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Applied Statistics I

by: Vance Bode Sr.

Applied Statistics I 22S 164

Vance Bode Sr.
GPA 3.72


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This 3 page Study Guide was uploaded by Vance Bode Sr. on Friday October 23, 2015. The Study Guide belongs to 22S 164 at University of Iowa taught by Staff in Fall. Since its upload, it has received 44 views. For similar materials see /class/228089/22s-164-university-of-iowa in Natural Sciences and Mathematics at University of Iowa.

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Date Created: 10/23/15
Revised September 26 2008 Study Guide for the MS Comprehensive Exam Applied Statistics 22S 1 641 65 The following teth are suggested as references Abraham amp Ledolter Introduction to Regression Modeling Dquury FOX AppliedRegression Analysis Linear Models andRelatedMethods Sage Montgomery Design and Analysis of Experiments Wiley Oehlert A First Course in Design andAnalysis ofExperiments Freeman Weisberg Applied Linear Regression Wiley Topics 1 The simple linear regression model model assumptions least squares estimation properties of least squares estimators partitioning the total variation hypothesis tests on slope and intercept con dence intervals for slope intercept mean response predicted value regression through the origin coef cient of multiple determination R2 simultaneous inference on slope and intercept residual plots normal probability plots Fquotr39FtPrhrDFpsrs 2 Multiple linear regression model all of the same subtopics listed for the simple linear regression model joint partial and sequential Ftests Ftests for the general linear hypothesis polynomial regression Ftest for lackof t Multicollinearity and variance in ation factors indicator variables Ftests for submodels involving indicators and continuous predictors eg parallel trends weenie057 3 Model selection a b selection criteria including PRESS Cp and Bayesian c1ite1ia sequential variable selection procedures 4 Residual analysis and in uence diagnostics a b c d e f residual plots outlier detection added variable plots in uence diagnostics such as Cook s distances DFFITS etc autocorrelation and DurbinWatson test nonconstant variance and Levene s test 5 Single factor xed effects a b c d e f g h i j completely randomized designs and the role of randomization ANOVA Ftests eXpected mean square power regression approach using SAS estimation of effects multiple compa1isons contrasts orthogonal polynomials residual plots transformation of the response va1iable power and sample size single factor random effects ANOVA Ftests EMS estimation of variance components 6 Fact01ial designs a b c d e f factotial structure no interaction interaction more than one observation per cell interaction one observation per cell polynomials and division of interaction effects unequal cell sizes 7 Block designs rue057 role of blocking randomized completeblock designs Latin Square Designs replicating Latin Squares blocking of factotials incomplete blocks confounding in 2k designs 8 Complex designs a random effects b mixed models c repeated measures d crossed and nested factors e split plot designs f rules for eXpected mean squares g variances of means and contrasts h crossover designs i power and sample size j restricted and unrestricted models k EMS and estimation of variance components 1 PROC MIXED ls means and differences 9 Other topics a power transformations b weighted and generalized least squares c variancestabilizing transformations d BoxCox and BoxTidwell procedures to obtain transformations e analysis of covariance f unbalanced data GLM approach estimable functions g missing data empty cells h Tukey s one degree of freedom for nonadditivity


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