**Statistics A Step-By-Step Introduction**

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Language: English | Size: 9.35 GB | Duration: 7h 11m

Lessons and examples from a former Google data scientist to master hypothesis tests, confidence intervals, and more

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

Requirements

Basic arithmetic skills

Basic algebra (ability to understand equations with variables)

Description

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.The course includes:51 video lectures, using the innovative lightboard technology to deliver face-to-face lectures157 pages of lecture notes covering important vocabulary, examples and explanations from the 51 lessons19 quizzes to check your understanding9 assignments with solutions to practice what you have learnedYou will learn about:Common terminology to describe different types of data and learn about commonly used graphsBasic probability, including the concept of a random variable, probability mass functions, cumulative distribution functions, and the binomial distributionWhat is the normal distribution, why it is so important, and how to use z-scores and z-tables to compute probabilitiesType I errors, alpha, critical values, and p-valuesHow to conduct hypothesis tests for one and two proportions using a z-testHow to conduct hypothesis tests for one and two means using a t-testConfidence Intervals for proportions and means, and the connection between hypothesis testing and confidence intervalsHow to conduct a chi-square goodness-of-fit testHow to conduct a chi-square test of homogeneity and independence.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 intervalsStudents taking an introductory college or high school statistics class who would like further explanations and detailed examplesData science professionals who would like to refresh and expand their statistics knowledge to prepare for job interviews

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