Here, we will understand the concepts of statistical moments, which generalizes what we already know about the central tendency measures and spread/dispersion.

In this video, we will look at the impact of the data changes we make with underlying data in the measures of central tendency or dispersion.

After learning about the measures of spread, the range, and IQR, we will now learn more about the two most common measures of spread, the variance, and standard deviation.

This lecture will explain the fundamental difference between a sample and a population in statistical data.

In this lesson, we will understand the measures of spread or dispersion, which is the range, and we will look at the interquartile range in detail.

This video is an explanation of the practice lesson/exercise of skewness.

This is a practice lesson/exercise to better understand the concept of skewness.

In this video, you will learn about skewness, the symmetry you learned about in the previous lesson.

In this lesson, the author explains more about the mean and median and when to use them for the best results.

In this lecture, we will elaborate more about the third and final measure of central tendency, the mode.

In this lesson, we will discuss the second important measure of central tendency, the median.

In this video, we will discuss the measures of central tendency. Here, you will learn more about the first measure, the mean.

This brief introduction to the section outlines what you will be learning from this section, including the basics of statistics.

In this lesson, the author explains how to use the course best and the techniques and tools best applicable.

In this video, the author briefly introduces what it would take to become a data scientist and then goes over in detail what one can learn from this course.

This is a welcome introduction by the author where he explains in detail what the course is all about, what you will learn from the course, and a brief outline of the entire course's content.

In this video, you'll learn about p-value and understand why it is one of the most useful tools for statisticians. This clip is from the chapter "Hypothesis Testing" of the series "Statistics for Data Science and Business Analysis".This...

This video explains hypothesis testing.<br<br/>/>

This clip is from the chapter "Hypothesis Testing" of the series "R Programming for Statistics and Data Science".This section explains hypothesis testing.

Simple linear regression models using R programming doesn't need to be scary. This video will take you through the basics of how to interpret linear regression results and the code needed to do the regression models. If you are learning...

R programming is easy. In this video, I'll walk you though how to clean your data; how to manipulate (or wrangle) your data; how to summarize your data; how to create tables and describe your data; how to visualize your data using ggplot...

Simple linear regression models don't need to be scary. This video will take you through the basics of how to interpret linear regression results. If you are learning regression modelling or just learning statistics and quantitative...

This video explains the Central Limit Theorem.<br<br/>/>

This clip is from the chapter "Statistical Techniques" of the series "Practical Data Science Using Python".This section explains advanced visualizations using business applications.

This video explains the foundation of the linear regression model, which is the very first approach to supervised learning.
<
br/>
This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This...

This video explains hypothesis testing.<br<br/>/>

This clip is from the chapter "Statistical Techniques" of the series "Practical Data Science Using Python".This section explains advanced visualizations using business applications.