Linear Models

  • This introductory tutorial for SPSS 10.1 and 11.0 for Windows explains how to enter and summarize data and groups of data and to generate graphs.
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  • This activity guides students through the process of checking the validity of data, performing summary analysis, constructing box plots, and determining whether significant differences exist. The data comes from a study of mineral levels in older adults and is available in Minitab, Excel, SAS, and text formats.
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  • This online textbook provides information on the statistical analysis of nutritional data. Techniques covered include data cleaning, descriptive statistics, histograms, graphics, scatterplots, outlier identification, regression and correlation, confounding, and interactions. Each chapter includes exercises with real data and self-tests to be used with SPSS.
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  • This site lists a set of case studies that cover regression topics, random number calculations of pi, as well as limit theorems. On the individual case study pages are the descriptions and/or instructions.
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  • This part of the NIST Engineering Statistics handbook describes different graphs and plots used in Exploratory Data Analysis.
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  • This chapter of the NIST Engineering Statistics handbook describes the measurement process characterization with discussions of control, calibration, gauge studies, and uncertainty analysis, and a set of case studies.
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  • This site gives an explanation, a definition and an example of linear regression. Topics include least-squares, residuals, extrapolation, outliers, and influential observations.

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  • This site gives an explanation, a definition and an example of inference in linear regression. Topics include confidence intervals for intercept and slope, significance tests, mean response, and prediction intervals.
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  • This site gives an explanation, a definition and an example of multiple linear regression. Topics include confidence intervals, tests of significance, and squared multiple correlation.
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  • This site gives an explanation, a definition and an example of ANOVA for regression. Topics include analysis of variance calculations for simple and multiple regression, and F-statistics.
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