+
Instructional Video36:06
Curated Video

Random Variables and its Probability Distributions

Pre-K - Higher Ed
It defines and explains random variable and its probability distributions. Further it elaborates the concept of mean, variance and standard deviation of a random variable.
+
Instructional Video3:24
Curated Video

Variance and Standard Deviation

3rd - Higher Ed
A video entitled “Variance and Standard Deviation” which describes ways that variance and standard deviation are used as measures in the real world.
+
Instructional Video4:40
Packt

Statistics for Data Science and Business Analysis - Calculating Confidence Intervals Within a Population with an Unknown Variance

Higher Ed
This video is about calculating confidence intervals within a population with an unknown variance. This clip is from the chapter "Confidence Intervals" of the series "Statistics for Data Science and Business Analysis".This section...
+
Instructional Video9:38
Packt

Probability Statistics - The Foundations of Machine Learning - Dispersion and Spread in Data, Variance, Standard Deviation

Higher Ed
In this video, we will cover dispersion and spread in data, variance, standard deviation. This clip is from the chapter "Measures of Spread" of the series "Probability / Statistics - The Foundations of Machine Learning".In this section,...
+
Instructional Video7:01
Packt

R Programming for Statistics and Data Science - Test for the Mean - Population Variance Known

Higher Ed
This video explains test for the mean - population variance known. This clip is from the chapter "Hypothesis Testing" of the series "R Programming for Statistics and Data Science".This section explains hypothesis testing.
+
Instructional Video6:06
Packt

Practical Data Science using Python - Random Variables and Normal Distribution

Higher Ed
This video explains random variables and normal distribution. This clip is from the chapter "Statistical Techniques" of the series "Practical Data Science Using Python".This section explains advanced visualizations using business...
+
Instructional Video5:10
Packt

R Programming for Statistics and Data Science - Test for the Mean - Population Variance Unknown

Higher Ed
This video explains test for the mean - population variance unknown. This clip is from the chapter "Hypothesis Testing" of the series "R Programming for Statistics and Data Science".This section explains hypothesis testing.
+
Instructional Video11:17
Packt

Probability Statistics - The Foundations of Machine Learning - Dependence and Variance of Two Random Variables

Higher Ed
In this video, we will cover the dependence and variance of two random variables. This clip is from the chapter "Visualization in Intuition Building" of the series "Probability / Statistics - The Foundations of Machine Learning".In this...
+
Instructional Video11:25
Curated Video

Randomness - Crash Course Statistics

12th - Higher Ed
There are a lot of events in life that we just can’t predict, but just because something is random doesn’t mean we don’t know or can’t learn anything about it. Today, we’re going to talk about how we can extract information from...
+
Instructional Video8:05
Packt

Statistics for Data Science and Business Analysis - Calculating Confidence Intervals Within a Population with a Known Variance

Higher Ed
In this video, you will see the first example of the use of confidence intervals and introduce the concept of the z-score. This clip is from the chapter "Confidence Intervals" of the series "Statistics for Data Science and Business...
+
Instructional Video4:23
Packt

Statistics for Data Science and Business Analysis - Understanding the central limit theorem

Higher Ed
This video is about the central limit theorem - one of the most important statistical concepts. This clip is from the chapter "Inferential Statistics Fundamentals" of the series "Statistics for Data Science and Business Analysis".This...
+
Instructional Video15:32
Packt

Practical Data Science using Python - Linear Regression Data Preparation and Analysis 2

Higher Ed
This video explains how to plot the distribution of various values of engine types. This clip is from the chapter "Linear Regression" of the series "Practical Data Science Using Python".This section explains linear regression.
+
Instructional Video15:42
Packt

Practical Data Science using Python - EDA Project - 2

Higher Ed
This video explains data distribution. This clip is from the chapter "Exploratory Data Analysis (EDA)" of the series "Practical Data Science Using Python".This section explains Exploratory Data Analysis.
+
Instructional Video18:43
Packt

Practical Data Science using Python - Linear Regression OLS Assumptions and Testing

Higher Ed
This video explains linear regression OLS assumptions and testing. This clip is from the chapter "Linear Regression" of the series "Practical Data Science Using Python".This section explains linear regression.
+
Instructional Video18:31
Packt

Practical Data Science using Python - Histograms and Normal Approximation

Higher Ed
This video explains histograms and normal approximation. This clip is from the chapter "Statistical Techniques" of the series "Practical Data Science Using Python".This section explains advanced visualizations using business applications.
+
Instructional Video52:11
Packt

Data Science and Machine Learning (Theory and Projects) A to Z - Project Bayes Classifier: Project Bayes Classifier from Scratch

Higher Ed
In this video, we will cover project Bayes Classifier from scratch. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
+
Instructional Video5:10
Packt

Statistics for Data Science and Business Analysis - A3. Normality and Homoscedasticity

Higher Ed
In this video, the third assumption of OLS, normality and homoscedasticity, are explained. This clip is from the chapter "Assumptions for Linear Regression Analysis" of the series "Statistics for Data Science and Business Analysis".This...
+
Instructional Video2:38
Packt

Data Science and Machine Learning (Theory and Projects) A to Z - Continuous Random Variables: Gaussian Random Variables Solution 01

Higher Ed
In this video, we will cover Gaussian random variables solution 01. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
+
Instructional Video9:40
Packt

Fundamentals of Neural Networks - Linear Regression

Higher Ed
This video explains statistical machine learning, where you will start with the linear regression model. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...
+
Instructional Video11:06
Packt

Probability Statistics - The Foundations of Machine Learning - Dispersion Exploration Through Code

Higher Ed
In this video, we will cover dispersion exploration through code. This clip is from the chapter "Measures of Spread" of the series "Probability / Statistics - The Foundations of Machine Learning".In this section, we will cover measures...
+
Instructional Video16:58
Packt

Practical Data Science using Python - Central Limit Theorem

Higher Ed
This video explains the Central Limit Theorem. This clip is from the chapter "Statistical Techniques" of the series "Practical Data Science Using Python".This section explains advanced visualizations using business applications.
+
Instructional Video1:07:36
Curated Video

How to Use Math to Get Rich in the Lottery* - Jordan Ellenberg (Wisconsin–Madison)

9th - 11th
For seven years, a group of students from MIT exploited a loophole in the Massachusetts State Lottery’s Cash WinFall game to win drawing after drawing, eventually pocketing more that $3 million. How did they do it? How did they get away...
+
Instructional Video16:36
Packt

Practical Data Science using Python - Naive Bayes Probability Computation

Higher Ed
This video explains Naive Bayes probability computation. This clip is from the chapter "Naive Bayes Probability Model" of the series "Practical Data Science Using Python".This section explains Naive Bayes probability model – introduction.