Excel 2013 Statistical Analysis #09: Cumulative Frequency Distribution & Chart, PivotTable & Formula
Download files: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch02/Excel2013StatisticsChapter02.xlsx Topics in this video: 1. (00:09) Overview of % Cumulative Frequency 2. (00:42) Formulas to create Cumulative Frequency Distribution and % Cumulative Frequency Distribution. 3.
From playlist Excel for Statistical Analysis in Business & Economics Free Course at YouTube (75 Videos)
Correlation Coefficient (2 of 2: Evaluating with a calculator)
More resources available at www.misterwootube.com
From playlist Bivariate Data Analysis
BIVARIATE Probability Distribution for Discrete Random Variables (9-7)
A Bivariate Probability Distribution gives the probabilities for simultaneous outcomes of two random variables, such as the joint distribution of rolling a pair of dice. Each outcome consists of two values, one for each random variable (Die 1 vs. Die 2). This allows us to explore the relat
From playlist Discrete Probability Distributions in Statistics (WK 9 - QBA 237)
Understanding Sensor Fusion and Tracking, Part 5: How to Track Multiple Objects at Once
Check out the other videos in the series: Part 1 - What Is Sensor Fusion?: https://youtu.be/6qV3YjFppuc Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: https://youtu.be/0rlvvYgmTvI Part 3 - Fusing a GPS and IMU to Estimate Pose: https://youtu.be/hN8dL55rP5I Part 4 - Trac
From playlist Understanding Sensor Fusion and Tracking
Restricted Boltzmann Machine | Neural Network Tutorial | Deep Learning Tutorial | Edureka
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** This Edureka video on "Restricted Boltzmann Machine" will provide you with a detailed and comprehensive knowledge of Restricted Boltzmann Machines, also known as RBM. You will also
From playlist Deep Learning With TensorFlow Videos
Sanjoy Mitter - Overview of variational approach to nonlinear filtering
PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi
From playlist Nonlinear filtering and data assimilation
Bayesian Networks 3 - Maximum Likelihood | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Zlc5Iu Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associa
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
2 Sample t Test v Paired t Test
Identifying the difference between situations when a 2-sample t Test is appropriate and when a paired t Test is appropriate, including the recognition of paired dependent data versus independent samples.
From playlist Unit 9: t Inference and 2-Sample Inference
Excel 2013 Statistical Analysis #24: Numerical Measures: Covariance and Correlation Coefficient
Download file: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch03/Excel2013StatisticsChapter03.xlsm Topics in this video: 1. (00:15) Review of different X-Y Scatter 2. (02:42) Add Xbar Line and Ybar Line to X Y Scatter Chart to help interpret how Covariance is calculated and
From playlist Excel for Statistical Analysis in Business & Economics Free Course at YouTube (75 Videos)
Bayes Classifiers (2): Naive Bayes
Complexity and overfitting in Bayes classifiers; naive Bayes models
From playlist cs273a
Duality between estimation and control - Sanjoy Mitter
PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod
From playlist Data Assimilation Research Program
08 Data Analytics: Correlation
Lecture on bivariate statistics and correlation.
From playlist Data Analytics and Geostatistics
Bayesian Networks 1 - Inference | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3bcQMeG Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associa
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
Stanford CS330: Deep Multi-task and Meta Learning | 2020 | Lecture 13: A Graphical Model Perspective
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai A Graphical Model Perspective on Multi-Task and Meta-RL To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and pro
From playlist Stanford CS330: Deep Multi-task and Meta Learning | Autumn 2020
Scatterplots, Part 3: The Formula Behind the Correlation Coefficient
We use the Scatterplots & Correlation app to explain the formula behind the correlation coefficient. The app allows you to find and plot the z-scores, showing the 4 quadrants in which points on the scatterplot can fall.
From playlist Chapter 3: Relationships between two variables
Time Series class: Part 2 - Professor Chis Williams, University of Edinburgh
Part 1: https://youtu.be/vDl5NVStQwU Introduction: Moving average, Autoregressive and ARMA models. Parameter estimation, likelihood based inference and forecasting with time series. Advanced: State-space models (hidden Markov models, Kalman filter) and applications. Recurrent neural netw
From playlist Data science classes
Colin Guillarmou: Segal axioms and resolution of Liouville conformal field theory
HYBRID EVENT Liouville conformal field theory is a 2 dimensional field theory introduced in physics in the 80's. Here we give a probabilistic construction of the amplitudes of Riemann surfaces with boundary for this field theory, and we prove that they satisfy the so called Segal Axioms. T
From playlist Probability and Statistics
Joint Discrete Random Variables
Understanding and calculating probabilities involving joint discrete random variables
From playlist Unit 6 Probability B: Random Variables & Binomial Probability & Counting Techniques