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Introduction to Statistical Analysis: Hypothesis Testing

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Free Online Course: Introduction to Statistical Analysis: Hypothesis Testing provided by Coursera is a comprehensive online course, which lasts for 3 weeks long, 10 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from Coursera. Introduction to Statistical Analysis: Hypothesis Testing is taught by Jordan Bakerman.

SAS via Coursera
3 weeks long, 10 hours worth of material
Upcoming
Overview

  • This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

Syllabus

    • Course Overview and Data Setup
      • In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.
    • Introduction and Review of Concepts
      • In this module you learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. Then you review fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals. After reviewing these concepts, you apply one-sample and two-sample t tests to data to confirm or reject preconceived hypotheses.
    • ANOVA and Regression
      • In this module you learn to use graphical tools that can help determine which predictors are likely or unlikely to be useful. Then you learn to augment these graphical explorations with correlation analyses that describe linear relationships between potential predictors and our response variable. After you determine potential predictors, tools like ANOVA and regression help you assess the quality of the relationship between the response and predictors.
    • More Complex Linear Models
      • In this module you expand the one-way ANOVA model to a two-factor analysis of variance and then extend simple linear regression to multiple regression with two predictors. After you understand the concepts of two-way ANOVA and multiple linear regression with two predictors, you'll have the skills to fit and interpret models with many variables.

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