Introduction to Biostatistics with JMP
- Length: 288 pages
- Edition: 1
- Language: English
- Publisher: SAS Institute
- Publication Date: 2019-09-27
- ISBN-10: 1629606332
- ISBN-13: 9781629606330
- Sales Rank: #2985778 (See Top 100 Books)
Explore biostatistics using JMP in this refreshing introduction
Presented in an easy-to-understand way, Introduction to Biostatistics with JMP introduces undergraduate students in the biological sciences to the most commonly used (and misused) statistical methods that they will need to analyze their experimental data using JMP. It covers many of the basic topics in statistics using biological examples for exercises so that the student biologists can see the relevance to future work in the problems addressed.
The book starts by teaching students how to become confident in executing the right analysis by thinking like a statistician then moves into the application of specific tests. Using the powerful capabilities of JMP, the book addresses problems requiring analysis by chi-square tests, t tests, ANOVA analysis, various regression models, DOE, and survival analysis. Topics of particular interest to the biological or health science field include odds ratios, relative risk, and survival analysis.
The author uses an engaging, conversational tone to explain concepts and keep readers interested in learning more. The book aims to create bioscientists who can competently incorporate statistics into their investigative toolkits to solve biological research questions as they arise.
Contents About This Book What Does This Book Cover? Is This Book for You? What Should You Know about the Examples? We Want to Hear from You About The Author Chapter 1: Some JMP Basics Introduction JMP Help Manual Data Entry Opening Excel Files Column Information – Value Ordering Formulas “Platforms” The Little Red Triangle is Your Friend! Row States – Color and Markers Row States – Hiding and Excluding Saving Scripts Saving Outputs – Journals & RTF Files Graph Builder Chapter 2: Thinking Statistically Thinking Like a Statistician Summary Chapter 3: Statistical Topics in Experimental Design Introduction Sample Size and Power Replication and Pseudoreplication Randomization and Preventing Bias Variation and Variables Chapter 4: Describing Populations Introduction Population Description The Most Common Distribution – Normal or Gaussian Two Other Biologically Relevant Distributions The JMP Distribution Platform An Example: Big Class.jmp Parametric versus Nonparametric and “Normal Enough” Chapter 5: Inferring and Estimating Introduction Inferential Estimation Confidence Intervals There Are Error Bars, and Then There Are Error Bars So, You Want to Put Error Bars on Your JMP Graphs… Chapter 6: Null Hypothesis Significance Testing Introduction Biological Versus Statistical Ho NHST Rationale Error Types A Case Study in JMP Chapter 7: Tests on Frequencies: Analyzing Rates and Proportions Introduction Y.O.D.A. Assessment One-way Chi-Square Tests and Mendel’s Peas Two-way Chi-Square Tests and Piscine Brain Worms Chapter 8: Tests on Frequencies: Odds Ratios and Relative Risk Introduction Experimental Design and Data Collection Relative Risk Odds Ratios Chapter 9: Tests of Differences Between Two Groups Introduction Comparing Two Unrelated Samples and Bone Density Comparing Two Related Samples and Secondhand Smoke Chapter 10: Tests of Differences Between More Than Two Groups Introduction Comparing Unrelated Data Comparing Related Data Chapter 11: Tests of Association: Regression Introduction What Is Bivariate Linear Regression? What Is Regression? What Does Linear Regression Tell Us? What Are the Assumptions of Linear Regression? Is Your Weight Related to Your Fat? How Do You Identify Independent and Dependent Variables? It Is Difficult to Make Predictions, Especially About the Future Chapter 12: Tests of Association: Correlation Introduction What Is Correlation? How Does It Work? What Can’t Correlation Do? How to Calculate Correlation Coefficients: An Eyepopping Example Chapter 13: Modeling Trends: Multiple Regression Introduction What Is Multiple Regression? The Fit Model Platform Is Your Friend! Let’s Throw All of Them in… Stepwise Chapter 14: Modeling Trends: Other Regression Models Introduction Modeling Nominal Responses It’s Not Linear! Now What? Predictions Chapter 15: Modeling Trends: Generalized Linear Models Introduction What Are Generalized Linear Models? Why Use Generalized Linear Models? How to Use Generalized Linear Models The General Linear Model Binomial Generalized Linear Models Poisson Generalized Linear Models Chapter 16: Design of Experiments (DOE) Introduction What Is DOE? The Goals of DOE But Why DOE? DOE Flow in JMP Modeling the Data The Practical Steps for a DOE A DOE Example Start to Finish in JMP Chapter 17: Survival Analysis Introduction So, What Is It? Comparing Survival with Kaplan-Meier Curves Modeling Survival Quantitating Survival: Hazard Ratios Chapter 18: Hindrances to Data Analysis Introduction Hindrance 1: Outliers Hindrance 2: “Unclean” Data Hindrance 3: Sample Size and Power
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