# Statistics: A Step-by-step Introduction

This 51 lesson course teaches the foundational material of statistics covered in an introductory college course, with a focus on mastering hypothesis testing for proportions, means, and categorical data.

Instructor : Brian Greco

1 Course

## What you'll learn

• Build a strong statistical vocabulary and foundation in probability
• Learn to tests hypotheses for proportions and means
• Learn how to create confidence intervals, and their connection to hypothesis tests
• Learn how to perform chi-square tests for categorical data

## Description

### The course includes:

• 10 hours of video lectures, using the innovative lightboard technology to deliver face-to-face lectures
• Supplementary lecture notes with each lesson covering important vocabulary, examples and explanations from the video lessons
• 19 quizzes to check your understanding
• 9 assignments with solutions to practice what you have learned

• Common terminology to describe different types of data and learn about commonly used graphs
• Basic probability, including the concept of a random variable, probability mass functions, cumulative distribution functions, and the binomial distribution
• What is the normal distribution, why it is so important, and how to use z-scores and z-tables to compute probabilities
• Type I errors, alpha, critical values, and p-values
• How to conduct hypothesis tests for one and two proportions using a z-test
• How to conduct hypothesis tests for one and two means using a t-test
• Confidence Intervals for proportions and means, and the connection between hypothesis testing and confidence intervals
• How to conduct a chi-square goodness-of-fit test
• How to conduct a chi-square test of homogeneity and independence.
• An introduction to correlation and simple linear regression

### This course is ideal for many types of students:

• Anyone who wants to learn the foundations of statistics and understand concepts like p-values and confidence intervals
• Students taking an introductory college or high school statistics class who would like further explanations and detailed examples
• Data science professionals who would like to refresh and expand their statistics knowledge to prepare for job interviews