Detecting Outliers
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SEVENTH EDITION Using Multivariate Statistics
www.pearsonhighered.comDistributions, and Univariate Outliers 81 4.2.1.2 Linearity and Homoscedasticity 84 4.2.1.3 Transformation 84 4.2.1.4 Detecting Multivariate Outliers 84 4.2.1.5 Variables Causing Cases to Be Outliers 86 4.2.1.6 Multicollinearity 88 4.2.2 Screening Grouped Data 88 4.2.2.1 Accuracy of Input, Missing Data,
LECTURE NOTES ON DATA MINING& DATA …
www.vssut.ac.inCommonly, outliers result from measurement errors, coding and recording errors, and, sometimes, are natural, abnormal values. Such nonrepresentative samples can seriously affect the model produced later. There are two strategies for dealing with outliers: a. Detect and eventually remove outliers as a part of the preprocessing phase, or b.
INTRODUCTION TO LINEAR REGRESSION ANALYSIS
download.e-bookshelf.de6.1 Importance of Detecting Influential Observations / 217 6.2 Leverage / 218 6.3 Measures of Influence: Cook’s D / 221 6.4 Measures of Influence: DFFITS and DFBETAS / 223 6.5 A Measure of Model Performance / 225
The ARIMA Procedure - SAS
support.sas.com188 F Chapter 7: The ARIMA Procedure Identification Stage Suppose you have a variable called SALES that you want to forecast. The following example illustrates ARIMA modeling and forecasting by using a simulated data set TEST that contains a time series SALES generated by an ARIMA(1,1,1) model.