Mathematics explained: Here you find videos on various math topics:

Pre-university Calculus (functions, equations, differentiation and integration)
Vector calculus (preparation for mechanics and dynamics courses)
Differential equations, Calculus
Functions of several variables, Calculus
Linear Algebra
Probability and Statistics

Math in Society is a free, open textbook. This book is a survey of mathematical topics, most non-algebraic, appropriate for a college-level topics course for liberal arts majors. The text is designed so that most chapters are independent, allowing the instructor to choose a selection of topics to be covered. Emphasis is placed on the applicability of the mathematics. Material for each topic is covered in the main text, with additional depth available through exploration exercises appropriate for in-class, group, or individual investigation.

Lab and Homework Site available through Lumens OHM but requires an access code.

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 resource was created for Introduction to Statistics students at the University of Maryland, and is designed to help you explore psychological theory, research, and practical applications of statistics. After completing this course in psychology, you will be able to:

- Explain how to use and interpret descriptive and inferential statistics in an ethically responsible way.
- Describe the difference between descriptive (central tendency, dispersion, correlation) and inferential statistics (single, multiple, logistic), and know when to use each.
- Demonstrate analytical skills by critiquing research and media claims.
- Apply statistical concepts and methods in a way that improves your own academic, personal, and professional life.

Each module is structured around key prompts - Learning Objective Questions - and followed by the links to articles, videos, and interactive demonstrations you will need to answer those questions. After studying the readings, videos, and presentations you should be able to answer the learning objective questions in detail without any notes in front of you. If you practice doing that regularly, you are well prepared for any assessment that your instructor can give you!

" This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology)."

LEARNING OBJECTIVE: Identify and distinguish between a parameter and a statistic.

LEARNING OBJECTIVE: Explain the concepts of sampling variability and sampling distribution.

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 material is a detailed exercise for students in introductory statistics. Students are asked to collect a random sample of data from a real estate website; conduct descriptive statistics (including confidence intervals); and write a report summarizing their findings.

Dawson (1995) presented a data set giving a population at risk and fatalities for an “unusual episode” (the sinking of the ocean liner Titanic) and discussed the use of the data set in a first statistics course as an elementary exercise in statistical thinking, the goal being to deduce the origin of the data. Simonoff (1997) discussed the use of this data set in a second statistics course to illustrate logistic regression. Moore (2000) used an abbreviated form of the data set in a chapter exercise on the chi-square test. This article describes an activity that illustrates contingency table (two-way table) analysis. Students use contingency tables to analyze the “unusual episode” data (from Dawson 1995) and attempt to use their analysis to deduce the origin of the data. The activity is appropriate for use in an introductory college statistics course or in a high school AP statistics course.

As teachers of statistics, we know that residual plots and other diagnostics are important to deciding whether or not linear regression is appropriate for a set of data. Despite talking with our students about this, many students might believe that if the correlation coefficient is strong enough, these diagnostic checks are not important. The data set included in this activity was created to lure students into a situation that looks on the surface to be appropriate for the use of linear regression but is instead based (loosely) on a quadratic function.

This article describes an interactive activity illustrating general properties of hypothesis testing and hypothesis tests for proportions. Students generate, collect, and analyze data. Through simulation, students explore hypothesis testing concepts. Concepts illustrated are: interpretation of p-values, type I error rate, type II error rate, power, and the relationship between type I and type II error rates and power. This activity is appropriate for use in an introductory college or high school statistics course.

This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.

Introductory statistics course developed through the Ohio Department of Higher Education OER Innovation Grant. The course is part of the Ohio Transfer Module and is also named TMM010. For more information about credit transfer between Ohio colleges and universities please visit: www.ohiohighered.org/transfer.Team LeadKameswarrao Casukhela                     Ohio State University – LimaContent ContributorsEmily Dennett                                       Central Ohio Technical CollegeSara Rollo                                            North Central State CollegeNicholas Shay                                      Central Ohio Technical CollegeChan Siriphokha                                   Clark State Community CollegeLibrarianJoy Gao                                                Ohio Wesleyan UniversityReview TeamAlice Taylor                                           University of Rio GrandeJim Cottrill                                             Ohio Dominican University

Producing Data – Sampling MethodsIn this module we will explore the different sampling methods to obtain representative samples from a population. We also learn about the relative advantages and disadvantages of each method. Learning Objectives:Reasons for samplingRandom Vs. Non-Random SamplesSampling Bias and VariabilityRandom Sampling Methods – Simple, Stratified, Systematic, Cluster and Multistage random samplesNon-Random Sampling Methods – Voluntary Response and Convenience samplingSample surveys, sampling errorsBest method of random samplingSampling distributions

This module contains Excel and Google Spreadsheets for all statistical procedures used in an Intro Stats course. The spreadsheets are self-explanatory. Students insert data in the indicated areas. Spreadsheets are designed to automatically complete all calculations and show the results.One Variable Statistics Frequency DistributionDiscrete Probability DistributionNormal DistributionConfidence IntervalsTest of HypothesisLinear RegressionIndependence

The purpose of this course is to provide background in the ways in which psychologists evaluate data collected from research projects. A researcher may gather many pieces of data that describe a group of research subjects and there are common ways in which these pieces of information are presented. Secondly, statistical tests can help investigators draw inferences about the relationship of the research sample to the general population it is supposed to represent. As a student of psychology or any other discipline that uses research data to explore ideas, it is important that you know how data is evaluated and that you gain an understanding of the ways in which these procedures help to summarize and clarify data.