Video Description
Statistics you need in the office: Descriptive and inferential statistics, hypothesis testing, and regression analysis
About This Video
 Learn and understand the fundamentals of statistics for Data Science and Business Analysis.
 A practical tutorial with case studies for people interested in Data Science and Business Analysis.
In Detail
This course will teach you fundamental skills that will enable you to understand complicated statistical analysis directly applicable to reallife situations. Modern software packages and programming languages are now automating most of these activities, but this course gives you something more valuable  critical thinking abilities. This course will help you understand the fundamentals of statistics, learn how to work with different types of data, calculate correlation and covariance, and more. Careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow
Table of Contents

Chapter 1 : Introduction
 What does the Course Cover? 00:02:56
 Chapter 2 : Sample or population data?
 Chapter 3 : The fundamentals of descriptive statistics

Chapter 4 : Measures of central tendency, asymmetry, and variability
 The main measures of central tendency: mean, median, mode 00:04:24
 Measuring skewness 00:02:44
 Measuring how data is spread out: calculating variance 00:05:58
 Standard deviation and coefficient of variation 00:04:55
 Calculating and understanding covariance 00:03:31
 The correlation coefficient 00:03:48

Chapter 5 : Practical example: descriptive statistics
 Practical example 00:14:31

Chapter 6 : Distributions
 Introduction to inferential statistics 00:01:02
 What is a distribution? 00:03:40
 The Normal distribution 00:03:46
 The standard normal distribution 00:02:52
 Understanding the central limit theorem 00:03:41
 Standard error 00:01:20

Chapter 7 : Estimators and estimates
 Working with estimators and estimates 00:02:36
 Confidence intervals  an invaluable tool for decision making 00:06:31
 Calculating confidence intervals within a population with a known variance 00:02:30
 Student’s T distribution 00:03:14
 Calculating confidence intervals within a population with an unknown variance 00:04:07
 What is a margin of error and why is it important in Statistics? 00:04:38

Chapter 8 : Confidence intervals: advanced topics
 Calculating confidence intervals for two means with dependent samples 00:04:48
 Calculating confidence intervals for two means with independent samples (part 1) 00:04:36
 Calculating confidence intervals for two means with independent samples (part 2) 00:03:40
 Calculating confidence intervals for two means with independent samples (part 3) 00:01:25
 Chapter 9 : Practical example: inferential statistics
 Chapter 10 : Hypothesis testing: Introduction

Chapter 11 : Hypothesis testing: Let's start testing!
 Test for the mean. Population variance known 00:06:08
 What is the pvalue and why is it one of the most useful tool for statisticians? 00:03:55
 Test for the mean. Population variance unknown 00:04:26
 Test for the mean. Dependent samples 00:04:45
 Test for the mean. Independent samples (Part 1) 00:03:39
 Test for the mean. Independent samples (Part 2) 00:03:49
 Chapter 12 : Practical example: hypothesis testing

Chapter 13 : The fundamentals of regression analysis
 Introduction to regression analysis 00:01:02
 Correlation and causation 00:04:07
 The linear regression model made easy 00:05:03
 What is the difference between correlation and regression? 00:01:28
 A geometrical representation of the linear regression model 00:01:18
 A practical example  Reinforced learning 00:05:36

Chapter 14 : Subtleties of regression analysis
 Decomposing the linear regression model  understanding its nuts and bolts 00:02:04
 What is R squared and how does it help us? 00:05:00
 The ordinary least squares setting and its practical applications 00:02:08
 Studying regression tables 00:04:35
 The multiple linear regression model 00:02:42
 Adjusted Rsquared 00:04:57
 What does the Fstatistic show us and why we need to understand it? 00:02:01

Chapter 15 : Assumptions for linear regression analysis
 OLS assumptions 00:02:12
 A1. Linearity 00:01:40
 A2. No endogeneity 00:03:44
 A3. Normality and homoscedasticity 00:05:10
 A4. No autocorrelation 00:03:11
 A5. No multicollinearity 00:03:22

Chapter 16 : Dealing with categorical data
 Dummy variables 00:05:21
 Chapter 17 : Practical example: regression analysis
Product Information
 Title: Statistics for Data Science and Business Analysis
 Author(s):
 Release date: October 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789803259