# Analytics - Chapter 1: Introduction

 .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } 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 complexitybasic models are tailored to fit given circumstancesideal model is parsimonious with many fewer parameters than observations Error the amount by which the model fails to represent the data accuratelya measure of how much the model mispredicts what is actually observedcan 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 Modelnull hypothesis Model A Augmented Modelalternative hypothesis Proportional Reduction in Error PREindex, 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 Yi ith observation Ýi prediction of ith observation Xij observed value of j for ith parameter βi true model parameter εi true amount of difference between Yi and β0, if β0 is known exactly bi estimated model parameter ei amount that predictions misses the actual observationan estimate of εimeasures of variabilitymeasures of spread βiXij adjustment of basic prediction β0 unknown parameter estimated from the data b0 estimate of β0 that is derived from the datameasure of locationmeasure of central tendency .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } Authorathorne ID233489 Card SetAnalytics - Chapter 1: Introduction DescriptionIntroduction to Data Analysis Updated2013-09-08T16:06:54Z Show Answers