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A Novel test structure for automated measurement of charge transfer efficiency in charge coupled imaging devices
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Increased urinary excretion of triiodthyronine (T3) and thyroxine (T4) and decreased serum thyreotropic hormone (TSH) induced by motion sickness
The diagnostic significance of the Stimulation of TSH secretion by administration of thyrotropin releasing hormone ( TRH ) in diseases of the hypothalamus and pituitary
Advanced Data Analysis - Lecture Notes
Lecture notes for Advanced Data Analysis (ADA1 Stat 427/527 and ADA2 Stat 428/528), Department of Mathematics and Statistics, University of New Mexico, Fall 2016-Spring 2017. Additional material including RMarkdown templates for in-class and homework exercises, datasets, R code, and video lectures are available on the course websites: https://statacumen.com/teaching/ada1 and https://statacumen.com/teaching/ada2 .
Contents
I ADA1: Software 0 Introduction to R, Rstudio, and ggplot
II ADA1: Summaries and displays, and one-, two-, and many-way tests of means 1 Summarizing and Displaying Data 2 Estimation in One-Sample Problems 3 Two-Sample Inferences 4 Checking Assumptions 5 One-Way Analysis of Variance
III ADA1: Nonparametric, categorical, and regression methods 6 Nonparametric Methods 7 Categorical Data Analysis 8 Correlation and Regression IV ADA1: Additional topics 9 Introduction to the Bootstrap 10 Power and Sample size 11 Data Cleaning
V ADA2: Review of ADA1 1 R statistical software and review
VI ADA2: Introduction to multiple regression and model selection 2 Introduction to Multiple Linear Regression 3 A Taste of Model Selection for Multiple Regression
VII ADA2: Experimental design and observational studies 4 One Factor Designs and Extensions 5 Paired Experiments and Randomized Block Experiments 6 A Short Discussion of Observational Studies
VIII ADA2: ANCOVA and logistic regression 7 Analysis of Covariance: Comparing Regression Lines 8 Polynomial Regression 9 Discussion of Response Models with Factors and Predictors 10 Automated Model Selection for Multiple Regression 11 Logistic Regression
IX ADA2: Multivariate Methods 12 An Introduction to Multivariate Methods 13 Principal Component Analysis 14 Cluster Analysis 15 Multivariate Analysis of Variance 16 Discriminant Analysis 17 Classificationhttps://digitalrepository.unm.edu/unm_oer/1002/thumbnail.jp
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