# Statistics A Stepbystep Introduction

Lessons and examples from a former Google data scientist to master hypothesis tests, confidence intervals, and more 4.7/5

## Statistics A Stepbystep Introduction

### 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 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:10 hours of video lectures, using the innovative lightboard technology to deliver face-to-face lecturesSupplementary lecture notes with each lesson covering important vocabulary, examples and explanations from the video 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.An introduction to correlation and simple linear regressionThis 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 Welcome Document and Probability Tables

Lecture 2 Section 1 Exercises and Solutions

Lecture 3 Introduction: Statistics, data, and variables

Lecture 4 Categorical Variables, Frequency and Proportion, Bar Charts

Lecture 5 Discrete and Continuous Variables, Dot Plots

Lecture 6 Stem-and-leaf plots and Histograms

Lecture 7 Shape, Skewness. and Symmetry

Lecture 8 Central Tendency: Mean, Median, Mode

Lecture 9 Spread: Range, IQR, Boxplots

Lecture 10 Spread: Variance and Standard Deviation

Section 2: Probability

Lecture 11 Section 2 Exercises and Solutions

Lecture 12 Observed vs. Expected

Lecture 13 Outcomes, Events, Sample Space, Complements

Lecture 14 Probability of A or B: Unions of Events

Lecture 15 Practice: Unions and Venn Diagrams

Lecture 16 Probability of A and B: Intersections and Conditional Probability

Lecture 17 Practice: Independence, Conditional Probability, Intersections

Lecture 18 Random Variables, PDF/PMF, CDF

Lecture 19 Practice: Discrete PMF and CDF

Lecture 20 Practice: Continuous CDF (Uniform Distribution)

Lecture 21 Binomial distribution

Lecture 22 Expected value

Lecture 23 Practice: Expected Value

Section 3: Normal distributions

Lecture 24 Section 3 Exercises and Solutions

Lecture 25 The Standard Normal Distribution and the Empirical Rule

Lecture 26 More on the Empirical Rule

Lecture 27 Z-table

Lecture 28 Normal distribution parameters: mu and sigma

Lecture 29 Z-scores

Lecture 30 Practice: Z-table

Lecture 31 Practice: Z-scores

Lecture 32 The Central Limit Theorem

Lecture 33 Practice: CLT for continuous data

Lecture 34 Practice: CLT for binomial data

Section 4: One Proportion: Z-test

Lecture 35 Section 4 Exercises and Solutions

Lecture 36 The Null and Alternative Hypothesis

Lecture 37 Critical values and Decision Rules

Lecture 38 P-values

Lecture 39 P-values with normal approximation

Lecture 40 Type I errors and Alpha

Lecture 41 One proportion z-test example

Section 5: Two Proportions:: Z-test

Lecture 42 Section 5 Exercises and Solutions

Lecture 43 Hypothesis testing for two proportions

Lecture 44 Hypothesis testing for two proportion example

Section 6: One Mean: Z-test, t-test

Lecture 45 Section 6 Exercises and Solutions

Lecture 46 One sample z-test

Lecture 47 One sample t-test

Lecture 48 One sample t-test example

Section 7: Two Means: T-test

Lecture 49 Section 7 Exercises and Solutions

Lecture 50 Two sample t-test

Lecture 51 Two sample t-test example

Lecture 52 Pooled and Unpooled

Lecture 53 Paired t-tests

Section 8: Confidence Intervals

Lecture 54 Section 8 Exercises and Solutions

Lecture 55 Confidence Intervals

Lecture 56 Pivoting a test statistic to make a CI

Lecture 57 Performing a hypothesis test based on a confidence interval

Lecture 58 All Four CI Formulas

Lecture 59 Confidence Interval One Proportion Example

Lecture 60 Confidence Interval Two Proportion Example

Lecture 61 Confidence Interval One Mean Example

Lecture 62 Confidence Interval Two Mean Example

Section 9: Chi-Square Tests

Lecture 63 Section 9 Exercises and Solutions

Lecture 64 Chi-square Goodness of Fit Test: Die

Lecture 65 Chi-square Goodness of Fit example

Lecture 66 Two way tables and expected counts

Lecture 67 Chi-square test for two way table

Lecture 68 Independence vs Homogeneity

Lecture 69 Chi Square Two way Example

Section 10: Correlation and Simple Linear Regression

Lecture 70 Section 10 Exercises and Solutions

Lecture 71 Two Quantitative Variables

Lecture 72 Correlation coefficient

Lecture 73 Regression Equation and Interpretation

Lecture 74 Least Squares

Lecture 75 Regression Example Problem

Lecture 76 Errors and Residuals

Lecture 77 R^2: The Coefficient of Determination

Lecture 78 Regression Inference: Hypothesis Testing and CIs

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

#### Course Information:

Udemy | English | 10h 3m | 13.06 GB
Created by: Brian Greco

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