## Data Analysis Tools

Start Date: 08/09/2020

 Course Type: Common Course

In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.

#### Course Syllabus

This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically. The first group of videos describe the process of hypothesis testing which you will use throughout this course to test relationships between different kinds of variables (quantitative and categorical). Next, we show you how to test hypotheses in the context of Analysis of Variance (when you have one quantitative variable and one categorical variable). Your task will be to write a program that manages any additional variables you may need and runs and interprets an Analysis of Variance test. Note that if your research question does not include one quantitative variable, you can use one from your data set just to get some practice with the tool. If your research question does not include a categorical variable, you can categorize one that is quantitative.

#### Course Introduction

Data Analysis Tools In this course, we’ll focus on the use of data to inform decision making. We’ll cover statistical techniques for independence testing (I&D), univariate logistic regression, and multivariate Poisson regression. You’ll learn about the different types of I&D, namely clustered, univariate, and mixed effects. We’ll also cover basic multivariate Poisson regression, which is the technique used to fit the Poisson distribution. We’ll use an example case to demonstrate the concepts. We’ll also cover some advanced techniques for independence testing. You’ll learn about noise in regression, the use of different regression models, and independence testing. An important component of independence testing is the use of a series of t-tests for equality. You’ll learn about these statistical tests and the appropriate strategy for using them. You’ll also learn about Poisson regression, which is the technique we use to fit the Poisson distribution. We’ll use an example case to demonstrate the concepts. We’ll also cover some advanced techniques for independence testing. You’ll learn about noise in regression, the use of different regression models, and independence testing. An important component of independence testing is the use of a series of t-tests for equality. You’ll learn about these statistical tests and the appropriate strategy for using them. You’ll also learn about Poisson regression, which is

#### Course Tag

Chi-Squared (Chi-2) Distribution Data Analysis Statistical Hypothesis Testing Analysis Of Variance (ANOVA)

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