In this section, you will be introduced to the process of performing a hypothesis test using a four-step testing procedure to test a population proportion.

In the previous section, you were introduced to the concepts testing a population proportion. In this section, we will extend this to testing population means.

In this section, you will be introduced to the process of performing a hypothesis test using a four-step testing procedure for two-sample testing. Specifically, you will learn how to perform the large sample test for the difference in population proportions.

In this section, you will learn the process of testing the difference in population means. Specifically, when the population variances are unknown and unequal utilizing the t-statistic.

You will learn the difference between independent and dependent samples. In the previous section, you learned to test two independent samples. In this section, you’ll learn how to test dependent samples, known as a paired t test.

In this section, you will learn about the chi-square goodness of fit test to analyze probabilities of a multinomial distribution.

In this section, you will learn about experimental design – a plan and a structure to test hypotheses where the researcher either controls or manipulates one or more variables. One of the simplest experimental designs is the completely randomized design. In the completely randomized design, subjects are assigned randomly to treatments. The completely randomized design contains only one independent variable, with two or more treatment levels, or classifications. In this module, you will learn how to analyze such an experiment using analysis of variance (ANOVA).

In this section, you will learn how to perform multiple comparison procedures to conduct comparisons of the groups when there are more than two levels of the independent variable. Specifically, the Tukey-Kramer procedure.

The addition rule for probability is explained using Venn Diagrams. [Probability playlist: Lesson 3 of 29]

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.

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.

I have used this project successfully with community college students and dual credit high school students. I assign the project at the end of the first week and have regular checkpoints. Details are described as you read through the project.  

The objectives of this course are as follows: Demonstrate an understanding of graphical representations of data and their interpretation; Demonstrate a competency in mathematical tools of decision making, including derivatives and analytical optimization; Demonstrate an understanding of descriptive statistics, hypothesis testing, and the theory of regression; Demonstrate competency in the use of software used in quantitative analysis, including Excel tools and statistical software. This textbook is organized to support you in these goals. The textbook is adapted from Contemporary Calculus, written by Dale Hoffman from Bellevue Community College and Business Calculus written by Shana Calaway from Shoreline Community College. New material is written by Margo Bergman from University of Washington Tacoma.

Detection and measurement of radio and optical signals encountered in communications, astronomy, remote sensing, instrumentation, and radar. Statistical analysis of signal processing systems, including radiometers, spectrometers, interferometers, and digital correlation systems. Matched filters and ambiguity functions. Communications channel performance. Measurement of random electromagnetic fields. Angular filtering properties of antennas, interferometers, and aperture synthesis systems. Radiative transfer and parameter estimation.

This textbook is an adaptation of the Research Methods in Psychology that is available on this site in US and Canadian editions. This New Zealand edition is an adaptation to the New Zealand context. The main changes are in Chapters 1 and 3 and the spelling, grammar, and terminology are changed throughout. This textbook is adopted at the University of Waikato in our 200-level research methods in psychology class.

6.780 covers statistical modeling and the control of semiconductor fabrication processes and plants. Topics include design of experiments, response surface modeling, and process optimization; defect and parametric yield modeling; process/device/circuit yield optimization; monitoring, diagnosis, and feedback control of equipment and processes; analysis and scheduling of semiconductor manufacturing operations.

This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester of such a course. It is freely available under the Creative Commons License, and includes a software library in Python for making some of the calculations and visualizations easier.

This is a list of terms and their definitions related to statistics provided by the Australian Bureau of Statistics, an organ of the government of the Commonwealth of Australia.

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.