We

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Using Displayr
Using Partial Least Squares to Conduct Relative Importance Analysis in Displayr
16 June 2017 | by Jake Hoare

Partial Least Squares (PLS) is a popular method for relative importance analysis in fields where the data typically includes more predictors than observations. Relative importance analysis

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5 Ways to Visualize Relative Importance Scores from Key Driver Analysis
26 April 2017 | by Tim Bock

5 ways of presenting the results of key driver analysis techniques, such as Shapley Value, Kruskal Analysis, and Relative Weights.

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When to Use Relative Weights Over Shapley
19 April 2017 | by Justin Yap

Shapley regression has been gaining popularity in recent years and has been (re-)invented multiple times. However, relative weights, should be used instead.

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