Probability And Statistics Complete Course 2023 – Download Free

Free Download What you'll learn
Descriptive Statistics
Visualizing Data
Probability Theory
Bayesian Statistics
Discrete Distributions (Binomial, Poisson and More)
Continuous Distributions (Normal and Others)
Hypothesis Tests
Regression
Type I and Type II Errors
Chi-Squared Test
Requirements
No pre-requisites for most of the course. One small optional section requires knowledge and calculus, but other than that this is suitable for beginners.
Description
This is course designed to take you from beginner to expert in probability and statistics. It is designed to be practical, hands on and suitable for anyone who wants to use statistics in data science, business analytics or any other field to make better informed decisions.Videos packed with worked examples and explanations so you never get lost, and every technique covered is implemented in Microsoft Excel so that you can put it to use immediately.Key concepts taught in the course are:Descriptive Statistics: Averages, measures of spread, correlation and much more.Cleaning dаta: Identifying and removing outliersVisualization of dаta: All standard techniques for visualizing data, embedded in Excel.Probability: Independent Events, conditional probability and Bayesian statistics.Discrete Distributions: Binomial, Poisson, expectation and variance and approximations.Continuous Distributions: The Normal distribution, the central limit theorem and continuous random variables.Hypothesis Tests: Using binomial, Poisson and normal distributions, T-tests and confidence intervals.Regression: Linear regression analysis, correlation, testing for correlation, non-linear regression models.Quality of Tests: Type I and Type II errors, power and size, p-hacking.Chi-Squared Tests: The chi-squared distribution and how to use it to test for association and goodness of fit.Much, much more!It requires no prior knowledge, with the exception of 2 optional videos at the end of the continuous distribution chapter, in which knowledge of calculus is required).
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Course Overview
Section 2: Descriptive Statistics
Lecture 3 Data for this chapter
Lecture 4 The Mean Average
Lecture 5 The Median Average
Lecture 6 The Modal Average
Lecture 7 Comparing Averages
Lecture 8 Quantiles, Range and Inter-Quartile Range
Lecture 9 Quantiles, Range and Inter-Quartile Range - Data
Lecture 10 Standard Deviation and Variance
Lecture 11 Standard Deviation and Variance - Data
Lecture 12 The Coefficient of Variation
Lecture 13 The Coefficient of Variation - Data
Lecture 14 Skew
Lecture 15 Skew - data
Lecture 16 Kurtosis
Lecture 17 Correlation Coefficients
Lecture 18 Correlation Coefficients - Data
Section 3: Cleaning Data
Lecture 19 Anomalies and Outliers
Lecture 20 Anomalies and Outliers - Data
Lecture 21 Coding Your Data
Section 4: Data Visualization
Lecture 22 Line Graphs
Lecture 23 Bar Charts
Lecture 24 Dual Axis Charts
Lecture 25 Pie Charts
Lecture 26 Histograms
Lecture 27 Histograms - Data
Lecture 28 Box Plots
Lecture 29 Cumulative Frequency
Lecture 30 Comparing Visualizations
Section 5: Sampling
Lecture 31 Populations and Samples
Lecture 32 Random Sampling
Lecture 33 Non-Random Sampling
Section 6: Probability
Lecture 34 What is Probability?
Lecture 35 Set Notation
Lecture 36 Independent Events
Lecture 37 Mutually Exclusive Events
Lecture 38 Tree Diagrams
Lecture 39 Venn Diagrams
Lecture 40 Conditional Probability
Lecture 41 Bayes' Theorem
Section 7: Discrete Distributions
Lecture 42 What is a Discrete Random Variable?
Lecture 43 Probability Mass Functions
Lecture 44 The Expectation of a Discrete Random Variable
Lecture 45 The Variance of a Discrete Random Variable
Lecture 46 The Binomial Distribution - Intro
Lecture 47 The Binomial Distribution Formula - Part 1
Lecture 48 The Binomial Distribution Formula - Part 2
Lecture 49 Using Excel to Solve Binomial Problems
Lecture 50 Applying the Binomial Distribution to Real-World Problems
Lecture 51 Conditional Probability with the Binomial Distribution
Lecture 52 The Poisson Distribution - Intro
Lecture 53 Using Excel to Solve Poisson Problems
Lecture 54 Applying the Poisson Distribution Real-World Problems
Lecture 55 Conditional Probability with the Poisson Distribution
Lecture 56 The Geometric Distribution
Lecture 57 Expectation and Variance of Distributions
Lecture 58 Approximating the Binomial Distribution with the Poisson Distribution
Lecture 59 Derivation of the Poisson Formula
Section 8: Continuous Distributions
Lecture 60 What is a Continuous Distribution?
Lecture 61 The Normal Distribution - Intro
Lecture 62 Calculating Probabilities with the Normal Distribution
Lecture 63 The Inverse Normal Distribution
Lecture 64 Z-Scores
Lecture 65 Finding Unknown Means and Standard Deviations
Lecture 66 Conditional Probability with the Normal Distribution
Lecture 67 Normal Approximations to Binomial Distributions - Part 1
Lecture 68 Normal Approximations to Binomial Distributions - Part 2
Lecture 69 Normal Approximations to Poisson Distributions
Lecture 70 The Central Limit Theorem
Lecture 71 The Limitations of the Central Limit Theorem
Lecture 72 Continuous Random Variables - Probability Density Functions
Lecture 73 Continuous Random Variables - Cumulative Distribution Functions
Lecture 74 Continuous Random Variables - Expectation and Variance
Lecture 75 Continuous Random Variables - Medians and Quartiles
Section 9: Hypothesis Tests
Lecture 76 Introduction to Hypothesis Tests - P-Values
Lecture 77 Binomial Hypothesis Tests - Part 1
Lecture 78 Binomial Hypothesis Tests - Part 2
Lecture 79 Binomial Hypothesis Tests - Critical Regions
Lecture 80 Two-Tailed Tests
Lecture 81 Poisson Hypothesis Tests
Lecture 82 Poisson Critical Regions
Lecture 83 Normal Hypothesis Tests
Lecture 84 Normal Hypothesis Tests - Critical Regions
Lecture 85 T-Tests
Lecture 86 Confidence Intervals
Section 10: Regression
Lecture 87 Correlation
Lecture 88 Linear Regression
Lecture 89 Evaluating a Regression Line
Lecture 90 Correlation Hypothesis Tests - Intro
Lecture 91 Carrying Out a Test for Correlation
Lecture 92 Correlation Confidence Intervals
Lecture 93 Working with Non-Linear Data - Exponential Models
Lecture 94 Working with Non-Linear Data - Polynomial Models
Section 11: Quality of Tests
Lecture 95 Type I Errors
Lecture 96 Type II Errors
Lecture 97 Size and Power
Lecture 98 P-Hacking
Section 12: Chi-Squared Tests
Lecture 99 The Chi-Squared Distribution
Lecture 100 Chi-Squared Tests for Goodness of Fit
Lecture 101 Grouping
Lecture 102 Using Estimated Parameters in Chi-Squared Tests
Lecture 103 Chi-Squared Tests for Association
Data Scientists,Business Analysts,Business Students,People studying Statistics,Anyone looking to power their decision making with a thorough understanding of statistics.

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