Statistics: A Step-By-Step Introduction
Published 6/2022
MP4 | Video: h264, 1280x720 | 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 аbout: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
Overview
Section 1: Introduction, Data, and Graphs
Lecture 1 Introduction (Download Lecture Notes and Assignments here!)
Lecture 2 Statistics, data, and variables
Lecture 3 Categorical Variables, Frequency and Proportion, Bar Charts
Lecture 4 Discrete and Continuous Variables, Dot Plots
Lecture 5 Stem-and-leaf plots and Histograms
Lecture 6 Shape, Skewness. and Symmetry
Lecture 7 Central Tendency: Mean, Median, Mode
Lecture 8 Spread: Range, IQR, Boxplots
Lecture 9 Spread: Variance and Standard Deviation
Section 2: Probability
Lecture 10 Observed vs. Expected
Lecture 11 Outcomes, Events, Sample Space, Complements
Lecture 12 Probability of A or B: Unions of Events
Lecture 13 Probability of A and B: Intersections and Conditional Probability
Lecture 14 Random Variables, PDF/PMF, CDF
Lecture 15 Binomial distribution
Lecture 16 Expected value
Section 3: Normal distributions
Lecture 17 The Standard Normal Distribution and the Empirical Rule
Lecture 18 More on the Empirical Rule
Lecture 19 Z-table
Lecture 20 Normal distribution parameters: mu and sigma
Lecture 21 Z-scores
Lecture 22 The Central Limit Theorem
Section 4: One Proportion: Z-test
Lecture 23 The Null and Alternative Hypothesis
Lecture 24 Critical values and Decision Rules
Lecture 25 P-values
Lecture 26 P-values with normal approximation
Lecture 27 Type I errors and Alpha
Lecture 28 One proportion z-test example
Section 5: Two Proportions:: Z-test
Lecture 29 Hypothesis testing for two proportions
Lecture 30 Hypothesis testing for two proportion example
Section 6: One Mean: Z-test, t-test
Lecture 31 One sample z-test
Lecture 32 One sample t-test
Lecture 33 One sample t-test example
Section 7: Two Means: T-test
Lecture 34 Two sample t-test
Lecture 35 Two sample t-test example
Lecture 36 Pooled and Unpooled
Lecture 37 Paired t-tests
Section 8: Confidence Intervals
Lecture 38 Confidence Intervals
Lecture 39 (Optional) Pivoting a test statistic to make a CI
Lecture 40 Performing a hypothesis test based on a confidence interval
Lecture 41 All Four CI Formulas
Lecture 42 Confidence Interval One Proportion Example
Lecture 43 Confidence Interval Two Proportion Example
Lecture 44 Confidence Interval One Mean Example
Lecture 45 Confidence Interval Two Mean Example
Section 9: Chi-Square Tests
Lecture 46 Chi-square Goodness of Fit Test: Die
Lecture 47 Chi-square Goodness of Fit example
Lecture 48 Two way tables and expected counts
Lecture 49 Chi-square test for two way table
Lecture 50 Independence vs Homogeneity
Lecture 51 Chi Square Two way Example
Self-learners who want a strong college-level foundational course in statistics,College and high school students who need to supplement their course with high-quality lectures and example problems,Data science professionals looking to refresh or expand their knowledge to prepare for job interviews
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