golf

A full factorial experiment with four factors run by a golf enthusiast. The 
 objective of the experiments was for the golfer to maximize her driving distance 
 at a specific tee off location on her local golf course. The golfer considered 
 the following factors:
<ul>
<li><code>T</code> = Tee height (cm)</li>
<li><code>H</code> = Holes the golf ball had been played for prior to the experimental tee shot</li>
<li><code>C</code> = Club type</li>
<li><code>D</code> = Time of day (on the 24 hour clock)</li>
</ul>The data are in standard order, however the actual experiments were run in 
 random order. 
Coded values for <code>A</code>, <code>B</code>, <code>C</code>, and <code>D</code> should be used in the linear
 regression model analysis, with <code>-1</code> representing the low value and <code>+1</code> the
 high value.

datasets

A collection of scripts and data files for the statistics text:
"Process Improvement using Data" <https://learnche.org/pid> and the online
course "Experimentation for Improvement" found on Coursera. The package
contains code for designed experiments, data sets and other convenience
functions used in the book.

Kevin Dunn

Process Improvement using Data

golf function

A data.frame containing 16 observations of 4 variables (A, B, C, D, with y as a response variable.)

golf: Full factorial experiments to maximize a golfer's driving distance.

Description

Usage

Arguments

Format