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.
Statistical Inference for Estimation in Data Science
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Statistical Inference for Estimation in Data Science
This course is part of Data Science Foundations: Statistical Inference Specialization
Instructor: Jem Corcoran
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4.1
(81 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 28 hours
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What you'll 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.
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Assessments
12 quizzes
Taught in English
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This course is part of the Data Science Foundations: Statistical Inference Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
Welcome to the course! This module contains logistical information to get you started!
What's included
1 video4 readings1 ungraded lab
In this module you will learn how to estimate parameters from a large population based only on information from a small sample. You will learn about desirable properties that can be used to help you to differentiate between good and bad estimators. We will review the concepts of expectation, variance, and covariance, and you will be introduced to a formal, yet intuitive, method of estimation known as the "method of moments".
What's included
10 videos11 readings4 quizzes1 programming assignment1 ungraded lab
In this module we will learn what a likelihood function is and the concept of maximum likelihood estimation. We will construct maximum likelihood estimators (MLEs) for one and two parameter examples and functions of parameters using the invariance property of MLEs.
What's included
5 videos5 readings2 quizzes1 programming assignment1 ungraded lab
In this module we will explore large sample properties of maximum likelihood estimators including asymptotic unbiasedness and asymptotic normality. We will learn how to compute the “Cramér–Rao lower bound” which gives us a benchmark for the smallest possible variance for an unbiased estimator.
What's included
5 videos5 readings2 quizzes1 programming assignment1 ungraded lab
In this module we learn about the theory of “interval estimation”. We will learn the definition and correct interpretation of a confidence interval and how to construct one for the mean of an unseen population based on both large and small samples. We will look at the cases where the variance is known and unknown.
What's included
5 videos5 readings2 quizzes1 programming assignment2 ungraded labs
In this module, we will generalize the lessons of Module 4 so that we can develop confidence intervals for other quantities of interest beyond the distribution mean and for other distributions entirely. This module covers two sample confidence intervals in more depth, and confidence intervals for population variances and proportions. We will also learn how to develop confidence intervals for parameters of interest in non-normal distributions.
What's included
5 videos5 readings2 quizzes1 ungraded lab
Instructor
Instructor ratings
4.3 (27 ratings)Offered by
Recommended if you're interested in Probability and Statistics
University of Colorado Boulder
University of Colorado Boulder
University of Colorado Boulder
University of Colorado Boulder
Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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