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$79

Fitting Statistical Models to Data with Python

Created by -

Brenda Gunderson,Brady T. West,Kerby Shedden
,
University of Michigan

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(0 ratings)

English

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Overview

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

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USD 79

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Type: Online

This course includes

  • Approx. 19 hours to complete
  • Earn a Certificate upon completion
  • Start instantly and learn at your own schedule.

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course image

USD 79

provider image

Type: Online

This course includes

  • Approx. 19 hours to complete
  • Earn a Certificate upon completion
  • Start instantly and learn at your own schedule.

Taken this course?

Share your experience with other students

Share

Add Review

Fitting Statistical Models to Data with Python

Created by -

Brenda Gunderson,Brady T. West,Kerby Shedden
,
University of Michigan

0.00

(0 ratings)

All Levels

Start Date: February 10th 2021

Course Description

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

The information used on this page is how each course is described on the Coursera platform.

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Brenda Gunderson,Brady T. West,Kerby Shedden,University of Michigan

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