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Statistics: A Step-by-step Introduction

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

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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

You will learn about:

  • 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

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