Instructional Video6:12
Curated Video

R Programming for Statistics and Data Science - Variance, standard deviation, and coefficient of variability

Higher Ed
This video explains variance, standard deviation, and coefficient of variability. This clip is from the chapter "Exploratory Data Analysis" of the series "R Programming for Statistics and Data Science".This section explains exploratory...
Instructional Video3:31
Curated Video

R Programming for Statistics and Data Science - Introduction to Vectors

Higher Ed
This video explains introduction to vectors. This clip is from the chapter "Vectors and Vector Operations" of the series "R Programming for Statistics and Data Science".This section explains vectors and vector operations.
Instructional Video6:32
Curated Video

R Programming for Statistics and Data Science - Distributions

Higher Ed
This video explains distributions. This clip is from the chapter "Hypothesis Testing" of the series "R Programming for Statistics and Data Science".This section explains hypothesis testing.
Instructional Video8:37
Curated Video

R Programming for Statistics and Data Science - Standard Error and Confidence Intervals

Higher Ed
This video explains standard error and confidence intervals. This clip is from the chapter "Hypothesis Testing" of the series "R Programming for Statistics and Data Science".This section explains hypothesis testing.
Instructional Video9:45
Curated Video

Learn JMeter from Scratch on Live Applications - Performance Testin - Different Type of Listeners and Their Use in Gathering Performance Metrics

Higher Ed
This video explains the different Listeners and their use in gathering performance metrics. This clip is from the chapter "How to Put Load and Analyze Performance Metrics?" of the series "Learn JMeter from Scratch on Live Applications -...
Instructional Video5:34
Curated Video

Julia for Data Science (Video 20)

Higher Ed
Julia is an easy, fast, open source language that if written well performs nearly as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing high machine performance...
Instructional Video54:11
APMonitor

Classification and Regression: Concrete Strength

10th - Higher Ed
This case study is to determine the factors (inputs) that have correlation to the concrete compressive strength (output). 0:00 Introduction 2:35 Download Jupyter Notebook 4:00 Import Machine Learning Packages and Data 7:33 Part 1: Data...
Instructional Video36:52
APMonitor

Data Engineering Summary Statistics

10th - Higher Ed
Summary statistics give valuable insights as one of the first steps in data engineering after the data is gathered. Statistics help to assess data quality and diversity. Data discovery with statistics is a common first activity and there...
Instructional Video5:59
Curated Video

Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] Linear Regression in Action

Higher Ed
From the section: Machine Learning: Part 1. In this section, we’ll learn how machine learning works, and how it fits in with the world of AI and deep learning. And learn to train, test and validate the data using K-fold cross-validation....
Instructional Video36:10
APMonitor

Logistic Regression from Scratch

10th - Higher Ed
Logistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function. Logistic regression makes a binary...
Instructional Video12:06
APMonitor

Data Science 🐍 Statistical Analysis

10th - Higher Ed
Once data is read into Python, a first step is to analyze the data with summary statistics. This is especially true if the data set is large. Summary statistics include the count, mean, standard deviation, maximum, minimum, and quartile...
Instructional Video16:08
APMonitor

ML Draw Classification

10th - Higher Ed
26 - ML Draw Classification
Instructional Video4:48
Curated Video

Predictive Analytics with TensorFlow 11.3: Developing a Stock Price Predictive Model

Higher Ed
An emerging area for applying is the stock market trading, where a trader acts like a reinforcement agent since buying and selling particular stock changes the state of the trader by generating profit or loss, that is, reward. • Define...
Instructional Video6:53
Curated Video

Comparing Treatments Using Resampling: Determining the Effectiveness of New Salt Substitute and Fertilizer

K - 5th
In this video, the teacher explains how researchers can determine if there is a difference between two treatments using a resampling strategy. They use examples of comparing salt substitutes and fertilizers to demonstrate the process. By...
Instructional Video11:09
Curated Video

The Complete Excel Guide: Beginners to Advanced - Statistical Functions for Inference

Higher Ed
The aim of this video is to explore statistical functions for inference. This clip is from the chapter "Excel 2019 Advanced: Statistical Functions" of the series "The Complete Excel Guide: Beginners to Advanced".In this section, we'll...
Instructional Video32:00
APMonitor

Data Visualization in Python

10th - Higher Ed
Data visualization and exploration is one of the first steps in machine learning after the data is gathered and statistically summarized. It is used to graphically represent data to qualitatively understand relationships and data...
Instructional Video8:07
Curated Video

Finding Percentages in a Normal Distribution Using Z Scores and Tables

K - 5th
In this lesson, you will learn how to use Z scores and statistics tables to find the percentage of a population that falls within a certain interval. This method is particularly useful when dealing with values that are not exactly 1-2 or...
Instructional Video5:49
Curated Video

Predicting Population Percentages Using a Graphing Calculator

K - 5th
In this video, students learn how to predict population percentages using a graphing calculator. The lesson focuses on the normal model and its application to data sets. It also covers the use of the empirical rule and explores different...
Instructional Video5:08
Catalyst University

NO BS: Paired t-Test Excel Tutorial

Higher Ed
NO BS: Paired t-Test Excel Tutorial
Instructional Video5:37
Curated Video

Estimating Percentages using the Empirical Rule

K - 5th
This video explains how to estimate the percentage of adult American females between 5 feet and 5 feet 10 inches tall using the empirical rule. The empirical rule states that approximately 68% of the data falls within one standard...
Instructional Video5:48
Catalyst University

NO BS: Independent t-Test Excel Tutorial

Higher Ed
NO BS: Independent t-Test Excel Tutorial
Instructional Video7:26
Catalyst University

Excel/Numbers: Program Average and Standard Deviation

Higher Ed
Excel/Numbers: Program Average and Standard Deviation
Instructional Video3:58
Curated Video

Comparing Data: Measures of Center and Spread

K - 5th
In this video, the teacher explains how to compare two sets of data using measures of center (median and mean) and measures of spread (standard deviation and IQR). The teacher emphasizes the importance of choosing the appropriate...
Instructional Video4:17
Brian McLogan

How to find the number of standard deviations that it takes to represent all the data

12th - Higher Ed
👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard...