Analytics - Chapter 1: Introduction

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  1. Data
    the basic score os observations which we want to analyze
  2. 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
  3. 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
  4. Parameter
    a numerical characteristic of a population which is hypothesized to be a significant predictor of the data
  5. Model C
    • Compact Model
    • null hypothesis
  6. Model A
    • Augmented Model
    • alternative hypothesis
  7. 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
  8. Yi
    ith observation
  9. Ýi
    prediction of ith observation
  10. Xij
    observed value of j for ith parameter
  11. βi
    true model parameter
  12. εi
    true amount of difference between Yi and β0, if β0 is known exactly
  13. bi
    estimated model parameter
  14. ei
    • amount that predictions misses the actual observation
    • an estimate of εi
    • measures of variability
    • measures of spread
  15. βiXij
    adjustment of basic prediction
  16. β0
    unknown parameter estimated from the data
  17. b0
    • estimate of β0 that is derived from the data
    • measure of location
    • measure of central tendency

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Analytics - Chapter 1: Introduction
2013-09-08 16:06:54
psychology 400a stats

Introduction to Data Analysis
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