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# Introduction to Statistical Data Analysis for the Life Sciences, Second Edition by Helle Sorensen, Claus Thorn Ekstrom (Paperback, 2014)

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## About this product

### Key Features

- Author(s)Claus Thorn Ekstrom,Helle Sorensen
- PublisherApple Academic Press Inc.
- Date of Publication18/12/2014
- Language(s)English
- FormatPaperback
- ISBN-101482238934
- ISBN-139781482238938
- GenreScience & Mathematics: Textbooks & Study Guides

### Publication Data

- Place of PublicationOakville
- Country of PublicationCanada
- ImprintApple Academic Press Inc.
- Content Note101 black & white illustrations, 97 black & white tables

### Dimensions

- Weight748 g
- Width156 mm
- Height235 mm
- Spine25 mm
- Pagination526

### Editorial Details

- Edition Statement2nd Revised edition

### Description

- Table Of ContentsDescription of Samples and Populations Data types Visualizing categorical data Visualizing quantitative data Statistical summaries What is a probability? R Linear Regression Fitting a regression line When is linear regression appropriate? The correlation coefficient Perspective R Comparison of Groups Graphical and simple numerical comparison Between-group variation and within-group variation Populations, samples, and expected values Least squares estimation and residuals Paired and unpaired samples Perspective R The Normal Distribution Properties One sample Are the data (approximately) normally distributed? The central limit theorem R Statistical Models, Estimation, and Confidence Intervals Statistical models Estimation Confidence intervals Unpaired samples with different standard deviations R Hypothesis Tests Null hypotheses t-tests Tests in a one-way ANOVA Hypothesis tests as comparison of nested models Type I and type II errors R Model Validation and Prediction Model validation Prediction R Linear Normal Models Multiple linear regression Additive two-way analysis of variance Linear models Interactions between variables R Non-Linear Regression Non-linear regression models Estimation, confidence intervals, and hypothesis tests Model validation R Probabilities Outcomes, events, and probabilities Conditional probabilities Independence The Binomial Distribution The independent trials model The binomial distribution Estimation, confidence intervals, and hypothesis tests Differences between proportions R Analysis of Count Data The chi-square test for goodness-of-fit 2 x 2 contingency table Two-sided contingency tables R Logistic Regression Odds and odds ratios Logistic regression models Estimation and confidence intervals Hypothesis tests Model validation and prediction R Statistical Analysis Examples Water temperature and frequency of electric signals from electric eels Association between listeria growth and RIP2 protein Degradation of dioxin Effect of an inhibitor on the chemical reaction rate Birthday bulge on the Danish soccer team Animal welfare Monitoring herbicide efficacy Case Exercises Case 1: Linear modeling Case 2: Data transformations Case 3: Two sample comparisons Case 4: Linear regression with and without intercept Case 5: Analysis of variance and test for linear trend Case 6: Regression modeling and transformations Case 7: Linear models Case 8: Binary variables Case 9: Agreement Case 10: Logistic regression Case 11: Non-linear regression Case 12: Power and sample size calculations Appendix A: Summary of Inference Methods Appendix B: Introduction to R Appendix C: Statistical Tables Appendix D: List of Examples Used throughout the Book Bibliography Index Exercises appear at the end of each chapter.

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