Familiarize yourself with the core set of skills in statistical inference necessary to understand, interpret, and tune your statistical & machine learning models. 

A core set of skills in statistical inference necessary to understand, interpret, and tune your statistical & machine learning models. 

with R

Statistical Inference

In this chapter, you will investigate how repeated samples taken from a population can vary.  It is the variability in samples that allow you to make claims about the population of interest.  It is important to remember that the research claims of interest focus on the population while the information available comes only from the sample data.

Introduction to ideas of inference

In this chapter, you will gain the tools and knowledge to complete a full hypothesis test.  That is, given a dataset, you will know whether or not is appropriate to reject the null hypothesis in favor of the research claim of interest.

Completing a randomization test: gender discrimination

You will continue learning about hypothesis testing with a new example and the same structure of randomization tests.  In this chapter, however, the focus will be on different errors (type I and type II), how they are made, when one is worse than another, and how things like sample size and effect size impact the error rates.

Hypothesis testing errors: opportunity cost

As a complement to hypothesis testing, confidence intervals allow you to estimate a population parameter.  Recall that your interest is always in some characteristic of the population, but you only have incomplete information to estimate the parameter using sample data.  Here, the parameter is the true proportion of successes in a population.  Bootstrapping is used to estimate the variability needed to form the confidence interval.

Confidence intervals

All polls

Polling data

Big discrimination dataset

New discrimination dataset

Small discrimination dataset

One of the foundational aspects of statistical analysis is inference, or the process of drawing conclusions about a larger population from a sample of data. Although counter intuitive, the standard practice is to attempt to disprove a research claim that is not of interest. For example, to show that one medical treatment is better than another, we can assume that the two treatments lead to equal survival rates only to then be disproved by the data. Additionally, we introduce the idea of a p-value, or the degree of disagreement between the data and the hypothesis. We also dive into confidence intervals, which measure the magnitude of the effect of interest (e.g. how much better one treatment is than another).

Correlation and Regression in R

Learn the practice of drawing conclusions about a population from a sample of data, a process known as statistical inference. 

Foundations of Inference in R

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Foundations of Inference

Probability & Statistics

In this chapter you will learn how to perform statistical inference on a single parameter that describes categorical data. This includes both resampling based methods and approximation based methods for a single proportion.

Inference for a single parameter

This chapter dives deeper into performing hypothesis tests and creating confidence intervals for a single parameter. Then, you'll learn how to perform inference on a difference between two proportions. Finally, this chapter wraps up with an exploration of what happens when you know the null hypothesis is true.

Proportions: testing and power

This part of the course will teach you how to use both resampling methods and classical methods to test for the indepence of two categorical variables. This chapter covers how to perform a Chi-squared test.

Comparing many parameters: independence

The course wraps up with two case studies using election data. Here, you'll learn how to use a Chi-squared test to check goodness-of-fit. You'll study election results from Iran and Iowa and test if Benford's law applies to these datasets.

Comparing many parameters: goodness of fit

GSS data

Iowa election data

Iran election data

Categorical data is all around us. It's in the latest opinion polling numbers, in the data that lead to new breakthroughs in genomics, and in the troves of data that internet companies collect to sell products to you. In this course you'll learn techniques for parsing the signal from the noise; tools for identifying when structure in this data represents interesting phenomena and when it is just random noise.

In this course you'll learn how to leverage statistical techniques for working with categorical data.

Inference for Categorical Data in R

In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.

Bootstrapping for estimating a parameter

In this chapter you'll use Central Limit Theorem based techniques to estimate a single parameter from a numerical distribution. You will do this using the t-distribution.

Introducing the t-distribution

In this chapter you'll extend what you have learned so far to use both simulation and CLT based techniques for inference on the difference between two parameters from two independent numerical distributions.

Inference for difference in two parameters

In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.

Comparing many means

Chp1-vid1-boot-dist-noaxes-parantheses

Chp1-vid1-bootsamp-bootpop.001

Chp1-vid1-manhattan-rents

Chp1-vid2-boot-dist-withaxes

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Chp1-vid2-perc-method.002

Chp1-vid3-boot-test.001

Chp3-vid3-hrly-rate-citizen-smaller

Chp3-vid3-hrly-rate-citizen

Chp4-vid1-class-bar

Chp4-vid1-wodrsum-hist

Gss moredays

Manhattan rent data

Runners.001

Tdistcomparetonormaldist

In this course, you'll learn how to use statistical techniques to make inferences and estimations using numerical data. This course uses two approaches to these common tasks. The first makes use of bootstrapping and permutation to create resample based tests and confidence intervals. The second uses theoretical results and the t-distribution to achieve the same result. You'll learn how (and when) to perform a t-test, create a confidence interval, and do an ANOVA!

In this course you'll learn techniques for performing statistical inference on numerical data, like the bootstrap, permutation tests, t-tests, and ANOVA.

Inference for Numerical Data in R

In this course you'll learn techniques for performing statistical inference on numerical data.

In the first chapter, you will understand how and why to perform inferential (instead of descriptive only) analysis on a regression model.

Inferential ideas

In this chapter you will learn about the ideas of the sampling distribution using simulation methods for regression models.

Simulation-based inference for the slope parameter

In this chapter you will learn about how to use the t-distribution to perform inference in linear regression models. You will also learn about how to create prediction intervals for the response variable.

t-Based Inference For the Slope Parameter

Additionally, you will consider the technical conditions that are important when using linear models to make claims about a larger population.

Technical Conditions in linear regression

This chapter covers topics that build on the basic ideas of inference in linear models, including multicollinearity and inference for multiple regression models.

Building on Inference in Simple Linear Regression

LA home price data

NYC restaurant data

Twin data

Previously, you learned the fundamentals of both statistical inference and linear models; now, the next step is to put them together.  This course gives you a chance to think about how different samples can produce different linear models, where your goal is to understand the underlying population model. From the estimated linear model, you will learn how to create interval estimates for the effect size as well as how to determine if the effect is significant.  Prediction intervals for the response variable will be contrasted with estimates of the average response. Throughout the course, you'll gain more practice with the dplyr and ggplot2 packages, and you will learn about the broom package for tidying models; all three packages are invaluable in data science.

Multiple and Logistic Regression in R

Inference for Linear Regression in R

In this course you'll learn how to perform inference using linear models.

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Statistical Inference with R

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