Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs.

Access also available here: https://openstax.org/details/books/introductory-statistics

Table of Contents
Sampling and Data
Descriptive Statistics
Probability Topics
Discrete Random Variables
Continuous Random Variables
The Normal Distribution
The Central Limit Theorem
Confidence Intervals
Hypothesis Testing with One Sample
Hypothesis Testing with Two Samples
The Chi-Square Distribution
Linear Regression and Correlation
F Distribution and One-Way ANOVA

This book is meant to be a textbook for a standard one-semester introductory statistics course for general education students. Our motivation for writing it is twofold: 1.) to provide a low-cost alternative to many existing popular textbooks on the market; and 2.) to provide a quality textbook on the subject with a focus on the core material of the course in a balanced presentation.

Table of Contents
Chapter 1: Introduction
Chapter 2: Descriptive Statistics
Chapter 3: Basic Concepts of Probability
Chapter 4: Discrete Random Variables
Chapter 5: Continuous Random Variables
Chapter 6: Sampling Distributions
Chapter 7: Estimation
Chapter 8: Testing Hypotheses
Chapter 9: Two-Sample Problems
Chapter 10: Correlation and Regression
Chapter 11: Chi-Square Tests and F-Tests

Access also available here: https://resources.saylor.org/wwwresources/archived/site/textbooks/Introductory%20Statistics.pdf

Introductory Statistics was conceived and written to fit a particular topical sequence, but it can be used flexibly to accommodate other course structures. One such potential structure, which fits reasonably well with the textbook content, is provided below. Please consider, however, that the chapters were not written to be completely independent, and that the proposed alternate sequence should be carefully considered for student preparation and textual consistency.

1: Sampling and Data
Included in this chapter are the basic ideas and words of probability and statistics. You will soon understand that statistics and probability work together. You will also learn how data are gathered and what "good" data can be distinguished from "bad."
1.1: Introduction
1.2: Definitions of Statistics, Probability, and Key Terms
1.3: Data, Sampling, and Variation in Data and Sampling
1.4: Frequency, Frequency Tables, and Levels of Measurement
1.5: Experimental Design and Ethics
1.6: Data Collection Experiment (Worksheet)
1.7: Sampling Experiment (Worksheet)
1.E: Sampling and Data (Exercises)

2: Descriptive Statistics
In this chapter, you will study numerical and graphical ways to describe and display your data. This area of statistics is called "Descriptive Statistics." You will learn how to calculate, and even more importantly, how to interpret these measurements and graphs.
2.1: Prelude to Descriptive Statistics
2.2: Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs
2.3: Histograms, Frequency Polygons, and Time Series Graphs
2.4: Measures of the Location of the Data
2.4E: Measures of the Location of the Data (Exercises)
2.5: Box Plots
2.6: Measures of the Center of the Data
2.7: Skewness and the Mean, Median, and Mode
2.8: Measures of the Spread of the Data
2.9: Descriptive Statistics (Worksheet)
2.E: Descriptive Statistics (Exercises)

3: Probability Topics
Probability theory is concerned with probability, the analysis of random phenomena. The central objects of probability theory are random variables, stochastic processes, and events: mathematical abstractions of non-deterministic events or measured quantities that may either be single occurrences or evolve over time in an apparently random fashion.
3.1: Introduction
3.2: Terminology
3.3: Independent and Mutually Exclusive Events
3.4: Two Basic Rules of Probability
3.5: Contingency Tables
3.6: Tree and Venn Diagrams
3.7: Probability Topics (Worksheet)
3.E: Probability Topics (Exercises)

4: Discrete Random Variables
4.1: Prelude to Discrete Random Variables
4.2: Probability Distribution Function (PDF) for a Discrete Random Variable
4.3: Mean or Expected Value and Standard Deviation
4.4: Binomial Distribution
4.5: Discrete Distribution (Playing Card Experiment)
4.6: Discrete Distribution (Lucky Dice Experiment)
4.E: Discrete Random Variables (Exercises)

5: Continuous Random Variables
5.1: Introduction
5.2: Continuous Probability Functions
5.3: The Uniform Distribution
5.4: Continuous Distribution (Worksheet)
5.E: Continuous Random Variables (Exercises)
5.E: Exercises

6: The Normal Distribution
In this chapter, you will study the normal distribution, the standard normal distribution, and applications associated with them. The normal distribution has two parameters (two numerical descriptive measures), the mean ( μμ ) and the standard deviation ( σσ ).
6.1: Prelude to The Normal Distribution
6.2: The Standard Normal Distribution
6.1E: The Standard Normal Distribution (Exercises)
6.3: Using the Normal Distribution
6.4: Normal Distribution - Lap Times (Worksheet)
6.5: Normal Distribution - Pinkie Length (Worksheet)
6.E: The Normal Distribution (Exercises)

7: The Central Limit Theorem
In a population whose distribution may be known or unknown, if the size (n) of samples is sufficiently large, the distribution of the sample means will be approximately normal. The mean of the sample means will equal the population mean. The standard deviation of the distribution of the sample means, called the standard error of the mean, is equal to the population standard deviation divided by the square root of the sample size (n).
7.1: Prelude to the Central Limit Theorem
7.2: The Central Limit Theorem for Sample Means (Averages)
7.2E: The Central Limit Theorem for Sample Means (Exercises)
7.3: The Central Limit Theorem for Sums
7.4: Using the Central Limit Theorem
7.4E: Using the Central Limit Theorem (Exercises)
7.5: Central Limit Theorem - Pocket Change (Worksheet)
7.6: Central Limit Theorem - Cookie Recipes (Worksheet)
7.E: The Central Limit Theorem (Exercises)

8: Confidence Intervals
In this chapter, you will learn to construct and interpret confidence intervals. You will also learn a new distribution, the Student's-t, and how it is used with these intervals. Throughout the chapter, it is important to keep in mind that the confidence interval is a random variable. It is the population parameter that is fixed.
8.1: Prelude to Confidence Intervals
8.2: A Single Population Mean using the Normal Distribution
8.2E: A Single Population Mean using the Normal Distribution (Exercises)
8.3: A Single Population Mean using the Student t-Distribution
8.4: A Population Proportion
8.5: Confidence Interval - Home Costs (Worksheet)
8.6: Confidence Interval -Place of Birth (Worksheet)
8.7: Confidence Interval -Women's Heights (Worksheet)
8.E: Confidence Intervals (Exercises)
8.S: Confidence Intervals (Summary)

9: Hypothesis Testing with One Sample
9.1: Prelude to Hypothesis Testing
9.2: Null and Alternative Hypotheses
9.1E: Null and Alternative Hypotheses (Exercises)
9.3: Outcomes and the Type I and Type II Errors
9.2E: Outcomes and the Type I and Type II Errors (Exercises)
9.4: Distribution Needed for Hypothesis Testing
9.3E: Distribution Needed for Hypothesis Testing (Exercises)
9.5: Rare Events, the Sample, Decision and Conclusion
9.4E: Rare Events, the Sample, Decision and Conclusion (Exercises)
9.6: Additional Information and Full Hypothesis Test Examples
9.7: Hypothesis Testing of a Single Mean and Single Proportion (Worksheet)
9.E: Hypothesis Testing with One Sample (Exercises)

10: Hypothesis Testing with Two Samples
You have learned to conduct hypothesis tests on single means and single proportions. You will expand upon that in this chapter. You will compare two means or two proportions to each other. To compare two means or two proportions, you work with two groups. The groups are classified either as independent or matched pairs.
10.1: Prelude to Hypothesis Testing with Two Samples
10.2: Two Population Means with Unknown Standard Deviations
10.3: Two Population Means with Known Standard Deviations
10.4: Comparing Two Independent Population Proportions
10.5: Matched or Paired Samples
10.6: Hypothesis Testing for Two Means and Two Proportions (Worksheet)
10.E: Hypothesis Testing with Two Samples (Exercises)

11: The Chi-Square Distribution
11.1: Prelude to The Chi-Square Distribution
11.2: Facts About the Chi-Square Distribution
11.3: Goodness-of-Fit Test
11.4: Test of Independence
11.5: Test for Homogeneity
11.6: Comparison of the Chi-Square Tests
11.7: Test of a Single Variance (Not Included in the Course)
11.8: Lab 1: Chi-Square Goodness-of-Fit (Worksheet)
11.9: Lab 2: Chi-Square Test of Independence (Worksheet)
11.E: The Chi-Square Distribution (Exercises)

12: Linear Regression and Correlation
Regression analysis is a statistical process for estimating the relationships among variables and includes many techniques for modeling and analyzing several variables. When the focus is on the relationship between a dependent variable and one or more independent variables.
12.1: Prelude to Linear Regression and Correlation
12.2: Linear Equations
12.2E: Linear Equations (Exercises)
12.3: Scatter Plots
12.3E: Scatter Plots (Exercises)
12.4: The Regression Equation
12.4E: The Regression Equation (Exercise)
12.5: Testing the Significance of the Correlation Coefficient
12.5E: Testing the Significance of the Correlation Coefficient (Exercises)
12.6: Prediction
12.6E: Prediction (Exercises)
12.7: Outliers
12.7E: Outliers (Exercises)
12.8: Regression - Distance from School (Worksheet)
12.9: Regression - Textbook Cost (Worksheet)
12.10: Regression - Fuel Efficiency (Worksheet)
12.E: Linear Regression and Correlation (Exercises)

13: F Distribution and One-Way ANOVA
For hypothesis tests comparing averages between more than two groups, statisticians have developed a method called "Analysis of Variance" (abbreviated ANOVA). In this chapter, you will study the simplest form of ANOVA called single factor or one-way ANOVA. You will also study the FF distribution, used for one-way ANOVA, and the test of two variances. This is just a very brief overview of one-way ANOVA.
13.1: Prelude to F Distribution and One-Way ANOVA
13.2: One-Way ANOVA
13.3: The F Distribution and the F-Ratio
13.4: Facts About the F Distribution
13.5: Lab: One-Way ANOVA
13.E: F Distribution and One-Way ANOVA (Exercises)

We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.

(1) Statistics is an applied field with a wide range of practical applications.

(2) You don't have to be a math guru to learn from interesting, real data.

(3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world.

Reviews available here: https://open.umn.edu/opentextbooks/textbooks/introductory-statistics-with-randomization-and-simulation-first-edition

We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.

(1) Statistics is an applied field with a wide range of practical applications.

(2) You don't have to be a math guru to learn from interesting, real data.

(3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world.

This is a first draft of a free (as in speech, not as in beer, [Sta02]) (although it is free as in beer as well) textbook for a one-semester, undergraduate statistics course. It was used for Math 156 at Colorado State University–Pueblo in the spring semester of 2017.

Textbook for a one-semester, undergraduate statistics course. It was used for Math 156 at Colorado State University–Pueblo in the spring semester of 2017.

Table of Contents
Chapter 1. One-Variable Statistics: Basics
Chapter 2. Bi-variate Statistics: Basics
Chapter 3. Linear Regression
Chapter 4. Probability Theory
Chapter 5. Bringing Home the Data
Chapter 6. Basic Inferences

This course provides students with decision theory, estimation, confidence intervals, and hypothesis testing. It introduces large sample theory, asymptotic efficiency of estimates, exponential families, and sequential analysis.

In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.

This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 & TI-89 calculators. A formula packet and pdf version of the text are available on the website http://mostlyharmlessstatistics.com. Students new to probability and statistics are sure to benefit from this fully ADA accessible and relevant textbook. The examples resonate with everyday life, the text is approachable, and has a conversational tone to provide an inclusive and easy to read format for students.

This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 & TI-89 calculators. A formula packet and pdf version of the text are available on the website http://mostlyharmlessstatistics.com. Students new to probability and statistics are sure to benefit from this fully ADA accessible and relevant textbook. The examples resonate with everyday life, the text is approachable, and has a conversational tone to provide an inclusive and easy to read format for students.

able of Contents
Chapter 1 Introduction to Data
Chapter 2 Organizing Data
Chapter 3 Descriptive Statistics
Chapter 4 Probability
Chapter 5 Discrete Probability Distributions
Chapter 6 Continuous Probability Distributions
Chapter 7 Confidence Intervals for One Population
Chapter 8 Hypothesis Tests for One Population
Chapter 9 Hypothesis Tests & Confidence Intervals for Two Populations
Chapter 10 Chi-Square Tests
Chapter 11 Analysis of Variance
Chapter 12 Correlation and Regression
Chapter 12 Formulas
Chapter 12 Exercises
Chapter 13 Nonparametric Tests

Natural Resources Biometrics begins with a review of descriptive statistics, estimation, and hypothesis testing. The following chapters cover one- and two-way analysis of variance (ANOVA), including multiple comparison methods and interaction assessment, with a strong emphasis on application and interpretation. Simple and multiple linear regressions in a natural resource setting are covered in the next chapters, focusing on correlation, model fitting, residual analysis, and confidence and prediction intervals. The final chapters cover growth and yield models, volume and biomass equations, site index curves, competition indices, importance values, and measures of species diversity, association, and community similarity.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB programming.

OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League

OpenIntro Statistics strives to be a complete introductory textbook of the highest caliber. Its core derives from the classic notions of statistics education and is extended by recent innovations. The textbook meets high quality standards and has been used at Princeton, Vanderbilt, UMass Amherst, and many other schools. We look forward to expanding the reach of the project and working with teachers from all colleges and schools.

Table of Contents
1 Introduction to data
2 Probability (special topic)
3 Distributions of random variables
4 Foundations for inference
5 Inference for numerical data
6 Inference for categorical data
7 Introduction to linear regression
8 Multiple and logistic regression

Access also available here: https://www.openintro.org/book/os/

PowerPoint Slides to accompany Chapter 12 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 1 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 2 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 3 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 4 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.