This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings.
This course is part of the Data Science Foundations: Statistical Inference Specialization

About this Course
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
What you will learn
Identify characteristics of “good” estimators and be able to compare competing estimators.
Construct sound estimators using the techniques of maximum likelihood and method of moments estimation.
Construct and interpret confidence intervals for one and two population means, one and two population proportions, and a population variance.
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Offered by
Start working towards your Master's degree
Syllabus - What you will learn from this course
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Point Estimation
Maximum Likelihood Estimation
Large Sample Properties of Maximum Likelihood Estimators
Confidence Intervals Involving the Normal Distribution
About the Data Science Foundations: Statistical Inference Specialization

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