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This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.

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This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.

By the end of this course, you will:

-Explore the use of predictive models to describe variable relationships, with an emphasis on correlation

-Determine how multiple regression builds upon simple linear regression at every step of the modeling process

-Run and interpret one-way and two-way ANOVA tests

-Construct different types of logistic regressions including binomial, multinomial, ordinal, and Poisson log-linear regression models

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Syllabus

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

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches methods highly relevant in industry
Builds upon an existing foundation for intermediate learners
Taught by Google employees who currently work in the field
Examines methods highly relevant to business
Develops professional skills or deep expertise in data analytics
Requires learners to come in with extensive background knowledge first

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Regression Analysis: Simplify Complex Data Relationships with these activities:
Review Regression and Hypothesis Testing
Refreshing your knowledge of regression and hypothesis testing will provide a solid foundation for deeper exploration of advanced topics in this course.
Browse courses on Regression
Show steps
  • Review course notes from previous coursework on regression and hypothesis testing.
  • Work through practice problems to test your understanding.
  • Read articles or watch videos to refresh your knowledge.
Practice Regression Modeling in Python
Working through guided tutorials on regression modeling in Python will help you develop practical skills that can be applied in this course and beyond.
Browse courses on Regression Modeling
Show steps
  • Find guided tutorials on Python libraries for regression modeling.
  • Follow the tutorials step-by-step to build regression models using real-world data.
  • Experiment with different parameters and settings to observe their effects on model performance.
Collaborate on Regression Modeling Projects
Collaborating on regression modeling projects with peers will foster teamwork skills and provide diverse perspectives on problem-solving.
Browse courses on Regression Modeling
Show steps
  • Find a group of peers with similar interests in regression modeling.
  • Choose a project to work on together, such as building a model for a real-world dataset.
  • Divide tasks among group members and work together to complete the project.
Five other activities
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Attend a Workshop on Logistic Regression
Attending a workshop on logistic regression will provide in-depth knowledge and practical skills in this specific regression technique.
Browse courses on Logistic Regression
Show steps
  • Find a workshop on logistic regression that aligns with your skill level.
  • Participate actively in the workshop, ask questions, and engage with the instructor.
  • Apply the techniques learned in the workshop to your own projects or assignments.
Solve Regression and Hypothesis Testing Problems
Solving practice drills on regression and hypothesis testing will enhance your problem-solving skills and solidify your understanding of key concepts.
Browse courses on Regression
Show steps
  • Find practice problems or test papers on regression and hypothesis testing.
  • Attempt to solve the problems independently.
  • Check your solutions against provided answer keys or consult with experts.
Read 'Regression Analysis by Example' by Chatterjee and Hadi
Reading 'Regression Analysis by Example' will provide a comprehensive understanding of regression concepts and techniques.
Show steps
  • Read the book thoroughly to grasp the fundamentals of regression analysis.
  • Work through the examples and exercises to reinforce your understanding.
  • Apply the concepts to real-world data analysis scenarios.
Develop a Regression Model for a Business Problem
Creating a regression model for a business problem will allow you to apply the concepts learned in this course to a practical scenario.
Browse courses on Regression Modeling
Show steps
  • Identify a business problem that can be addressed using regression modeling.
  • Gather and prepare the necessary data.
  • Build a regression model using appropriate techniques.
  • Interpret the model results and draw insights for decision-making.
Contribute to Open-Source Regression Libraries
Contributing to open-source regression libraries will give you hands-on experience with real-world regression modeling tools.
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Show steps
  • Identify an open-source regression library that interests you.
  • Find a feature or bug that you can contribute to.
  • Fork the repository, make your changes, and submit a pull request.

Career center

Learners who complete Regression Analysis: Simplify Complex Data Relationships will develop knowledge and skills that may be useful to these careers:
Market Researcher
Market Researchers collect, analyze, and interpret data to understand market trends and consumer behavior. The Regression Analysis course can be beneficial for individuals in this field as it provides a comprehensive understanding of statistical modeling techniques. By learning how to identify relationships between variables and build predictive models, Market Researchers can gain valuable insights into consumer behavior and market dynamics, enhancing their ability to make informed decisions and develop effective marketing strategies.
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. The Regression Analysis course can provide a comprehensive understanding of statistical modeling and hypothesis testing to aspiring Statisticians. By learning how to build and evaluate regression models, Statisticians can gain the skills to analyze data, draw inferences, and make informed conclusions, enhancing their ability to contribute to research and decision-making.
Data Scientist
Data Scientists use advanced statistical techniques and machine learning algorithms to extract insights from data. The Regression Analysis course can provide a strong foundation for aspiring Data Scientists by introducing them to statistical modeling and its application in real-world scenarios. By learning how to build and evaluate regression models, Data Scientists can gain the skills to analyze data, identify patterns, and make predictions, increasing their value in the data science field.
Business Analyst
Business Analysts use data to understand business processes and identify opportunities for improvement. The Regression Analysis course can be valuable for Business Analysts as it provides a comprehensive understanding of statistical modeling and its application in business scenarios. By learning how to build and interpret regression models, Business Analysts can gain the skills to analyze data, identify key factors influencing business outcomes, and make data-driven recommendations for improvement.
Data Analyst
Data Analysts examine and interpret data to extract meaningful insights. This course in Regression Analysis can help students develop foundational skills in statistical modeling and hypothesis testing, which are essential for success in this field. By learning how to build and evaluate regression models, learners can gain the ability to analyze complex data relationships and draw informed conclusions, a valuable skill for any Data Analyst.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Regression Analysis course can provide a solid foundation for aspiring Quantitative Analysts by introducing them to statistical modeling and hypothesis testing. By mastering these techniques, learners can develop the skills necessary to analyze financial data, identify patterns, and build predictive models, enhancing their ability to make informed investment decisions.
Financial Analyst
Financial Analysts use data analysis to evaluate and forecast financial performance. The Regression Analysis course can provide a strong foundation for aspiring Financial Analysts by introducing them to statistical modeling and hypothesis testing. By mastering these techniques, learners can develop the skills necessary to analyze financial data, identify trends, and make informed recommendations, increasing their value in the financial industry.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to optimize business processes and improve decision-making. The Regression Analysis course can provide a strong foundation for aspiring Operations Research Analysts by introducing them to statistical modeling and its application in business scenarios. By learning how to build and interpret regression models, Operations Research Analysts can gain the skills to analyze data, identify key factors influencing business outcomes, and develop data-driven recommendations for improvement.
Survey Researcher
Survey Researchers design, conduct, and analyze surveys to collect and analyze data. The Regression Analysis course can benefit Survey Researchers by providing them with a comprehensive understanding of statistical modeling and hypothesis testing. By learning how to build and interpret regression models, Survey Researchers can gain the skills to analyze survey data, identify relationships between variables, and draw informed conclusions, enhancing the quality and impact of their research.
Risk Analyst
Risk Analysts use statistical techniques to assess and manage risks. The Regression Analysis course can provide a strong foundation for aspiring Risk Analysts by introducing them to statistical modeling and hypothesis testing. By mastering these techniques, learners can develop the skills necessary to analyze data, identify patterns, and build predictive models, enhancing their ability to evaluate risks and make informed decisions.
Actuary
Actuaries use mathematical and statistical techniques to assess and manage financial risks. The Regression Analysis course can provide a strong foundation for aspiring Actuaries by introducing them to statistical modeling and hypothesis testing. By mastering these techniques, learners can develop the skills necessary to analyze financial data, identify patterns, and build predictive models, enhancing their ability to assess risks and make informed decisions.
Epidemiologist
Epidemiologists investigate the distribution and determinants of health-related states and events in defined populations. The Regression Analysis course can provide a strong foundation for aspiring Epidemiologists by introducing them to statistical modeling and hypothesis testing. By learning how to build and interpret regression models, Epidemiologists can gain the skills to analyze data, identify factors influencing health outcomes, and draw informed conclusions, enhancing their ability to develop and implement public health interventions.
Biostatistician
Biostatisticians apply statistical methods to analyze biological and health-related data. The Regression Analysis course can provide a strong foundation for aspiring Biostatisticians by introducing them to statistical modeling and hypothesis testing. By learning how to build and interpret regression models, Biostatisticians can gain the skills to analyze data, identify relationships between health-related variables, and draw informed conclusions, enhancing their contributions to research and healthcare decision-making.
Econometrician
Econometricians use statistical techniques to analyze economic data. The Regression Analysis course can provide a foundation for aspiring Econometricians by introducing them to statistical modeling and hypothesis testing. By learning how to build and evaluate regression models, Econometricians can develop the skills necessary to analyze economic data, identify relationships between economic variables, and draw informed conclusions.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. The Regression Analysis course may provide a useful foundation for aspiring Machine Learning Engineers by introducing them to statistical modeling and hypothesis testing. By learning how to build and evaluate regression models, Machine Learning Engineers can gain a better understanding of the underlying principles of machine learning algorithms and their application in real-world scenarios.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Regression Analysis: Simplify Complex Data Relationships.
Provides a comprehensive overview of regression models used in actuarial and financial applications. It covers a wide range of topics, including linear and nonlinear regression, model selection, and forecasting.
Provides a comprehensive introduction to statistical learning, including regression models. It is written in a clear and concise style, and it is suitable for both beginners and experienced learners.
Provides a practical guide to regression analysis. It covers a wide range of topics, including linear and nonlinear regression, model selection, and forecasting. It is written in a clear and concise style, and it is suitable for both beginners and experienced learners.
Provides a comprehensive overview of regression analysis and generalized linear models. It is written in a clear and concise style, and it is suitable for both beginners and experienced learners.
Provides a comprehensive overview of econometrics. It covers a wide range of topics, including regression analysis, time series analysis, and forecasting.
Provides a practical guide to regression analysis using the R statistical software. It covers a wide range of topics, including linear and nonlinear regression, model selection, and forecasting.
Provides a practical guide to regression analysis using the Python programming language. It covers a wide range of topics, including linear and nonlinear regression, model selection, and forecasting.
Provides a practical guide to regression analysis using the Stata statistical software. It covers a wide range of topics, including linear and nonlinear regression, model selection, and forecasting.

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