Home
Search results “Create a bell curve from data mining”
Normal Distribution - Explained Simply (part 1)
 
05:04
*** Check-out the improved version of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1050974 how2stats
StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained
 
06:56
Quantile-Quantile (QQ) plots are used to determine if data can be approximated by a statistical distribution. For example, you might collect some data and wonder if it is normally distributed. A QQ plot will help you answer that question. You can also use QQ plots to compare to different datasets that you collected to determine if their distributions are comparable. This video shows you how to do both things. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Excel Magic Trick #243: MEAN MEDIAN MODE STDEV Histogram
 
11:14
See how to calculate and interpret Mean Median Mode Standard Deviation in Excel. Create a Frequency Distribution and then a Histogram. Basic Statistics. Mean Median Mode, and Standard Deviation Mean Median and Mode are all Averages The reason we have averages is because we need "ONE" value that will represent all the values so we can talk about the "typical score". All the data is so spread out that is hard to talk about 'all" the data unless we calculate a typical value. Here are three ways to calculate a typical value: Mean, Median Mode. MEAN is the arithmetic mean (add all the scores and divide by the count). In Excel we use the AVERAGE function MEDIAN is the one in the middle (position) after we have sorted (this is good when we have extreme values like in real estate (most of the houses are around $200,000, but a few are $1,000,000)). In Excel we use the MEDIAN function MODE is the one that occurs most often. This is good when we have "word" categories such as preference for "cola". In Excel we use the MODE function (It will not tell you when there are more than 1 mode). The Standard Deviation tells you: 1) how spread out the data is; 2) what the mean deviation is; 3) does the average represent its data points fairly. In Excel we use the STDEV function for a sample and the STDEVP function for a population (population is all possible values; sample is some of the values but not all). Histogram. SUMPRODUCT COUNTIF function formula. Column Chart Ampersand Concatenate all these functions ignore blanks or dashes. If you really want to include them you must put a zero instead of a dash or blank.
Views: 136509 ExcelIsFun
Data Analysis with EXCEL Part 1
 
02:57
Construct Frequency Distribution with EXCEL
Views: 1893 Wei Ching Quek
The Normal Distribution
 
02:46
You have surely seen a normal distribution before as it is the most common one. The statistical term for it is Gaussian distribution, but many people call it the Bell Curve as it is shaped like a bell. It is symmetrical and its mean, median and mode are equal. If you remember the lesson about skewness, you would recognize it has no skew! It is perfectly centered around its mean. Alright. So, it is denoted in this way. N stands for normal, the tilde sign denotes it is a distribution and in brackets we have the mean and the variance of the distribution. On the plane, you can notice that the highest point is located at the mean, because it coincides with the mode. The spread of the graph is determined by the standard deviation. Now, let’s try to understand the normal distribution a little bit better. Website: https://365datascience.com Facebook: https://www.facebook.com/365datascience Twitter: https://twitter.com/365datascience LinkedIn: https://www.linkedin.com/company-beta/18061054/ Google+: https://plus.google.com/114636546494634370189/
Views: 469 365 Data Science
Methods of Performance Appraisal
 
29:00
Subject:Human Resource Management Paper: Performance and Compensation Management
Views: 23150 Vidya-mitra
Predictive Quality Control with STATISTICA Data Miner
 
05:17
I use STATISTICA Data Miner to create a predictive quality control method with some manufacturing data I was able to acquire.
Views: 459 DManswers
Normalizing Data
 
05:07
Normalizing Data
Views: 1412 KevinAtStout
What is Skewness?
 
03:25
What is Skewness? What are the different types of Skewness? To know more, visit https://DontMemorise.com Don’t Memorise brings learning to life through its captivating FREE educational videos. New videos every week. To stay updated, subscribe to our YouTube channel : http://bit.ly/DontMemoriseYouTube Register on our website to gain access to all videos and quizzes: http://bit.ly/DontMemoriseRegister Subscribe to our Newsletter: http://bit.ly/DontMemoriseNewsLetter Join us on Facebook: http://bit.ly/DontMemoriseFacebook Follow us : http://bit.ly/DontMemoriseBlog
Views: 131133 Don't Memorise
How to calculate Standard Deviation and Variance
 
05:05
Tutorial on calculating the standard deviation and variance for statistics class. The tutorial provides a step by step guide. Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos: How to Calculate Mean and Standard Deviation Using Excel http://www.youtube.com/watch?v=efdRmGqCYBk Why are degrees of freedom (n-1) used in Variance and Standard Deviation http://www.youtube.com/watch?v=92s7IVS6A34 Playlist of z scores http://www.youtube.com/course?list=EC6157D8E20C151497 David Longstreet Professor of the Universe Like us on: http://www.facebook.com/PartyMoreStudyLess Professor of the Universe: David Longstreet http://www.linkedin.com/in/davidlongstreet/ MyBookSucks.Com
Views: 1571070 statisticsfun
How to calculate Normalized z score
 
07:15
Tutorial on finding the mean, z score when you know the area (or probability). Playlist on Z scores http://www.youtube.com/course?list=EC6157D8E20C151497 Like MyBookSucks: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 32137 statisticsfun
Distribution Analysis Using SAS Studio
 
07:02
In this video, you learn how to use the Distribution Analysis task in SAS Studio. You learn how to request histograms with overlaid density curves and inset statistics, as well as a normal probability plot and fit statistics for assessing normality.
Views: 4234 SAS Software
Measures of data dispersion
 
05:01
Medical Statistics: Measures of Data Dispersion. In this video I take a look at variance and standard deviation.
Views: 35 Juan Klopper
Creating and Interpreting a Scatterplot Matrix in SPSS
 
12:38
This video demonstrates how to create and interpret a scatterplot matrix using in SPSS. A scatterplot matrix is useful for analyzing relationships between multiple variables at the same time.
Views: 40675 Dr. Todd Grande
Anomaly Detection: Algorithms, Explanations, Applications
 
01:26:56
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 12712 Microsoft Research
Creating a Random Process Fitted Line Plot in Excel 2007
 
07:17
I use data representing the amount of natural gas used in my home on a daily basis for 30 consecutive days to demonstrate the creation of a random process fitted line plot. I also show calculation of the statistic RMSE (Root Mean Square Error) and use it to represent the quality of fit. The random process model assumes the data fluctuates randomly around a constant level - which we estimate with the mean of the data we've collected.
Views: 1973 ProfTDub
Import Data, Analyze, Export and Plot in Python
 
16:16
A common task in data science is to analyze data from an external source that may be in a text or comma separated value (CSV) format. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. This demonstrates how to import data, perform a basic analysis such as average values, trend the results, save the figure, and export the results to another text file.
Views: 38128 APMonitor.com
Binomial distribution | Probability and Statistics | Khan Academy
 
11:52
PWatch the next lesson: https://www.khanacademy.org/math/probability/random-variables-topic/binomial_distribution/v/visualizing-a-binomial-distribution?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/random-variables-topic/expected-value/v/law-of-large-numbers?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 952132 Khan Academy
Constructing an ROC curve - Part I
 
09:36
The video describes how to analyze data from a recognition memory experiment to create a Receiver Operating Characteristic (ROC) curve, which indicates how well the person is able to distinguish things they studied from things they didn't study. We don't get too far into the theory here, this really will just let you see how to do the simple calculations that let you create the ROC curve! (this is part I where we set up the problem, in part II we actually plot the ROC)
Views: 56828 Sean Polyn
Frequency Polygons - Data Analysis with R
 
03:17
This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 8611 Udacity
Make a Histogram Using Excel's Histogram tool in the Data Analysis ToolPak
 
05:41
We will create a Histogram in Excel using the Histogram tool in the Data Analysis ToolPak, and we will let Excel choose the number of classes/bins to use.
Views: 14084 Vickie Dominguez
Probability based on data
 
02:03
Created with TechSmith Snagit for Google Chrome™ http://goo.gl/ySDBPJ
Views: 61 rberman217
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
 
15:12
LDA is surprisingly simple and anyone can understand it. Here I avoid the complex linear algebra and use illustrations to show you what it does so you will know when to use it and how to interpret the results. Sample code for R is at the StatQuest website: https://statquest.org/2016/07/10/statquest-linear-discriminant-analysis-lda-clearly-explained/ For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Find Number of Data Points within 1, 2 or 3 St Deviations in Excel
 
07:54
Learn how to use formulas in Excel to find out how many of the data points fall within 1, 2, or 3 standard deviations of the mean. For more help, visit my website: http://mathandstatshelp.com
Views: 437 Math and Stats Help
Checking for a normal distribution
 
03:51
Checking for a Normal Distribution on SPSS
Views: 15658 LoucollSport
Descriptive statistics in Excel
 
08:06
This brief tutorial provides a quick overview of descriptive statistics, specifically measures of: 1) Central tendency 2) Statistical dispersion 3) Distribution
Views: 690 leuschf
Data - processing excel table
 
10:01
processing raw data
Views: 3421 Derek Druce
But what *is* a Neural Network? | Deep learning, chapter 1
 
19:13
Subscribe to stay notified about new videos: http://3b1b.co/subscribe Support more videos like this on Patreon: https://www.patreon.com/3blue1brown Or don't. It's your call really, no pressure. Special thanks to these supporters: http://3b1b.co/nn1-thanks Additional funding provided by Amplify Partners. For any early-stage ML entrepreneurs, Amplify would love to hear from you: [email protected] Full playlist: http://3b1b.co/neural-networks Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! https://github.com/mnielsen/neural-networks-and-deep-learning I also highly recommend Chris Olah's blog: http://colah.github.io/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI https://youtu.be/bxe2T-V8XRs For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: https://distill.pub/ Lion photo by Kevin Pluck If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. Music by Vincent Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 3742805 3Blue1Brown
6k:175 Business Intelligence -- Binning data within XLMiner
 
07:58
Here I am describing how to bin data within XLMiner.
Views: 1468 Michael Rechenthin
Ever wonder how Bitcoin (and other cryptocurrencies) actually work?
 
26:21
Bitcoin explained from the viewpoint of inventing your own cryptocurrency. Videos like these made possible by patreon: https://patreon.com/3blue1brown Protocol Labs: https://protocol.ai/ Interested in contributing? https://protocol.ai/join/ Special thanks to the following patrons: http://3b1b.co/btc-thanks Some people have asked if this channel accepts contributions in cryptocurrency form as an alternative to Patreon. As you might guess, the answer is yes :). Here are the relevant addresses: ETH: 0x88Fd7a2e9e0E616a5610B8BE5d5090DC6Bd55c25 BTC: 1DV4dhXEVhGELmDnRppADyMcyZgGHnCNJ BCH: qrr82t07zzq5uqgek422s8wwf953jj25c53lqctlnw LTC: LNPY2HEWv8igGckwKrYPbh9yD28XH3sm32 Supplement video: https://youtu.be/S9JGmA5_unY Music by Vincent Rubinetti: https://soundcloud.com/vincerubinetti/heartbeat Here are a few other resources I'd recommend: Original Bitcoin paper: https://bitcoin.org/bitcoin.pdf Block explorer: https://blockexplorer.com/ Blog post by Michael Nielsen: https://goo.gl/BW1RV3 (This is particularly good for understanding the details of what transactions look like, which is something this video did not cover) Video by CuriousInventor: https://youtu.be/Lx9zgZCMqXE Video by Anders Brownworth: https://youtu.be/_160oMzblY8 Ethereum white paper: https://goo.gl/XXZddT If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. Music by Vince Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 2428610 3Blue1Brown
BUAD 425: Confusion Matrices with Pivot Tables
 
08:05
Creating a confusion matrix using pivot tables for a binary classifier for the loans dataset.
Views: 2372 Vishal Gupta
Correlation in Google Sheets - Multiple Variables
 
05:58
This video examines how to calculate a correlation in Google Sheets using multiple variables. All bi-variate (two at a time) correlations are produced.
Statistical Analysis, Research, and Modeling - Useful Free On-Line Resources
 
01:22
Check out the many useful links below! I find the sample size calculator and social research methods website to be particularly helpful. Research randomizer is not bad either, a quick way to randomize numbers for unbiased samples. Check out the NEW WEBSITE: https://arcologydesigns.com UPDATED BLOG: https://arcologydesigns.blogspot.com There's also some interesting research articles on tax compliance, population density and many other types of data sets that could prove useful. As always, please remember to give credit where it's due and use the materials below in accordance with any applicable TOS and/or EULAs. _____________________________________________________________________ WEB CENTER FOR Social Research Methods (There's a free on-line book on there) http://socialresearchmethods.net/ COMBINATIONS (nCr) CALCULATOR http://www.calculatorsoup.com/calculators/discretemathematics/combinations.php SAMPLE SIZE CALCULATOR http://surveysystem.com/sscalc.htm RESEARCH RANDOMIZER http://www.randomizer.org/ GAME THOERY http://msl1.mit.edu/ESD10/block4/4.4_-_Game_Theory.pdf FINANCE AND ECONOMICS DISCUSSION SERIES http://www.federalreserve.gov/Pubs/feds/2009/200901/ INTERNATIONAL POPULATION DENSITY DATA http://www.infoplease.com/ipa/A0934666.html INCOME TAX SIMULATION MODEL (UNIV. of ALASKA) http://www.iser.uaa.alaska.edu/Publications/workingpapers/WP_77.2_State_Personal_Income_Tax_SimModel.pdf TAX COMPLIANCE RESEARCH PAPER http://www.amstat.org/sections/srms/proceedings/papers/1987_014.pdf CREATING BELL CURVES IN EXCEL http://www.youtube.com/watch?v=50kZjl-7ZaQ 2000 CENSUS DATASET (Direct Link) http://www2.census.gov/census_2000/datasets/ CENSUS DATA ACCESS TOOLS http://www.census.gov/main/www/access.html DEP. of HOMELAND SECURITY ARCHIVES http://www.dhs.gov/archives ECON DATA http://web.ku.edu/~econmirr/data.html OECD DATA http://stats.oecd.org/Index.aspx PRIO NETWORK http://www.prio.no/ CORRELATES OF WAR http://www.correlatesofwar.org/ GEO DATA PROTAL http://geodata.grid.unep.ch/ ODUM INSTITUTE DATAVERSE NETWORK http://arc.irss.unc.edu/dvn/ IQSS DATAVERSE NETWORK http://dvn.iq.harvard.edu/dvn/ ICPSR DATAVERSE http://arc.irss.unc.edu/dvn/dv/ICPSR INTERNATIONAL STUDIES QUARTERLY DATA ARCHIVE http://www.isanet.org/data_archive.html _________________________________________________________________
Views: 1575 Grow Your Career
Creating Chatbots Using TensorFlow | Chatbot Tutorial | Deep Learning Training | Edureka
 
11:59
** AI & Deep Learning with Tensorflow Training : https://www.edureka.co/ai-deep-learning-with-tensorflow ** This Edureka video of "Chatbots using TensorFlow" gives you an idea about what are chatbots and how did they come into existence. It provides a brief introduction about all the layers involved in creating a chatbot using TensorFlow and Machine Learning. Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Check out our Deep Learning blog series: https://bit.ly/2xVIMe1 Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. --------------------------------- Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #Autoencoder #Tensorflow #DeepLearning #NeuralNetworks #python #MachineLearning #DimensionalityReduction ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 17171 edureka!
Lecture 4: Measures of dispersion using excel
 
03:30
Learn measures of dispersion using Excel For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected] Find all the study packs available with us here: http://analyticsuniversityblog.blogspot.in/ SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop
R Data Analysis Projects: Kernel Density Estimation| packtpub.com
 
05:51
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2Gi1Gzx]. Kernel density estimate techniques help find the underlying probability distribution. It helps find the probability density function for the given sample of data. Using KDE, we will find the distribution for positively oriented text and negatively oriented text. • Generate histogram for KDE For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 1483 Packt Video
Perseus network analysis tutorial/Jan Rudolph
 
01:38:20
All course materials can be found online: https://drive.google.com/drive/folders/11jVCEej9cSN9bzIwgBW0jGAdvBdcnQA7?usp=sharing Information about the tutorial: 3 new workflows in Perseus: 1. Hawaii (multi-volcano) plot: Analyze your pull-down screens in one go. The Hawaii plots offers the same interactivity as the regular volcano plot while providing global control over parameters and making it easier to compare different conditions. Visualize the resulting interaction network directly within Perseus. 2. Phosphoproteomics + PPI: Analyze your PTM data in the context of a PPI network such as STRING. Derive signaling functionality scores that allow you to identify which proteins significantly drive/suppress phosphorylation in your sample. 3.Co-expression analysis + phenotype/clinical: Cluster your data based on a co-expression network. Understand which clusters drive your phenotype by correlating it with proteins representative for each cluster. MaxQuant Summer school 2018 in Barcelona: website: http://summerschool.maxquant.de/summerschool2018/welcome.html program: http://summerschool.maxquant.de/summerschool2018/program.html All the MaxQuant Summer School presentations are going to be live streamed.
Views: 1104 Max Quant
Testing for correlations in data with Excel
 
04:57
Learn how to carry out tests for correlations in data using Microsoft Excel, including the Spearman’s rank correlation, and Pearson’s product moment correlation. https://global.oup.com/academic/product/research-methods-for-the-biosciences-9780198728498 This video relates to section 9.5 in the book Research Methods for the Biosciences third edition by Debbie Holmes, Peter Moody, Diana Dine, and Laurence Trueman. The video is narrated by Laurence Trueman. © Oxford University Press
Plotting Data in Excel
 
08:02
Tutorial on plotting data in Excel, and getting it to look half-way decent.
Views: 292 Brian Blais
Range Normalisation/Scaling 1/4: Normalisation
 
05:01
In these videos I show how you can normalise/denormalise numerical values to a certain range. I also show PHP implementation. My web page: www.imperial.ac.uk/people/n.sadawi
Views: 6957 Noureddin Sadawi
How to Use the Outliers Function in Excel
 
04:23
See more: http://www.ehow.com/tech/
Views: 57539 eHowTech
Progressives Getting Serious About Congressional Power
 
23:17
In this Majority Report clip, David Dayen on the new push for progressives on congressional committees. We need your help to keep providing free videos! Support the Majority Report's video content by going to http://www.Patreon.com/MajorityReport "After a wildly successful election for House Democrats, progressives in Congress did something relatively novel: They tried to wield power. Last week, the heads of the Congressional Progressive Caucus won a major concession from Democratic Leader Nancy Pelosi, but it’s now an open question whether they’ll have the foot soldiers to complete the mission. The particular play the CPC is making will have a dual end result, if successful. First, it will build progressive power within the House caucus, which is currently in scarce supply. And it will throw a wrench into the gears of the money-and-politics machinery of Washington, by making it more difficult for corporate-friendly Democrats to do legislative favors for lobbyists."* Read more here: https://theintercept.com/2018/11/21/progressive-caucus-congressional-progressive-caucus/ Watch the Majority Report live M–F at 12 p.m. EST at youtube.com/samseder or listen via daily podcast at http://Majority.FM Download our FREE app: http://majorityapp.com SUPPORT the show by becoming a member: http://jointhemajorityreport.com LIKE us on Facebook: http://facebook.com/MajorityReport FOLLOW us on Twitter: http://twitter.com/MajorityFM SUBSCRIBE to us on YouTube: http://youtube.com/SamSeder
How to choose bin sizes for histograms
 
04:55
A few simple rules for choosing bin sizes for histograms.
Views: 84223 Stephanie Glen
Python Tutorial: Exoplanet and Star Data Analysis
 
15:00
Find the code files here: https://github.com/whatdamath/spaceengine Hello and welcome to What Da Math! In this video, we will talk about analysis exoplanet data with Python Links: https://exoplanetarchive.ipac.caltech.edu/ https://exoplanets.nasa.gov https://seaborn.pydata.org/examples/horizontal_boxplot.html Code: %matplotlib inline import numpy as np import seaborn as sns import matplotlib.pyplot as plt import pandas as pd planets = pd.read_csv("planets.csv", sep=',') sns.set(style="ticks") # Initialize the figure with a logarithmic x axis f, ax = plt.subplots(figsize=(7, 6)) ax.set_xscale("log") # Load the example planets dataset #planets = sns.load_dataset("planets") # replace pl_pnum and st_mass with other column names sns.boxplot(x="pl_pnum", y="st_mass", data=planets) # Add in points to show each observation sns.swarmplot(x="pl_pnum", y="st_mass", data=planets, size=2, color=".3", linewidth=0) # Tweak the visual presentation ax.xaxis.grid(True) ax.set(ylabel="") sns.despine(trim=True, left=True) Support this channel on Patreon to help me make this a full time job: https://www.patreon.com/whatdamath Space Engine is available for free here: http://spaceengine.org Enjoy and please subscribe. Twitter: https://twitter.com/WhatDaMath Facebook: https://www.facebook.com/whatdamath Twitch: http://www.twitch.tv/whatdamath Bitcoins to spare? Donate them here to help this channel grow! 1GFiTKxWyEjAjZv4vsNtWTUmL53HgXBuvu
Views: 4325 Anton Petrov
Smoothing Conditional Means - Data Analysis with R
 
03:21
This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 4112 Udacity
10. Exercises on Normal Distribution
 
23:46
The lecture spends more time reinforcing the properties of a normal distribution discussed in previous lectures. We talk area under the curve within 1/2/3 standard deviations from the mean as per the normal/z table. Post this lecture the student should be able to grasp the concept of normally distributed variables, how mean and standard deviations can be used to create 90/95/99% confidence intervals around the mean. This playlist provides approximately 10 hours of our Analytics Training series. For more information, please visit www.learnanalytics.in . For enquiries drop an email to [email protected] . The training covers basic business statistics concepts and using tools such as SAS, SPSS , Statistica and R using Rattle. The objective of the training series is to prepare the student for a career in Data Analysis and the Analytics Industry in general. Please visit our website for further details. If you wish to subscribe to our full Analytics Training module, please visit http://goo.gl/nIJJHg for our paid Youtube Channel which contains additional hours covering more extensive topics including Linear Regression/Logistic Regression, building and testing predictive models using Logistic / Decision Trees and Ensembling. All videos on our paid channel are available without advertisement interruptions and you can enrol for a 14 day free subscription trial. You pay only if you want to continue. Additionally, you get access to the datasets discussed in the videos and all SAS Codes.
Views: 5083 Learn Analytics
Hoda Eldardiry -  predictive analytics, machine learning, data mining at PARC
 
02:32
Hoda Eldardiry (PhD Purdue) talks about her work on predictive analytics, using machine learning and data mining at Palo Alto Research Center (PARC).
Views: 4074 computingresearch
Mr Excel & excelisfun Trick 14: Trending Up Down Arrows
 
07:29
See Mr Excel and excelisfun create formulas and Conditional Formatting that will display UP, DOWN, and SIDE arrows to indicate up or down for a list of numbers. See functions like: SIGN, IF, and CHAR. Related videos: Dueling: Up/Down Symbols - 1035 - Learn Excel from MrExcel
Views: 50961 ExcelIsFun