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
Statistical Inference for Estimation in Data Science
This course is part of Data Science Foundations: Statistical Inference Specialization
Instructor: Jem Corcoran
Access provided by New York State Department of Labor
7,930 already enrolled
Gain insight into a topic and learn the fundamentals.
4.1
(88 reviews)
Intermediate level
Recommended experience
26 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Identify characteristics of 鈥済ood鈥� 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
2 quizzes, 10 assignments
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 5 modules in this course
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 readings1 quiz3 assignments1 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 assignments1 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 鈥淐ram茅r鈥揜ao lower bound鈥� which gives us a benchmark for the smallest possible variance for an unbiased estimator.
What's included
5 videos5 readings2 assignments1 programming assignment1 ungraded lab
In this module we learn about the theory of 鈥渋nterval estimation鈥�. 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 readings1 quiz1 assignment1 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 assignments1 ungraded lab
Instructor
Instructor ratings
4.4 (30 ratings)Offered by
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DP
5
Reviewed on Jan 28, 2024
Excellent. Challenging quizzes that really make you apply the points from the lectures. Very detailed course that has taken me to the next level of my understanding of statistical inference.
IL
5
Reviewed on Sep 4, 2022
T鈥媓e instrustor, Dr. Jem, is really interesting. She made the hard part of the Statistics easy to understand!
AT
5
Reviewed on Jul 19, 2024
This course provided me with truly deep insights into the inner workings of statistics. Thank you very much.
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