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Part of: Synthesis digital library of engineering and computer science

Includes bibliographical references (page 213).

1. Introduction to statistical analysis -- 1.1. Scales of measurements -- 1.2. Data, data collection, and data presentation -- 1.3. Data groupings -- 1.4. Methods of visualizing data -- 1.5. Exploring data analysis (EDA) -- 1.6. Exercises

2. Introduction to R software -- 2.1. How to download and install R -- 2.2. Using R for descriptive statistical and plots -- 2.3. Basics of R -- 2.4. Basic operations in R -- 2.5. Data exploration -- 2.6. Exercises

3. Descriptive data -- 3.1. Central tendency -- 3.2. Measure of dispersion -- 3.3. Shapes of the distribution--symmetric and asymmetric -- 3.4. Exercises

4. Basic probability concepts -- 4.1. Experiment, outcome, and sample space -- 4.2. Elementary events -- 4.3. Complementary events -- 4.4. Mutually exclusive events -- 4.5. Mutually inclusive events -- 4.6. Venn diagram -- 4.7. Probability -- 4.8. Axioms of probability -- 4.9. Basic properties of probability -- 4.10. Independent events and dependent events -- 4.11. Multiplication rule of probability -- 4.12. Conditional probabilities -- 4.13. Computation of probability in R -- 4.14. Exercises

5. Discrete probability distributions -- 5.1. Probability mass distribution -- 5.2. Expected value and variance of a discrete random variable -- 5.3. Binomial probability distribution -- 5.4. Expected value and variance of a binomial distribution -- 5.5. Solve problems involving binomial distribution using R -- 5.6. Exercises

6. Continuous probability distributions -- 6.1. Normal distribution and standardized normal distribution -- 6.2. Standard normal score (z-score) -- 6.3. Approximate normal distribution to the binomial distribution -- 6.4. Use of the normal distribution in business problem solving using R -- 6.5. Exercises

7. Other continuous probability distributions -- 7.1. Student-t distribution -- 7.2. Chi-square distribution -- 7.3. F-Distribution -- 7.4. Exercises

8. Sampling and sampling distribution -- 8.1. Probability and non-probability sampling -- 8.2. Probability sampling techniques--simple random, systematic, stratified, and cluster samples -- 8.3. Non-probability sampling techniques -- 8.4. Sampling distribution of the mean -- 8.5. Central limit theorem and its significance -- 8.6. Exercises

9. Confidence intervals for single population mean and proportion -- 9.1. Point estimates and interval estimates -- 9.2. Confidence intervals for mean -- 9.3. Confidence intervals for proportion -- 9.4. Calculating the sample size -- 9.5. Factors that determine margin of error -- 9.6. Exercises

10. Hypothesis testing for single population mean and proportion -- 10.1. Null and alternative hypotheses -- 10.2. Type I and type II errors -- 10.3. Acceptance and rejection regions -- 10.4. Hypothesis testing procedures -- 10.5. Exercises

11. Regression analysis and correlation -- 11.1. Construction of line fit plots -- 11.2. Types of regression analysis -- 11.3. Multiple linear regression -- 11.4. Pearson correlation coefficient -- 11.5. Exercises

12. Poisson distribution -- 12.1. Poisson distribution and its properties -- 12.2. Mean and variance of a Poisson distribution -- 12.3. Application of Poisson distribution -- 12.4. Poisson to approximate the binomial -- 12.5. Exercises

13. Uniform distributions -- 13.1. Uniform distribution and its properties -- 13.2. Mean and variance of a uniform distribution -- 13.3. Exercises.

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Introduction to Statistics Using R is organized into 13 major chapters. Each chapter is broken down into many digestible subsections in order to explore the objectives of the book. There are many real-life practical examples in this book and each of the examples is written in R codes to acquaint the readers with some statistical methods while simultaneously learning R scripts.

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Title from PDF title page (viewed on July 31, 2019).