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TIME SERIES ANALYSIS THE BEST EXAMPLE
 
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QUANTITATIVE METHODS TIME SERIES ANALYSIS
Views: 205924 Adhir Hurjunlal
Time Series: Measurement of Trend in Hindi under E-Learning Program
 
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It covers in detail various methods of measuring trend like Moving Averags & Least Square. Lecture by: Rajinder Kumar Arora, Head of Department of Commerce & Management
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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
Views: 73913 edureka!
Time Series Analysis: What is Stationarity?
 
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In this video you will learn what is a stationary series. It is an important property for AR, MA, ARIMA, Arch, Garch Models For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected] Find all free videos & study packs available with us here: http://analyticuniversity.com/ SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop
Views: 40848 Analytics University
Time Series - 1 Method of Least Squares - Fitting of Linear Trend - Odd number of years
 
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#Statistics #Time #Series #Business #Forecasting #Linear #Trend #Values #LeastSquares #Fitting #Odd Definitions  “A time series may be defined as a sequence of values of same variable corresponding to successive points in time.” – W. Z. Hersch  “A time series may be defined as a sequence of repeated measurement of a variable made periodically through time.” – Cecil H. Mayers Analysis of Time Series “The main object of analyzing time series is to understand, interpret and evaluate changes in economic phenomena in the hope of more correctly anticipating the course of future events.” – Hersch A time series is a dynamic distribution, which reveals a good deal of variations over time. Statistical methods are, therefore, required to analyze various types of movements in a time series. There may be cyclical variations in general business activity and there may be short duration seasonal variations. There are also some accidental and random variables. The primary purpose of the analysis of time series is to discover and measure all such types of variations, which characterize a time series. Time series analysis means analyzing the historical patterns of the variable that have occurred in past as a means of predicting the future value of the variable. It helps to identify and explain the following: (i) Any regular or systematic variation in the series of data which is due to seasonality- the ‘seasonal’ (ii) Cyclical patterns. (iii) Trends in the data. (iv) Growth rates of these trends. This method can be useful when no major environmental changes are expected and it does highlight seasonal variations in sales and consumer demand. However, time series analysis is limited when organizations face volatile environments. Components of Time series – The time series are classified into four basic types of variations which are analyzed below: T = Trend S = Seasonal variations C = Cyclic variations I = Irregular fluctuations. This composite series is symbolized by the following general terms: O = T x S x C x I Where O = Original data T = Trend S = Seasonal variations C = Cyclic variations I = Irregular components. This Multiplicative model is to be used when S, C, and I are given in percentages. If, however, their true (absolute) values are known the model takes the additive form i.e., O=T+C+S+I. Algebraic Method For Finding Trend (Method of curve fitting by the principle of Least Squares) Fitting of Linear Trend Let the straight line trend between the given time series values (y) and time (x) be given by the standard equation: y = a + bx Then for any given time ‘x’ the estimated value of ye as given by the equation is ye = a + bx The following two normal equations are used for estimating 'a' and 'b'. Σy = na + bΣx Σxy = aΣx + bΣx^2 When Odd No. of Years, [X = (Year – Origin) / Interval] Case Given below are the figures of sales (in '000 units) of a certain shop. Fit a straight line by the method of least square and show the estimate for the year 2017: Year: 2010 2011 2012 2013 2014 2015 2016 Sales: 125 128 133 135 140 141 143 Time Series, Linear Trend, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12 - www.prashantpuaar.com
Views: 97227 Prashant Puaar
Time Series Data and Index Numbers Lecture
 
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Time Series Data and Index Numbers Lecture
Semi average method in time series analysis
 
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Long term trend analysis in statistics
Time series analysis in hindi (काल श्रेणी विश्लेषण) ASO EXAM
 
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PLEASE SUBSCRIBE MY YOUTUBE CHANNEL. #investigatorexam #asoexam #timeseriesanalysis Comment fast . watch full video . and time series part link. https://www.youtube.com/watch?v=tc1THnIEhG8&t=326s other important information join my website My website URL https://rajexamtech.blogspot.com/2018/12/time-series-analysis-in-hindi.html अगर आप हमारे statistics के और विडियो देखना चाहते हैं तो नीचे दिए गये लिंक पर जाकर हमारी प्लेलिस्ट को देखें aso investigator https://www.youtube.com/watch?v=AKfbPqoJVFg&list=PLaDYmDETHCeIEafd-kw3_-l6bdJBJin_i Telegram join link for more details https://t.me/rajexa Sony typing tutor efficiency playlist join link https://www.youtube.com/playlist?list=PLaDYmDETHCeLEeGTsj6BiG0Z6nf85zEhw Facebook page link https://m.facebook.com/Rajasthan-Exam-Tech-knowledge-692708307813329/
Views: 6111 Rajasthan Exam Store
Panel Data Analysis | Econometrics | Fixed effect|Random effect | Time Series | Data Science
 
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This video is on Panel Data Analysis. Panel data has features of both Time series data and Cross section data. You can use panel data regression to analyse such data, We will use Fixed Effect Panel data regression and Random Effect panel data regression to analyse panel data. We will also compare with Pooled OLS , Between effect & first difference estimation For Analytics study packs visit : https://analyticuniversity.com Time Series Video : https://www.youtube.com/watch?v=Aw77aMLj9uM&t=2386s Logistic Regression using SAS: https://www.youtube.com/watch?v=vkzXa0betZg&t=7s Logistic Regression using R : https://www.youtube.com/watch?v=nubin7hq4-s&t=36s Support us on Patreon : https://www.patreon.com/user?u=2969403
Views: 76568 Analytics University
Time Series Analysis - An Introduction
 
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Quantitative Techniques in Management: Time Series Analysis - An Introduction; Video by Edupedia World (www.edupediaworld.com). All Rights Reserved. Have a look at the other videos on this topic: https://www.youtube.com/playlist?list=PLJumA3phskPH2vSufmMsrBUHbuoQY3G4R Browse through other subjects in our playlist: https://www.youtube.com/channel/UC6E97LDJTFJgzWU7G3CHILw/playlists?sort=dd&view=1
Views: 12614 Edupedia World
Time Series ARIMA Models
 
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Time Series ARIMA Models https://sites.google.com/site/econometricsacademy/econometrics-models/time-series-arima-models
Views: 257090 econometricsacademy
Forecasting - Simple moving average - Example 1
 
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In this video, you will learn how to find out the 3 month and 4 monthly moving average for demand forecasting.
Views: 208605 maxus knowledge
Excel - Time Series Forecasting - Part 1 of 3
 
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Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be This is Part 1 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Parts 2 and 3 upon completing Part 1. The links for 2 and 3 are in the video as well as above.
Views: 830158 Jalayer Academy
Time Series | Statistics by CA Raj K Agrawal
 
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To Buy Complete Classes visit www.studyathome.org or Call: 8737012345. StudyAtHome.org is a Online Platform, that provides CA/ CS/ CMA classes from India's Best Professors at your Home.
Views: 31680 Study At Home
Chapter 16: Time Series Analysis (1/4)
 
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Time Series Analysis: Introduction to the model; Seasonal Adjustment Method Part 1 of 4
Views: 186211 Simcha Pollack
Time Series ARIMA Models Example
 
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Time Series ARIMA Models Example https://sites.google.com/site/econometricsacademy/econometrics-models/time-series-arima-models
Views: 117038 econometricsacademy
ARMA(1,1) processes - introduction and examples
 
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In this video I explain what is meant by an ARMA(1,1) process, and provide a couple of examples of processes which could be modelled as thus. Check out http://www.oxbridge-tutor.co.uk/undergraduate-econometrics-course for course materials, and information regarding updates on each of the courses. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti
Views: 93662 Ben Lambert
Ratio to Trend Method
 
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This is the method of calculating seasonal variation. In ratio to trend method, we will calculate annual trend values. Then on this basis, we will calculate quarterly trend value. Now, it will be easy for us to calculate the ratio of original value to trend value which will be the seasonal indices.
Views: 33909 Svtuition
White Noise Process : Time Series
 
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In this video you will learn what is a white noise process and why it is important to check for presence of white noise in time series data For study pack : http://analyticuniversity.com/
Views: 19040 Analytics University
What are Multivariate Time Series Models
 
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Multivariate time series models are different from that of Univariate Time Series models in a way that it also takes structural forms that is it includes lags of different time series variable apart from the lags of it's own. For Study Packs Visit : http://analyticuniversity.com/
Views: 27919 Analytics University
Working with Time Series Data in MATLAB
 
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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 54258 MATLAB
How DTW (Dynamic Time Warping) algorithm works
 
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In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them. Source code of graphs available at https://github.com/tkorting/youtube/blob/master/how-dtw-works.m The presentation was created using as references the following scientific papers: 1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49. 2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009. 3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf
Views: 39553 Thales Sehn Körting
Introduction to Bayesian Structural Time Series
 
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This video is the first video in the Adventures in BSTS series. ****link to our Git Repository that contains all slides and data used in this tutorial series**** https://github.com/asbates/bayes-time-series ****link to the bsts package documentation**** https://cran.r-project.org/web/packages/bsts/bsts.pdf
Views: 1958 Joshua Gloyd
Multilevel Time Series Analysis,  Mplus Short Course Topic 12, Part 2
 
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Mplus Short Course Topic 11: Regression and Mediation Analysis Part 9 - Missing Data Analysis Link to handouts associated with this segment (slides 13-22): http://www.statmodel.com/download/Part%201%20and%202%20Hamaker.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 171 Mplus
Detecting AR & MA using ACF and PACF plots | Time Series
 
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In this video you will learn how to detect AR & MA series by using ACF & PACF function plots . Detecting the order of AR, MA is important while building ARIMA model . It also is important when building variance forecasting models like Arch & Garch For Study packs visit : http://analyticuniversity.com/. Contact us for training, consulting or guidance : [email protected] Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx
Views: 63340 Analytics University
Auto Correlation Function in Time Series Analysis | Foresting
 
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In this video you will learn what is Auto correlation function and what is it used for in time series analysis For Analytics Study Pack visit : http://analyticuniversity.com/ For training, mentorship contact us at [email protected] Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx
Views: 36080 Analytics University
Two Effective Algorithms for Time Series Forecasting
 
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In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting. Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 42132 InfoQ
ARIMA modeling (video 1) in SPSS: model identification
 
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Be sure to visit my website at: https://sites.google.com/view/statistics-for-the-real-world/home This video is the first of several on ARIMA modeling using IBM SPSS. Specifically, it focuses on how to identify AR and MA processes. It also covers the topic of stationarity and identification of trending. (Be sure to check out the next video in the series on estimating ARIMA model parameters using SPSS syntax. Example syntax can be accessed through links in the video description) A copy of the original dataset can be downloaded here: https://drive.google.com/open?id=1gT2FbgUeZHIAG5vKctUrJWM--pbkXWRk The demonstrations provided in this video come from Chapter 18 of Tabachnick & Fidell's text, Using Multivariate Statistics (6th edition; https://www.pearson.com/us/higher-education/program/Tabachnick-Using-Multivariate-Statistics-6th-Edition/PGM332849.html) The chapter is downloadable from the textbook website at: http://media.pearsoncmg.com/ab/ab_tabachnick_multistats_6/datafiles/M18_TABA9574_06_SE_C18.pdf For more details of the computations involved, you can go here: https://youtu.be/WlSz0Ji19PM
Views: 16668 Mike Crowson
Time Series Analysis with Python Intermediate | SciPy 2016 Tutorial | Aileen Nielsen
 
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Tutorial materials for the Time Series Analysis tutorial including notebooks may be found here: https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython See the complete SciPy 2016 Conference talk & tutorial playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6.
Views: 63238 Enthought
Create Time Series Dialog in SPSS
 
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This video demonstrates how to use the “Create Times Series” dialog in SPSS. Functions such as difference, cumulative sum, lag, and lead are reviewed.
Views: 29789 Dr. Todd Grande
Time Series Analysis and Forecast - Tutorial  1 - Concept
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 10763 iman
Jeffrey Yau: Time Series Forecasting using Statistical and Machine Learning Models | PyData NYC 2017
 
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PyData New York City 2017 Time series data is ubiquitous, and time series modeling techniques are data scientists’ essential tools. This presentation compares Vector Autoregressive (VAR) model, which is one of the most important class of multivariate time series statistical models, and neural network-based techniques, which has received a lot of attention in the data science community in the past few years.
Views: 33690 PyData
Strategy for IAS 2019 : UPSC Syllabus Analysis : Series 1 : Crack IAS / UPSC 2018
 
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Views: 262785 IAS with Ojaank Sir
Econometrics // Lecture 1: Introduction
 
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This is an introduction to econometrics tutorial. This video is a basic overview and touches on each of these subjects: 1. What is Econometrics? 2. Goals of Econometrics 3. Types of Economic Data 4. The "Simple Linear Regression" (SLR) 5. Causality This lecture on econometric theory is meant to introduce the student to the concepts of econometrics, as well as provide a basic overview of what the topic of econometrics encompasses. The next video tutorial on simple linear regressions: http://youtu.be/CBa8frhRKMw Follow us on Twitter @ https://twitter.com/KeynesAcademy All video, images, commentary and music is owned by Keynes Academy.
Views: 347213 KeynesAcademy
Time series in Stata®, part 5: Introduction to ARMA/ARIMA models
 
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Learn how to fit ARMA/ARIMA models in Stata. Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 109132 StataCorp LLC
Regression: Example 2 || Time Series Analysis || Demand Forecasting || Method of Least Squares
 
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Regression is the most important measure in statistical analysis. Most of the analysis in research is built around correlation and regression. This video though talks of regression being used in time series analysis to make demand or other forecasts. The pdf of the question is available at https://goo.gl/J2JPj7 more practice questions are available at https://goo.gl/XY8u2c Visit us at: http://www.adityaclasses.co.in Visit us at: http://adityaclassesbikaner.tumblr.com Like us on Facebook: http://www.facebook.com/AdityaClassesBikaner Follow us on Twitter: http://www.twitter.com/AdityaClasses Follow us on Instagram: https://www.instagram.com/AdityaClassesBikaner Connect on Linkedin: https://www.linkedin.com/in/AdityaClassesBikaner We're here: https://in.pinterest.com/AdityaClassesBikaner
Views: 115 Aditya Classes
Intro to Hypothesis Testing in Statistics - Hypothesis Testing Statistics Problems & Examples
 
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Get the full course at: http://www.MathTutorDVD.com The student will learn the big picture of what a hypothesis test is in statistics. We will discuss terms such as the null hypothesis, the alternate hypothesis, statistical significance of a hypothesis test, and more. In this step-by-step statistics tutorial, the student will learn how to perform hypothesis testing in statistics by working examples and solved problems.
Views: 1392491 mathtutordvd
DataChats | Episode 12 | An Interview With David Stoffer
 
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In this episode of DataChats Lore talks with David Stoffer. Interested in learning more? Start David's ARIMA Modeling with R course today: https://www.datacamp.com/courses/arima-modeling-with-r David Stoffer is a Professor of Statistics at the University of Pittsburgh. He is member of the editorial board of the Journal of Time Series Analysis and Journal of Forecasting. David is the coauthor of the book "Time Series Analysis and Its Applications: With R Examples", which is the basis of his course. Another (free) book he wrote on Time Series Analysis is available here: http://www.stat.pitt.edu/stoffer/tsa4/tsaEZ.pdf Together with Lore, David talks about his path to Statistics, his teaching method, his latest book, how he got into R, and much more.
Views: 907 DataCamp
The Speech that Made Obama President
 
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In 2004, a one-term senator from Illinois took the stage to deliver the keynote speech at the Democratic National Convention in Boston. By the time Barack Obama had finished speaking, Democrats across the country knew they had seen the future of their party. Political speech experts featured in this episode include: Michael A. Cohen Author, Live From The Campaign Trail Mario Cuomo Former Governor of New York Robert Lehrman Chief Speechwriter for Vice President Gore and Professor of Speechwriting, American University Charlton McIlwain Professor of Communication, New York University Jeff Shesol Speechwriter for President Clinton and Founding Partner, West Wing Writers PODIUM is a bi-weekly series that embraces the art of public speaking and honors those with something to say. From historic political speeches, to contemporary commencement addresses, to wedding toasts, the series explores various genres of speechmaking and provides inspiring, insightful analysis including "how-to" content. Created and produced by @radical.media, THNKR gives you extraordinary access to the people, stories, places and thinking that will change your mind. Follow @THNKR on Twitter for the latest! Like us on Facebook: http://www.facebook.com/thnkrtv Check out what we're into on Tumblr: http://thnkrtv.tumblr.com/
Views: 15335591 THNKR
Qualitative analysis of interview data: A step-by-step guide
 
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The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Text and video (including audio) © Kent Löfgren, Sweden
Views: 748838 Kent Löfgren
Learn Python - Full Course for Beginners
 
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This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗If you liked this video, Mike accepts donations on his website: https://www.mikedane.com/contribute/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 5758136 freeCodeCamp.org
Introducing Time Series
 
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Statistics_stat-11-12-time-series2.mp4
Views: 433 Sabaq. Pk
Time Series Analysis Lecture 01c
 
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Lecture Content by Dr. William W.S. Wei, Professor of Statistics, Temple University, Philadelphia, PA, USA. Presented by J.J. Singh, Partap University. Time Series Analysis realization of stochastic process, omega w sample space, time t index set
Views: 942 partapuniversity
Yojana योजना magazine March 2019 - UPSC / IAS / PSC aspirants के लिए analysis
 
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Flat 60% #Discount on #Pendrive_Courses for Various Govt. Exams. Click here to know more - https://goo.gl/aTFK6Q or #Call_9580048004. Discount VALID till 30th April. UPSCIQ - A Magazine for UPSC IAS Aspirant http://bit.ly/2DH1ZWq Download All Videos PDFs - https://goo.gl/X8UMwF || Join StudyIQ on Telegram - https://goo.gl/xBR3g8 UPSC/CSE 2019 - https://goo.gl/UrCD46 SSC & Bank - https://goo.gl/9LQ4Ai UPSC Optionals - https://goo.gl/rtmXRU State PSCs - https://goo.gl/FDB32q Defence Exams - https://goo.gl/UEmtRz SSC JE Exams - https://goo.gl/2WyU1Z RBI Grade B - https://goo.gl/PY32m6 NABARD Grade A - https://goo.gl/C6CzAL DMRC Exams - https://goo.gl/yDnvyf Insurance Exams - https://goo.gl/iLEFxf CLAT 2019 - https://goo.gl/Burjtj Railway Jobs - https://goo.gl/5KaL7h Teaching Jobs - https://goo.gl/q117TX UPSC Prelim 2019Test Series -https://goo.gl/zkCG51 Free PDFs - https://goo.gl/cJufZc || Free Quiz - https://goo.gl/wCxZsy || Free Video Courses - https://goo.gl/jtMKP9" Follow us on Facebook - https://goo.gl/iAhPDJ Telegram - https://t.me/Studyiqeducation The Hindu Editorial Analysis - https://goo.gl/vmvHjG Current Affairs by Dr Gaurav Garg - https://goo.gl/bqfkXe UPSC/IAS Burning Issues analysis- https://goo.gl/2NG7vP World History for UPSC - https://goo.gl/J7DLXv Indian History - https://goo.gl/kVwB79 Follow us on Facebook - https://goo.gl/iAhPDJ Follow Dr Gaurav Garg on Facebook - https://goo.gl/xqLaQm UPSC/IAS past papers questions - https://goo.gl/F5gyWH SSC CGL + IBPS Quantitative tricks - https://goo.gl/C6d9n8 English Vocabulary - https://goo.gl/G9e04H Reasoning tricks for Bank PO + SSC CGL- https://goo.gl/a68WRN Error spotting / Sentence correction https://goo.gl/6RbdjC Static GK complete- https://goo.gl/kB0uAo Complete GK + Current Affairs for all exams- https://goo.gl/MKEoLy World History - UPSC / IAS - https://goo.gl/kwU9jC Learn English for SSC CGL, Bank PO https://goo.gl/MoL2it Science and Technology for UPSC/IAS - https://goo.gl/Jm4h8j Philosophy for UPSC/IAS - https://goo.gl/FH9p3n Yojana Magazine analysis -https://goo.gl/8oK1gy History for SSC CGL + Railways NTPC - https://goo.gl/7939eV
Views: 47947 Study IQ education
Introduction to Pivot Tables, Charts, and Dashboards in Excel (Part 1)
 
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WATCH PART 2: https://www.youtube.com/watch?v=g530cnFfk8Y Download file used in the video: http://www.excelcampus.com/pivot-table-checklist-yt In this video series you will learn how to create an interactive dashboard using Pivot Tables and Pivot Charts. Works with Excel 2003, 2007, 2010, 2013 for Windows & Excel 2011 for Mac Don't worry if you have never created a Pivot Table before, I cover the basics of formatting your source data and creating your first Pivot Table as well. You will also get to see an add-in I developed named PivotPal that makes it easier to work with some aspects of Pivot Tables. Download the files to follow along at the following link. http://www.excelcampus.com/pivot-table-checklist-yt I have another video that shows how to reformat the pivot chart in Excel 2010. In the video above I'm using Excel 2013 and the menus are different from Excel 2007/2010. Here is the link to that video. http://www.youtube.com/watch?v=Jt_QqG-vRRw Get PivotPal: http://www.excelcampus.com/pivotpal Free webinar on The 5 Secrets to Understanding Pivot Tables: https://www.excelcampus.com/pivot-webinar-yt Subscribe to my free newsletter: http://www.excelcampus.com/newsletter
Views: 7017936 Excel Campus - Jon
Preview: Spatial autoregressive models in Stata 15
 
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spreg estimates the parameters of a cross-sectional spatial autoregressive model with spatial autoregressive disturbances, which is known as a SARAR model. A SARAR model includes a weighted average of the dependent variable, known as a spatial lag, as a right-hand-side variable, and it allows the disturbance term to depend on a weighted average of the disturbances corresponding to other units. The weights may differ for each observation and are frequently inversely related to the distance from the current observation. spreg estimates the parameters by either maximum likelihood (ML) or by generalized spatial two-stage least squares (GS2SLS). For more information about spatial autoregressive models in Stata, see http://www.stata.com/new-in-stata/spatial-autoregressive-models/. Copyright 2017 StataCorp LLC. All rights reserved.
Views: 5465 StataCorp LLC
The Zipf Mystery
 
21:05
The of and to. A in is I. That it, for you, was with on. As have ... but be they. RELATED LINKS AND SOURCES BELOW! http://www.twitter.com/tweetsauce http://www.instagram.com/electricpants WordCount.org http://www.wordcount.org/ How many days have you been alive? http://www.beatcanvas.com/daysalive.asp random letter generator: http://www.dave-reed.com/Nifty/randSeq.html Dictionary of Obscure Sorrows: https://www.youtube.com/user/obscuresorrows Word frequency resources: [lemmatized] https://en.wikipedia.org/wiki/Most_common_words_in_English http://www.uow.edu.au/~dlee/corpora.htm http://www.wordfrequency.info http://www.anc.org/data/anc-second-release/frequency-data/ http://www.titania.bham.ac.uk/docs/ http://www.kilgarriff.co.uk/bnc-readme.html#raw https://en.wiktionary.org/wiki/Wiktionary:Frequency_lists http://ucrel.lancs.ac.uk/bncfreq/ [PDF] http://www.wordfrequency.info/files/entries.pdf [combined Wikipedia and Gutenberg] http://www.monlp.com/2012/04/16/calculating-word-and-n-gram-statistics-from-a-wikipedia-corpora/ http://corpus.byu.edu/coca/files/100k_samples.txt http://corpus.byu.edu/ http://corpus.leeds.ac.uk/list.html https://books.google.co.uk/books?id=ja1_AAAAQBAJ&dq=word+frequency+coca&lr= http://www.ling.helsinki.fi/kit/2009s/clt231/NLTK/book/ch01-LanguageProcessingAndPython.html Great Zipf's law papers: http://colala.bcs.rochester.edu/papers/piantadosi2014zipfs.pdf http://www.ling.upenn.edu/~ycharles/sign708.pdf http://arxiv.org/pdf/cond-mat/0412004.pdf http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf Zipf’s law articles and discussions: http://www.theatlantic.com/magazine/archive/2002/04/seeing-around-corners/302471/ http://io9.com/the-mysterious-law-that-governs-the-size-of-your-city-1479244159?utm_expid=66866090-48.Ej9760cOTJCPS_Bq4mjoww.0 https://plus.maths.org/content/os/latestnews/may-aug08/food/index http://judson.blogs.nytimes.com/2009/05/19/math-and-the-city/?em https://plus.maths.org/content/mystery-zipf?src=aop http://www.datasciencecentral.com/profiles/blogs/why-zipf-s-law-explains-so-many-big-data-and-physics-phenomenons https://en.wikipedia.org/wiki/Zipf%27s_law https://books.google.co.uk/books?id=f8GrzlnMSm8C&pg=PA62&redir_esc=y#v=onepage&q&f=false http://arxiv.org/pdf/0802.4393v1.pdf http://www.pnas.org/content/108/9/3526.full http://lewisdartnell.com/language_page.htm http://wugology.com/zipfs-law/ other Zipf’s law PDFs http://ftp.iza.org/dp3928.pdf http://arxiv.org/pdf/1402.2965.pdf http://arxiv.org/pdf/1104.3199.pdf http://www.lel.ed.ac.uk/~jim/zipfjrh.pdf http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2834740/#pone.0009411-Mandelbrot1 http://polymer.bu.edu/hes/articles/pgs02a.pdf in untranslated language: http://arxiv.org/pdf/0808.2904.pdf http://pages.stern.nyu.edu/~xgabaix/papers/zipf.pdf http://www.hpl.hp.com/research/idl/papers/ranking/ranking.html http://statweb.stanford.edu/~owen/courses/306a/ZipfAndGutenberg.pdf http://arxiv.org/pdf/1310.0448v3.pdf http://www.kornai.com/Papers/glotto5.pdf Zipf’s law slides: http://www.slideshare.net/guest9fc47a/nlp-new-words Pareto Principle and related ‘laws’: http://www.squawkpoint.com/2013/03/pareto-principle/ http://billyshall.com/blog/post/paretos-principle https://en.wikipedia.org/wiki/Pareto_principle Random typing and Zipf: http://www.longtail.com/the_long_tail/2006/09/is_zipfs_law_ju.html health 80/20: http://archive.ahrq.gov/research/findings/factsheets/costs/expriach/expriach1.html Principle of least effort: https://en.wikipedia.org/wiki/Principle_of_least_effort https://en.wikipedia.org/wiki/Satisficing http://www.pnas.org/content/100/3/788.full.pdf [PDF] http://csiss.org/classics/content/99 self organized criticality: http://journal.frontiersin.org/article/10.3389/fnsys.2014.00166/full Hapax Legomenon: http://campus.albion.edu/english/2011/02/15/hapax-legomenon/ http://www.dailywritingtips.com/is-that-a-hapax-legomenon/ https://en.wikipedia.org/wiki/Hapax_legomenon [PDF] http://www.aclweb.org/anthology/J10-4003 http://www.wired.com/2012/01/hapax-legomena-and-zipfs-law/ http://oed.hertford.ox.ac.uk/main/content/view/402/450/index.html#_ftn1 http://oed.hertford.ox.ac.uk/main/content/view/36/166/index.html Learning curve: https://en.wikipedia.org/wiki/Learning_curve Forgetting curve: http://www.trainingindustry.com/wiki/entries/forgetting-curve.aspx https://en.wikipedia.org/wiki/Forgetting_curve Experience curve effects: https://en.wikipedia.org/wiki/Experience_curve_effects Forgetting and zipf's law: http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/37JRA_LS_PS_1991.pdf http://public.psych.iastate.edu/shacarp/Wixted_Carpenter_2007.pdf http://marshalljonesjr.com/youll-remember-less-than-001-of-your-life/ https://en.wikipedia.org/wiki/Forgetting https://www.reddit.com/r/Showerthoughts/comments/3gu9qk/it_only_takes_three_generations_for_you_to_be/ music from: http://www.youtube.com/jakechudnow http://www.audionetwork.com
Views: 13747608 Vsauce
Best 200 MARCH 2019 Current Affairs in Hindi Part 1 - Finest MCQ for all exams by Study IQ
 
32:21
Flat 60% #Discount on #Pendrive_Courses for Various Govt. Exams. Click here to know more - https://goo.gl/aTFK6Q or #Call_9580048004. Discount VALID till 30th April. UPSCIQ - A Magazine for UPSC IAS Aspirant http://bit.ly/2DH1ZWq Download All Videos PDFs - https://goo.gl/X8UMwF || Join StudyIQ on Telegram - https://goo.gl/xBR3g8 UPSC/CSE 2019 - https://goo.gl/UrCD46 SSC & Bank - https://goo.gl/9LQ4Ai UPSC Optionals - https://goo.gl/rtmXRU State PSCs - https://goo.gl/FDB32q Defence Exams - https://goo.gl/UEmtRz SSC JE Exams - https://goo.gl/2WyU1Z RBI Grade B - https://goo.gl/PY32m6 NABARD Grade A - https://goo.gl/C6CzAL DMRC Exams - https://goo.gl/yDnvyf Insurance Exams - https://goo.gl/iLEFxf CLAT 2019 - https://goo.gl/Burjtj Railway Jobs - https://goo.gl/5KaL7h Teaching Jobs - https://goo.gl/q117TX UPSC Prelim 2019Test Series -https://goo.gl/zkCG51 Free PDFs - https://goo.gl/cJufZc || Free Quiz - https://goo.gl/wCxZsy || Free Video Courses - https://goo.gl/jtMKP9" Follow us on Facebook - https://goo.gl/iAhPDJ Telegram - https://t.me/Studyiqeducation The Hindu Editorial Analysis - https://goo.gl/vmvHjG Current Affairs by Dr Gaurav Garg - https://goo.gl/bqfkXe UPSC/IAS Burning Issues analysis- https://goo.gl/2NG7vP World History for UPSC - https://goo.gl/J7DLXv Indian History - https://goo.gl/kVwB79 Follow us on Facebook - https://goo.gl/iAhPDJ Follow Dr Gaurav Garg on Facebook - https://goo.gl/xqLaQm UPSC/IAS past papers questions - https://goo.gl/F5gyWH SSC CGL + IBPS Quantitative tricks - https://goo.gl/C6d9n8 English Vocabulary - https://goo.gl/G9e04H Reasoning tricks for Bank PO + SSC CGL- https://goo.gl/a68WRN Error spotting / Sentence correction https://goo.gl/6RbdjC Static GK complete- https://goo.gl/kB0uAo Complete GK + Current Affairs for all exams- https://goo.gl/MKEoLy World History - UPSC / IAS - https://goo.gl/kwU9jC Learn English for SSC CGL, Bank PO https://goo.gl/MoL2it Science and Technology for UPSC/IAS - https://goo.gl/Jm4h8j Philosophy for UPSC/IAS - https://goo.gl/FH9p3n Yojana Magazine analysis -https://goo.gl/8oK1gy History for SSC CGL + Railways NTPC - https://goo.gl/7939eV
Views: 260710 Study IQ education
The Bermuda Triangle Mystery Has Been Solved
 
04:26
Scientists May Have Finally Cracked the Greatest Mystery Behind the Bermuda Triangle. How many creepy stories about the disappearing of airplanes and ships in the Bermuda Triangle have you heard? We guess many. It is a large area in the Atlantic Ocean between Florida, Puerto Rico, and Bermuda. This region is notorious for its mysterious phenomena. Huge amounts of ships and planes have disappeared here. Its second name is ‘The devil’s triangle.' All those mythical vanishings happened under unknown and unexplained circumstances. Some of the planes and ships have never been found. There have been many theories about why it all occurs in that area starting from waterspouts to aliens and even sea monsters. But those were only theories... TIMESTAMPS Where is the Bermuda Triangle? 0:22 Theories 0:47 A big incident in 2005 1:40 Stories behind the Bermuda triangle 2:50 The main mystery solved 3:08 SUMMARY - In 1945 five American torpedo bombers and a plane that was sent out to find them vanished without a trace. - 75 aircraft and several hundred ships have been lost. - The latest tragedy happened in 2015 when the cargo ship "El Faro" disappeared in this region. - In 2005 the first Piper-PA airplane disappeared in the Bermuda Triangle. - In 2007 one more Piper-PA airplane disappeared near Berry Island. Many stories are whirling around this area. But now, finally, the mystery behind the Bermuda Triangle, might have been solved. Subscribe to Bright Side : https://goo.gl/rQTJZz ---------------------------------------------------------------------------------------- Our Social Media: Facebook: https://www.facebook.com/brightside/ Instagram: https://www.instagram.com/brightgram/ 5-Minute Crafts Youtube: https://www.goo.gl/8JVmuC  ---------------------------------------------------------------------------------------- For more videos and articles visit: http://www.brightside.me/
Views: 22257883 BRIGHT SIDE
What is Philosophy?: Crash Course Philosophy #1
 
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Today Hank begins to teach you about Philosophy by discussing the historical origins of philosophy in ancient Greece, and its three main divisions: metaphysics, epistemology, and value theory. He will also introduce logic, and how you’re going to use it to understand and critically evaluate a whole host of different worldviews throughout this course. And also, hopefully, the rest of your life. -- Images and video via VideoBlocks or Wikimedia Commons, licensed under Creative Commons by 4.0: https://creativecommons.org/licenses/by/4.0/ -- Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Crash Course Philosophy is sponsored by Squarespace. http://www.squarespace.com/crashcourse -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support CrashCourse on Patreon: http://www.patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 3644382 CrashCourse

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