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# Regression Analysis: Simplify Complex Data Relationships

### Overview

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### Syllabus

• Introduction to complex data relationships
• Youâ€™ll begin by exploring the main steps for building regression models, from identifying your assumptions to interpreting your results. Next, youâ€™ll explore the two main types of regression: linear and logistic. Youâ€™ll learn how data professionals use linear and logistic regression to approach different kinds of business problems.
• Simple linear regression
• Youâ€™ll explore how to use models to describe complex data relationships. Youâ€™ll focus on relationships of correlation. Then, youâ€™ll build a simple linear regression model in Python and interpret your results.
• Multiple linear regression
• After simple regression, youâ€™ll move on to a more complex regression model: multiple linear regression. Youâ€™ll consider how multiple regression builds on simple linear regression at every step of the modeling process. Youâ€™ll also get a preview of some key topics in machine learning: selection, overfitting, and the bias-variance tradeoff.
• Youâ€™ll build on your prior knowledge of hypothesis testing to explore two more statistical tests: Chi-squared and analysis of variance (ANOVA). Youâ€™ll learn how data professionals use these tests to analyze different types of data. Finally, youâ€™ll conduct two kinds of Chi-squared tests, as well as one-way and two-way ANOVA tests.
• Logistic regression
• Youâ€™ll investigate binomial logistic regression, a type of regression analysis that classifies data into two categories. Youâ€™ll learn how to build a binomial logistic regression model and how data professionals use this type of model to gain insights from their data.
• Course 5 end-of-course project
• Youâ€™ll complete an end-of-course project by building a regression model to analyze a workplace scenario dataset.