Using R at the bench : step-by-step data analytics for biologists / M. Bremer, Department of Mathematics and Statistics, San Jose State University, R.W. Doerge, Department of Statistics, Department of Agronomy, Purdue University

Author:: Bremer, M. (Martina)
Published:: Cold Spring Harbor, New York : Cold Spring Harbor Laboratory Press, [2015]
Physical Description:: ix, 188 pages : illustrations ; 24 cm
Additional Creators:: Doerge, R. W. (Rebecca W.)

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Contents:

Machine generated contents note: 1.Introduction -- 2.Common Pitfalls -- 2.1.Examples of Common Mistakes -- 2.2.Defining Your Question -- 2.3.Working with and Talking to a Statistician -- 2.4.Exploratory versus Inferential Statistics -- 2.5.Different Sources of Variation -- 2.6.The Importance of Checking Assumptions and the Ramifications of Ignoring the Obvious -- 2.7.Statistical Software Packages -- 2.8.Installing and Using R and R Commander -- 2.8.1.Loading Data -- 2.8.2.Variable Types -- 2.8.3.Handling Graphics -- 2.8.4.Saving Your Work -- 2.8.5.Getting Help -- 3.Descriptive Statistics -- 3.1.Definitions -- 3.2.Numerical Ways to Describe Data -- 3.2.1.Categorical Data -- 3.2.2.Quantitative Data -- 3.2.3.Determining Outliers -- 3.2.4.How to Choose a Descriptive Measure -- 3.3.Graphical Methods to Display Data -- 3.3.1.How to Choose the Appropriate Graphical Display for Your Data -- 3.4.Probability Distributions -- 3.4.1.The Binomial Distribution -- 3.4.2.The Normal Distribution -- 3.4.3.Assessing Normality in Your Data -- 3.4.4.Data Transformations -- 3.5.The Central Limit Theorem -- 3.5.1.The Central Limit Theorem for Sample Proportions -- 3.5.2.The Central Limit Theorem for Sample Means -- 3.6.Standard Deviation versus Standard Error -- 3.7.Error Bars -- 3.8.Correlation -- 3.8.1.Correlation and Causation -- 4.Design of Experiments -- 4.1.Mathematical and Statistical Models -- 4.1.1.Biological Models -- 4.2.Describing Relationships between Variables -- 4.3.Choosing a Sample -- 4.3.1.Problems in Sampling: Bias -- 4.3.2.Problems in Sampling: Accuracy and Precision -- 4.4.Choosing a Model -- 4.5.Sample Size -- 4.6.Resampling and Replication -- 5.Confidence Intervals -- 5.1.Interpretation of Confidence Intervals -- 5.1.1.Confidence Levels -- 5.1.2.Precision -- 5.2.Computing Confidence Intervals -- 5.2.1.Confidence Intervals for Large Sample Mean -- 5.2.2.Confidence Interval for Small Sample Mean -- 5.2.3.Confidence Interval for Population Proportion -- 5.3.Sample Size Calculations -- 6.Hypothesis Testing -- 6.1.The Basic Principle -- 6.1.1.p-values -- 6.1.2.Errors in Hypothesis Testing -- 6.1.3.Power of a Test -- 6.1.4.Interpreting Statistical Significance -- 6.2.Common Hypothesis Tests -- 6.2.1.t-test -- 6.2.2.z-test -- 6.2.3.F-test -- 6.2.4.Tukey's Test and Scheffe's Test -- 6.2.5.Χ2-test: Goodness-of-Fit or Test of Independence -- 6.2.6.Likelihood Ratio Test -- 6.3.Non-parametric Tests -- 6.3.1.Wilcoxon-Mann-Whitney Rank Sum Test -- 6.3.2.Fisher's Exact Test -- 6.3.3.Permutation Tests -- 6.4.E-values -- 7.Regression and ANOVA -- 7.1.Regression -- 7.1.1.Correlation and Regression -- 7.1.2.Parameter Estimation -- 7.1.3.Hypothesis Testing -- 7.1.4.Logistic Regression -- 7.1.5.Multiple Linear Regression -- 7.1.6.Model Building in Regression: Which Variables to Use? -- 7.1.7.Verification of Assumptions -- 7.1.8.Outliers in Regression -- 7.1.9.A Case Study -- 7.2.ANOVA -- 7.2.1.One-Way ANOVA Model -- 7.2.2.Two-Way ANOVA Model -- 7.2.3.ANOVA Assumptions -- 7.2.4.ANOVA Model for Microarray Data -- 7.3.What ANOVA and Regression Models Have in Common -- 8.Special Topics -- 8.1.Classification -- 8.2.Clustering -- 8.2.1.Hierarchical Clustering -- 8.2.2.Partitional Clustering -- 8.3.Principal Component Analysis -- 8.4.Microarray Data Analysis -- 8.4.1.The Data -- 8.4.2.Normalization -- 8.4.3.Statistical Analysis -- 8.4.4.The ANOVA Model -- 8.4.5.Variance Assumptions -- 8.4.6.Multiple Testing Issues -- 8.5.Next-Generation Sequencing Analysis -- 8.5.1.Experimental Overview -- 8.5.2.Statistical Issues in Next-Generation Sequencing Experiments -- 8.6.Maximum Likelihood -- 8.7.Frequentist and Bayesian Statistics.

Subject(s):

ISBN:

9781621821120 (hardcover)
1621821129 (hardcover)

Bibliography Note:

Includes bibliographical references and index.

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