PowerPoint Slides to accompany Chapter 6 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 7 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 8 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 9 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.

Optimal, Integral, Likely is a free, open-source textbook intended for UBC’s course MATH 105: Integral Calculus with Applications to Commerce and Social Sciences. It is shared under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

1 Vectors and Geometry in Two & Three Dimensions
2 Partial Derivatives
3 Integration
4 Probability
5 Sequence and Series
A Proofs and Supplements
B High school material

Welcome to behavioral statistics, a statistics textbook for social science majors!

Table of Contents
Unit 1: Description
1: Introduction to Behavioral Statistics
2: What Do Data Look Like? (Graphs)
3: Descriptive Statistics
4: Distributions
5: Using z
6: APA Style
Unit 2: Mean Differences
7: Inferential Statistics and Hypothesis Testing
8: One Sample t-test
9: Independent Samples t-test
10: Dependent Samples t-test
11: BG ANOVA
12: RM ANOVA
13: Factorial ANOVA (Two-Way)
Unit 3: Relationships
14: Correlations
15: Regression
16: Chi-Square
Unit 4: Wrap Up
17: Wrap Up

Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.

This course takes a broad-based look at poker theory and applications of poker analytics to investment management and trading.

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

You are probably asking yourself the question, "When and where will I use statistics?". If you read any newspaper or watch television, or use the Internet, you will see statistical information. There are statistics about crime, sports, education, politics, and real estate. Typically, when you read a newspaper article or watch a news program on television, you are given sample information. With this information, you may make a decision about the correctness of a statement, claim, or "fact." Statistical methods can help you make the "best educated guess."

You are probably asking yourself the question, "When and where will I use statistics?". If you read any newspaper or watch television, or use the Internet, you will see statistical information. There are statistics about crime, sports, education, politics, and real estate. Typically, when you read a newspaper article or watch a news program on television, you are given sample information. With this information, you may make a decision about the correctness of a statement, claim, or "fact." Statistical methods can help you make the "best educated guess."

Table of Contents
1 Sampling and Data

1.1 Sampling and Data: Introduction
1.2 Sampling and Data: Statistics
1.3 Sampling and Data: Key Terms
1.4 Sampling and Data: Data
1.5 Sampling and Data: Variation and Critical Evaluation
1.6 Sampling and Data: Frequency, Relative Frequency, and Cumulative Frequency
2 Descriptive Statistics

2.1 Descriptive Statistics: Introduction
2.2 Descriptive Statistics: Displaying Data
2.3 Descriptive Statistics: Histogram
2.4 Descriptive Statistics: Measuring the Center of the Data
2.5 Descriptive Statistics: Skewness and the Mean, Median, and Mode
2.6 Descriptive Statistics: Measuring the Spread of the Data
3 The Normal Distribution

3.1 Normal Distribution: Introduction
3.2 Normal Distribution: Standard Normal Distribution
3.3 Normal Distribution: Z-scores
3.4 Normal Distribution: Areas to the Left and Right of x
3.5 Normal Distribution: Calculations of Probabilities
3.6 Central Limit Theorem: Central Limit Theorem for Sample Means
3.7 Central Limit Theorem: Using the Central Limit Theorem
4 Confidence Interval

4.1 Confidence Intervals: Introduction
4.2 Confidence Intervals: Confidence Interval, Single Population Mean, Population Standard Deviation Known, Normal
4.3 Confidence Intervals: Confidence Interval, Single Population Mean, Standard Deviation Unknown, Student's-t
4.4 Confidence Intervals: Confidence Interval for a Population Proportion
5 Hypothesis Testing

5.1 Hypothesis Testing of Single Mean and Single Proportion: Introduction
5.2 Hypothesis Testing of Single Mean and Single Proportion: Null and Alternate Hypotheses
5.3 Hypothesis Testing of Single Mean and Single Proportion: Using the Sample to Test the Null Hypothesis
5.4 Hypothesis Testing of Single Mean and Single Proportion: Decision and Conclusion
6 Linear Regression and Correlation

6.1 Linear Regression and Correlation: Introduction
6.2 Linear Regression and Correlation: Linear Equations
6.3 Linear Regression and Correlation: Slope and Y-Intercept of a Linear Equation
6.4 Linear Regression and Correlation: Scatter Plots
6.5 Linear Regression and Correlation: The Regression Equation
6.6 Linear Regression and Correlation: Correlation Coefficient and Coefficient of Determination
6.7 Linear Regression and Correlation: Testing the Significance of the Correlation Coefficient
6.8 Linear Regression and Correlation: Prediction

Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example: The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science". A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions. Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Netflix to the Office of Management and Budget. The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.

This is a review of  Probability And Its Applications To Reliability, Quality Control, And Risk Assessment Course: https://louis.oercommons.org/courses/probability-and-its-applications-to-reliability-quality-control-and-risk-assessment-fall-2005 completed by Dr. Esperanza Zenon, River Parishes Community College. This rubric was developed by BCcampus. This work is licensed under a Creative Commons Attribution 3.0 Unported license.The rubric allows reviewers to evaluate OER textbooks using a consistent set of criteria. Reviewers are encouraged to remix this rubric and add their review content within this tool. If you remix this rubric for an evaluation, please add the title to the evaluated content and link to it from your review.

Interpretations of the concept of probability. Basic probability rules; random variables and distribution functions; functions of random variables. Applications to quality control and the reliability assessment of mechanical/electrical components, as well as simple structures and redundant systems. Elements of statistics. Bayesian methods in engineering. Methods for reliability and risk assessment of complex systems, (event-tree and fault-tree analysis, common-cause failures, human reliability models). Uncertainty propagation in complex systems (Monte Carlo methods, Latin Hypercube Sampling). Introduction to Markov models. Examples and applications from nuclear and chemical-process plants, waste repositories, and mechanical systems. Open to qualified undergraduates.

This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.

Probability and Statistics, besides being a key area in the secondary schools’ teaching syllabuses, it forms an important background to advanced mathematics at tertiary level. Statistics is a fundamental area of Mathematics that is applied across many academic subjects and is useful in analysis in industrial production. The study of statistics produces statisticians that analyse raw data collected from the field to provide useful insights about a population. The statisticians provide governments and organizations with concrete backgrounds of a situation that helps managers in decision making. For example, rate of spread of diseases, rumours, bush fires, rainfall patterns, and population changes.

This is an Internet-based probability and statistics E-Book. The materials, tools and demonstrations presented in this E-Book would be very useful for advanced-placement (AP) statistics educational curriculum. The E-Book is initially developed by the UCLA Statistics Online Computational Resource (SOCR). However, all statistics instructors, researchers and educators are encouraged to contribute to this project and improve the content of these learning materials.
There are 4 novel features of this specific Statistics EBook. It is community-built, completely open-access (in terms of use and contributions), blends information technology, scientific techniques and modern pedagogical concepts, and is multilingual.

This is a review of Probability and Statistics EBook: https://louis.oercommons.org/courses/ap-statistics-curriculum-2007 completed by Dr. Esperanza Zenon, River Parishes Community College.This rubric was developed by BCcampus. This work is licensed under a Creative Commons Attribution 3.0 Unported license.The rubric allows reviewers to evaluate OER textbooks using a consistent set of criteria. Reviewers are encouraged to remix this rubric and add their review content within this tool. If you remix this rubric for an evaluation, please add the title to the evaluated content and link to it from your review.

Quantitative analysis of uncertainty and risk for engineering applications. Fundamentals of probability, random processes, statistics, and decision analysis. Random variables and vectors, uncertainty propagation, conditional distributions, and second-moment analysis. Introduction to system reliability. Bayesian analysis and risk-based decision. Estimation of distribution parameters, hypothesis testing, and simple and multiple linear regressions. Poisson and Markov processes. Emphasis on application to engineering problems.

" This course develops logical, empirically based arguments using statistical techniques and analytic methods. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice."