Probability and Statistics- Complete Course 2024

Probability and Statistics: Complete Course 2024, available at $94.99, has an average rating of 4.63, with 116 lectures, 63 quizzes, based on 335 reviews, and has 2270 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists or Business Analysts or Business Students or People studying Statistics or Anyone looking to power their decision making with a thorough understanding of statistics. It is particularly useful for Data Scientists or Business Analysts or Business Students or People studying Statistics or Anyone looking to power their decision making with a thorough understanding of statistics.

Enroll now: Probability and Statistics: Complete Course 2024

Summary

Title: Probability and Statistics: Complete Course 2024

Price: $94.99

Average Rating: 4.63

Number of Lectures: 116

Number of Quizzes: 63

Number of Published Lectures: 116

Number of Published Quizzes: 63

Number of Curriculum Items: 181

Number of Published Curriculum Objects: 181

Original Price: ￡149.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Data Scientists

Business Analysts

Business Students

People studying Statistics

Anyone looking to power their decision making with a thorough understanding of statistics.

Target Audiences

Data Scientists

Business Analysts

Business Students

People studying Statistics

Anyone looking to power their decision making with a thorough understanding of statistics.

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 Data: Identifying and removing outliers

Visualization of Data: 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).

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Overview

Chapter 2: Descriptive Statistics

Lecture 1: Data for this chapter

Lecture 2: The Mean Average

Lecture 3: The Median Average

Lecture 4: The Modal Average

Lecture 5: Comparing Averages

Lecture 6: Quantiles, Range and Inter-Quartile Range

Lecture 7: Quantiles, Range and Inter-Quartile Range – Data

Lecture 8: Standard Deviation and Variance

Lecture 9: Standard Deviation and Variance – Data

Lecture 10: The Coefficient of Variation

Lecture 11: The Coefficient of Variation – Data

Lecture 12: Skew

Lecture 13: Skew – data

Lecture 14: Kurtosis

Lecture 15: Correlation Coefficients

Lecture 16: Correlation Coefficients – Data

Lecture 17: Slides

Chapter 3: Cleaning Data

Lecture 1: Anomalies and Outliers

Lecture 2: Anomalies and Outliers – Data

Lecture 3: Coding Your Data

Lecture 4: Slides

Chapter 4: Data Visualization

Lecture 1: Line Graphs

Lecture 2: Bar Charts

Lecture 3: Dual Axis Charts

Lecture 4: Pie Charts

Lecture 5: Histograms

Lecture 6: Histograms – Data

Lecture 7: Box Plots

Lecture 8: Cumulative Frequency

Lecture 9: Comparing Visualizations

Lecture 10: Slides

Chapter 5: Sampling

Lecture 1: Populations and Samples

Lecture 2: Random Sampling

Lecture 3: Non-Random Sampling

Lecture 4: Slides

Chapter 6: Probability

Lecture 1: What is Probability?

Lecture 2: Set Notation

Lecture 3: Independent Events

Lecture 4: Mutually Exclusive Events

Lecture 5: Tree Diagrams

Lecture 6: Venn Diagrams

Lecture 7: Conditional Probability

Lecture 8: Bayes Theorem

Lecture 9: Slides

Chapter 7: Discrete Distributions

Lecture 1: What is a Discrete Random Variable?

Lecture 2: Probability Mass Functions

Lecture 3: The Expectation of a Discrete Random Variable

Lecture 4: The Variance of a Discrete Random Variable

Lecture 5: The Variance of a Discrete Random Variable – Data

Lecture 6: The Binomial Distribution – Intro

Lecture 7: The Binomial Distribution Formula – Part 1

Lecture 8: The Binomial Distribution Formula – Part 2

Lecture 9: Using Excel to Solve Binomial Problems

Lecture 10: Applying the Binomial Distribution to Real-World Problems

Instructors

Woody Lewenstein
Mathematics Teacher

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 17 votes

4 stars: 88 votes

5 stars: 225 votes

Frequently Asked Questions

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