Analytics - Chapter 1: Introduction

the basic score os observations which we want to analyze

• makes a specific prediction for each observation in the data, which can vary in complexity
• basic models are tailored to fit given circumstances
• ideal model is parsimonious with many fewer parameters than observations

• the amount by which the model fails to represent the data accurately
• a measure of how much the model mispredicts what is actually observed
• can be decreased by adding parameters to the model so that predictions are conditional of additional information to each observation

a numerical characteristic of a population which is hypothesized to be a significant predictor of the data

• Compact Model
• null hypothesis

• Augmented Model
• alternative hypothesis

• PRE
• index, as a percentage, of how worthwhile additional parameters are to the model
• values are between 0 and 1, where values closer to 1 are more worthwhile

i^th observation

prediction of i^th observation

observed value of j for i^th parameter

true model parameter

true amount of difference between Y_i and β₀, if β₀ is known exactly

estimated model parameter

• amount that predictions misses the actual observation
• an estimate of ε_i
• measures of variability
• measures of spread

adjustment of basic prediction

unknown parameter estimated from the data

• estimate of β₀ that is derived from the data
• measure of location
• measure of central tendency