Analytics  Chapter 1: Introduction
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Data
the basic score os observations which we want to analyze

Model
 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

Error
 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

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

Model C
 Compact Model
 null hypothesis

Model A
 Augmented Model
 alternative hypothesis

Proportional Reduction in Error
 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


Ý_{i}
prediction of i^{th} observation

X_{ij}
observed value of j for i^{th} parameter

β_{i}
true model parameter

ε_{i}
true amount of difference between Y_{i} and β_{0}, if β_{0} is known exactly

b_{i}
estimated model parameter

e_{i}
 amount that predictions misses the actual observation
 an estimate of ε_{i}
 measures of variability
 measures of spread

β_{i}X_{ij}
adjustment of basic prediction

β_{0}
unknown parameter estimated from the data

b_{0}
 estimate of β_{0} that is derived from the data
 measure of location
 measure of central tendency