Variable Selection, Model Validation, Nonlinear Regression from Coursera

If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.

This course will focus on getting you acquainted with the generalized linear model (GLM) through the examples of logistic and Poisson regression. You will also see how simple and multiple linear regression relates to GLM using the link function. We will also study a regression technique that is robust to having outliers in the data. Finally, we will learn how to perform model validation involving GLM.

After this course, students will be able to:

- Determine which regression models to use based on the nature of the response variable.

- Use regression technique which is robust to the presence of outliers.

- Perform generalized linear regression using R by identifying the correct link function.

- Interpret and draw conclusions on the regression model.

- Use R to perform statistical inference based on the regression models.

What's inside

Syllabus

Module 1: Logistic Regression

In this module, you will learn the differences between logistic regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, and use R to compute the estimators of a linear regression model and give a probabilistic prediction of Y=1 given X=x’s. There is a lot to read, watch, and consume in this module so, let’s get started!

Module 2: Poisson Regression and Generalized Linear Model

In this module, you will learn the difference between Poisson regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, use R to compute the estimators of a Poisson regression model and the generalized linear model, and the similarities between the linear, logistic, and Poisson regressions. There is a lot to read, watch, and consume in this module so, let’s get started!

Module 3: Robust Regression and Model Validation

In this module, you will learn how to modify the ordinary least squares method to make the regression model more robust to the effect of outliers and use R to compute the robust regression parameters using different M-estimators and perform model validations involving logistic regression. There is a lot to read, watch, and consume in this module so, let’s get started!

Summative Course Assessment

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for those with a technical background, such as mathematics, statistics, computer science, and engineering

Provides examples of logistic and Poisson regression

Covers generalized linear models and their relationship to linear regression

Instructs on the maximum likelihood method for parameter estimation

Implements model validation using R

Might require prerequisite knowledge in some topics

Career center

Learners who complete Variable Selection, Model Validation, Nonlinear Regression will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians design, conduct, analyze, and interpret statistical studies. This course can help you gain the skills necessary to design and conduct statistical studies, analyze data, and interpret results. This course can also help you build a foundation in generalized linear models, which are commonly used in statistical studies.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course can help you gain the skills necessary to collect, clean, and analyze data, and use generalized linear models to make predictions and draw conclusions.

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Financial Analyst

Financial analysts make investment recommendations and provide advice to clients. This course can help you gain the skills necessary to analyze financial data and make informed investment decisions.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and develop insurance products. This course can help you gain the skills necessary to assess risk and develop insurance products, particularly in the context of non-life insurance.

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Operations Research Analyst

Operations research analysts use mathematical and statistical techniques to solve business problems. This course can help you gain the skills necessary to solve business problems, particularly in the context of supply chain management and logistics.

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Market Research Analyst

Market research analysts collect and analyze data to help businesses understand their customers and make informed marketing decisions. This course can help you gain the skills necessary to collect and analyze data, and use generalized linear models to make predictions and draw conclusions.

See salaries and explore the career path for Market Research Analyst

Epidemiologist

Epidemiologists investigate the causes of disease and develop strategies to prevent and control it. This course can help you gain the skills necessary to investigate the causes of disease and develop strategies to prevent and control it, particularly in the context of non-communicable diseases.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians apply statistical methods to solve problems in biology and medicine. This course can help you gain the skills necessary to apply statistical methods to solve problems in biology and medicine, particularly in the context of clinical trials.

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Software Engineer

Software engineers design, develop, and test computer software. This course may be helpful for software engineers who want to gain a better understanding of statistical methods and how they can be used to improve software design and development.

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Data Scientist

Data scientists use data to solve business problems. This course may be helpful for data scientists who want to gain a better understanding of statistical methods and how they can be used to solve business problems.

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Quantitative Analyst

Quantitative analysts use mathematical and statistical techniques to develop financial models. This course may be helpful for quantitative analysts who want to gain a better understanding of statistical methods and how they can be used to develop financial models.

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Business Analyst

Business analysts help businesses improve their operations and make better decisions. This course may be helpful for business analysts who want to gain a better understanding of statistical methods and how they can be used to improve business operations and make better decisions.

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Risk Manager

Risk managers identify and assess risks, and develop strategies to mitigate them. This course may be helpful for risk managers who want to gain a better understanding of statistical methods and how they can be used to identify, assess, and mitigate risks.

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Economist

Economists study how people make decisions and how those decisions affect the economy. This course may be helpful for economists who want to gain a better understanding of statistical methods and how they can be used to study economic data and make economic predictions.

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Teacher

Teachers help students learn and grow. This course may be helpful for teachers who want to gain a better understanding of statistical methods and how they can be used to evaluate teaching methods and student learning.

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Variable Selection, Model Validation, Nonlinear Regression

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