Inferential Statistics

Duke University via Coursera

Coursera based on 2,733 ratings

Likes

Overview

Inferential Statistics is a fundamental course that teaches how to make data-driven decisions and draw conclusions about populations based on sample data. Unlike descriptive statistics, which summarize data, inferential statistics helps in making predictions, testing hypotheses, and estimating parameters, which are crucial for research and data analysis across many fields. The course covers key concepts such as probability distributions, sampling methods, confidence intervals, and hypothesis testing. Learners will explore statistical tests like t-tests, chi-square tests, ANOVA, and regression analysis to determine...

Syllabus

About the Specialization and the Course

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Inferential Statistics. Please take several minutes to browse them through. Thanks for joining us in this course!

Central Limit Theorem and Confidence Interval

Welcome to Inferential Statistics! In this course we will discuss Foundations for Inference. Check out the learning objectives, start watching the videos, and finally work on the quiz and the labs of this week. In addition to videos that introduce new concepts, you will also see a few videos that walk you through application examples related to the week's topics. In the first week we will introduce Central Limit Theorem (CLT) and confidence interval.

Inference and Significance

Welcome to Week Two! This week we will discuss formal hypothesis testing and relate testing procedures back to estimation via confidence intervals. These topics will be introduced within the context of working with a population mean, however we will also give you a brief peek at what's to come in the next two weeks by discussing how the methods we're learning can be extended to other estimators. We will also discuss crucial considerations like decision errors and statistical vs. practical significance. The labs for this week will illustrate concepts of sampling distributions and confidence levels.

Inference for Comparing Means

Welcome to Week Three of the course! This week we will introduce the t-distribution and comparing means as well as a simulation based method for creating a confidence interval: bootstrapping. If you have questions or discussions, please use this week's forum to ask/discuss with peers.

Inference for Proportions

Welcome to Week Four of our course! In this unit, well discuss inference for categorical data. We use methods introduced this week to answer questions like What proportion of the American public approves of the job of the Supreme Court is doing? Also in this week you will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.