This course covers descriptive statistics, the foundation of statistics, probability and random distributions, and the relationships between various characteristics of data. Upon successful completion of the course, the student will be able to: Define the meaning of descriptive statistics and statistical inference; Distinguish between a population and a sample; Explain the purpose of measures of location, variability, and skewness; Calculate probabilities; Explain the difference between how probabilities are computed for discrete and continuous random variables; Recognize and understand discrete probability distribution functions, in general; Identify confidence intervals for means and proportions; Explain how the central limit theorem applies in inference; Calculate and interpret confidence intervals for one population average and one population proportion; Differentiate between Type I and Type II errors; Conduct and interpret hypothesis tests; Compute regression equations for data; Use regression equations to make predictions; Conduct and interpret ANOVA (Analysis of Variance). (Mathematics 121; See also: Biology 104, Computer Science 106, Economics 104, Psychology 201)

This 4-minute video lesson continues to explore deductive reasoning (part 3). [Statistics playlist: Lesson 81 of 85]

This 2-minute video lesson continues to explore inductive reasoning (part 2). [Statistics playlist: Lesson 83 of 85]

This 15-minute video lesson explains the expected value of a random variable. [Statistics playlist: Lesson 23 of 85]

This 17-minute video lesson looks at the expected value of a binomial distributed random variable. [Statistics playlist: Lesson 24 of 85]

This 14-minute video lesson shows how to estimate the probability that the true population mean lies within a range around a sample mean .[Statistics playlist: Lesson 40 of 85]

This 8-minute video lesson gives an example of the mean and variance of Bernoulli Distribution. [Statistics playlist: Lesson 41 of 85]

This 11-minute video lesson looks at Hypothesis Testing and P-values. [Statistics playlist: Lesson 47 of 85]

This 16-minute video lesson looks at the hypothesis test for comparing population proportions. [Statistics playlist: Lesson 61 of 85]

This 13-minute video lesson looks at R-squared or the coefficient of determination. [Statistics playlist: Lesson 68 of 85]

This 9-minute video lesson provides a second regression example. [Statistics playlist: Lesson 69 of 85]

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.

This unit is concerned with two main topics. In Section 1, you will learn about another kind of graphical display, the boxplot. A boxplot is a fairly simple graphic, which displays certain summary statistics of a set of data. Boxplots are particularly useful for assessing quickly the location, dispersion, and symmetry or skewness of a set of data, and for making comparisons of these features in two or more data sets. Boxplots can also be useful for drawing attention to possible outliers in a data set. The other topic, which is covered in Sections 2 and 3, is that of dealing with data presented in tabular form. You are, no doubt, familiar with such tables: they are common in the media and in reports and other documents. Yet it is not always straightforward to see at first glance just what information a table of data is providing, and it often helps to carry out certain calculations and/or to draw appropriate graphs to make this clearer. In this unit, some other kinds of data tables and some different approaches are covered.

This course introduces statistical tools and techniques that are routinely used by modern statisticians for a wide variety of applications. Upon successful completion of this course, the student will be able to: apply statistical hypothesis testing for one population; conduct statistical hypothesis testing and estimation for two populations; apply multiple regression analysis to analyze a multivariate problem; analyze the outputs for a multiple regression model and interpret the regression results; conduct test hypotheses about the significance of a multiple regression model and test the significance of the independent variables in the model; select appropriate multiple regression models using automatic model selection, forward selection, backward elimination, and stepwise selection; recognize and address issues when using multiple regression analysis; identify situations when nonparametric tests are appropriate; conduct nonparametric tests; explain the principles underlying General Linear Model, Multilevel Modeling, Data Mining, Machine Learning, Bayesian Belief Networks, Neural Network, and Support Vector Machine. This free course may be completed online at any time. (Mathematics 251)

Student will conduct a coin tossing experiment for 30 trials. Their results will be graphed, showing a line graph that progresses toward the theoretical probability. Students will observe that as the number of trials increases they begin to see a graphical representation of the Law of Large Numbers. Instructions, handouts, and a lesson extension are all included here.

Want a different perspective of the world? Get a better, more friendly grasp of statistics -- one of the most understood and misused tools in modern society.

" 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)."

This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.