NHANES Case Study Tutorial (Marginal and Multilevel Regression) - Fitting Statistical Models to
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NHANES Case Study Tutorial (Marginal and Multilevel Regression) - Fitting Statistical Models to Data with Python
Statistics with Python Specialization
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.
Bayesian Statistics, Python Programming, Statistical Model, statistical regression
Good course, but the last of three was the most difficult one. I hope that it were a good introduction to the fascinating world of statistics and data science,Awesome overview about what can we do with statictics knowlegde! Half theory, half practice with Python is a great format
In the third week of this course, we will be building upon the modeling concepts discussed in Week 2. Multilevel and marginal models will be our main topic of discussion, as these models enable researchers to account for dependencies in variables of interest introduced by study designs. We’ll be covering why and when we fit these alternative models, likelihood ratio tests, as well as fixed effects and their interpretations.
NHANES Case Study Tutorial (Marginal and Multilevel Regression) - Fitting Statistical Models to Data with Python
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