PyData LA 2018 How can we estimate the impact of a historical event where there is no way to run a controlled experiment? For example, we may want to assess the impact of a TV campaign or account for lost sales during an outage. This talk presents a brief overview of interrupted time series analysis, a technique commonly used in econometrics and public health that is designed to address this type of problem. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1027 PyData
NURS.605. Policy Analysis and Development. Spring 2018. Interrupted Time Series Analysis. Athabasca University -- Created using Powtoon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 434 Christine Kluczynski
Take this course for on edX: https://www.edx.org/course/policy-analysis-using-interrupted-time-ubcx-itsx ↓ More info below. ↓ Follow on Facebook: www.facebook.com/edx Follow on Twitter: www.twitter.com/edxonline Follow on YouTube: www.youtube.com/user/edxonline ABOUT THIS COURSE Interrupted time series analysis and regression discontinuity designs are two of the most rigorous ways to evaluate policies with routinely collected data. ITSx comprehensively introduces analysts to interrupted time series analysis (ITS) and regression discontinuity designs (RD) from start to finish, including definition of an appropriate research question, selection and setup of data sources, statistical analysis, interpretation and presentation, and identification of potential pitfalls. At the conclusion of the course, students will have all the tools necessary to propose, conduct and correctly interpret an analysis using ITS and RD approaches. This will help them position themselves as a go-to person within their company, government department, or academic department as the technical expert on this topic. ITS and RD designs avoid many of the pitfalls associated with other techniques. As a result of their analytic strength, the use of ITS and RD approaches has been rapidly increasing over the past decade. These studies have cut across the social sciences, including: Studying the effect of traffic speed zones on mortality Quantifying the impact of incentive payments to workers on productivity Assessing whether alcohol policies reduce suicide Measuring the impact of incentive payments to physicians on quality of care Determining whether the use of HPV vaccination influences adolescent sexual behavior WHAT YOU'LL LEARN The strengths and drawbacks of ITS and RD studies Data requirements, setup, and statistical modelling Interpretation of results for non-technical audiences Production of compelling figures
Views: 3490 edX
Watch this brief (10 minutes or so!!) video tutorial on how to do all the calculations required for a Time Series analysis of data on Microsoft Excel. Try and do your best to put up with the pommie accent. The data for this video can be accessed at https://sites.google.com/a/obhs.school.nz/level-3-statistics-and-modelling/time-series
Views: 106084 mrmathshoops
Table of Contents: 00:27 - Agenda 00:35 - Quasi-experimental design 01:23 - Quasi-experimental design: pretest-posttest nonequivalent comparison group 02:59 - Quasi-experimental design: pretest-posttest nonequivalent comparison group 03:17 - Quasi-experimental design: pretest-posttest nonequivalent comparison group 03:42 - Quasi-experimental design: pretest-posttest nonequivalent comparison group 03:42 - Quasi-experimental design: a variation on above—the double pretest 03:42 - Quasi-experimental design: single (interrupted) time series 03:44 - Quasi-experimental design: a variation on above—the double pretest 03:44 - Quasi-experimental design: pretest-posttest nonequivalent comparison group 03:45 - Quasi-experimental design: pretest-posttest nonequivalent comparison group 04:05 - Quasi-experimental design: pretest-posttest nonequivalent comparison group 05:57 - Quasi-experimental design: a variation on above—the double pretest 07:25 - Quasi-experimental design: single (interrupted) time series 08:20 - Hair dryer electrocutions: interrupted time series 09:56 - Quasi-experimental design: single time series 10:50 - Hair dryer electrocutions: interrupted time series 10:50 - Quasi-experimental design: single (interrupted) time series 11:37 - Hair dryer electrocutions: interrupted time series 11:37 - Quasi-experimental design: single time series 12:13 - Interpreting single time series observations 13:56 - Quasi-experimental design: multiple time series 14:22 - Quasi-experimental design: multiple time series 14:41 - Quasi-experimental design: multiple time series example 16:28 - Traffic cameras and injuries/deaths in France 18:17 - Quantifying the effect (ARIMA models) 19:00 - Traffic cameras and injuries/deaths in France 19:03 - Quantifying the effect (ARIMA models) 19:04 - Summary of quasi-experimental design 19:50 - Threats to validity of quasi-experimental design
Views: 4871 EPIC Principles of Program Evaluation
In this video you will learn how to perform regression when your data has time series variables 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: 7238 Analytics University
This video provides an introduction to time series data by a comparison of this data with cross-sectional data. 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: 75845 Ben Lambert
PyData Ann Arbor Meetup - December 7, 2017 Sponsored by NumFOCUS and TD Ameritrade https://www.meetup.com/PyData-Ann-Arbor/ Drew Fustin | Interrupted Time Series Experiments in Python Although ideal experimental design involves hypothesis testing with randomized controlled trials on concurrent populations to minimize selection bias and convoluting variables, it often arises that experiments cannot be run on variant populations simultaneously -- for instance, when a stimulus necessarily impacts the entire population at a specific point in time (as in measuring a non-digital ad campaign's effectiveness). Dealing with these situations is common in the social sciences, where the method of Interrupted Time Series Analysis is commonly used. In order to measure the effect size of a stimulus in situations such as these, we have to consider many convoluting factors caused by our populations being non-concurrent before deriving meaning from the experimental results. In this Python-based tutorial, we will walk through a Monte Carlo-generated experiment measuring the lift induced by a simulated ad campaign. By the end of this tutorial, you will understand many complicating factors that could lead to faulty conclusions being drawn from the experimental results. However, you will also learn how best to design experiments and interpret results to mitigate these risks and take proper account of irreducible convoluting factors. About Drew Fustin is a former physicist and current data scientist in Chicago. He created and led the data science organization at SpotHero, focusing primarily on optimizing acquisition marketing spend and balancing supply and demand to generate inventory and rate recommendations. He's also worked for GrubHub as the insights analyst, turning food facts into media content for the PR department and transforming data into actionable initiatives within the organization. He was also a data scientist with Digital H2O, a SaaS startup providing water intelligence for the oil/gas industry. He holds a PhD in physics from the University of Chicago, where he studied dark matter by looking for tiny bubbles in a chamber over a mile underground in a Canadian nickel mine.
Views: 1213 PyData
When the circumstances are right, regression discontinuity can be an excellent way to extract causal estimates from observational data. In this video I give you a prototypical situation where RD is applicable and explain how it works. I also describe situations where the method fails and say a few words about fuzzy discontinuities. Intended audience: Folks who have had some exposure to linear regression models, but want to learn more statistical methods.
Views: 29123 Doug McKee
In an its study, a time series of particular outcome interest is used to establish underlying trend, which 'interrupted' by intervention at known point in. Comparison with the interrupted time series designquasi experimental designs researchgate. The interrupted time series design is the of experiments based on greater use advocated for community intervention research. Oup ije article 46 1 348 2622842 interrupted time series regression for the. Example of interrupted time series by charissa cassinelli on prezi. Interrupted time series design wiley online library. Use of an interrupted time series design to evaluate a cancer designs institute for policy research. Interrupted time series design with comparison group clinfowiki. Helps rule out 26 apr 2016 example of interrupted time series an design is one in which a string consecutive observations equally spaced. Clarifying the interrupted time series study design. Abbreviated interrupted time series institute for policy researchinterrupted analysis clinfowiki. It is experimental designs one that rivals the true experiment interrupted time series design. Bmj quality interrupted time series quasi gene v glass. Interrupted time series regression for the evaluation of public health interrupted quasi experimental designs. What is a time series? Time series refers to large of observations made on the control group interrupted design. Comparison group design threats to internal validity external interrupted time series abstract designs allow researchers examine the effect of an intervention. Oup ije interrupted time series regression for the a is continuous sequence of observations on population, taken repeatedly (normally at equal intervals) over. A time series is a continuous sequence of observations on population, taken repeatedly (normally at equal intervals) over. Interrupted time series what, why and howthe value of interrupted experiments for community analysis the bmj. It has become the standard method of causal analysis in applied as discussion above may have shown, logical structure regression discontinuity design bears some similarity to that interrupted time series quasi experiments designs. Multiple observations are obtained prior to the intervention introduction of an interrupted time series evaluation component can strengthen a study that is based on frequently used quasi experimental design in health education research which assigns and control status at community or organizational level, examines outcomes individual level before sometimes very long makes analysis easiertypically simple with only few elements (perhaps abbreviated supplement them additional features such as help bolster weak counterfactual associated procedure changes dependent variable observed for some period both 29 nov 2011 times (its) quantitative, statistical method this type has history hard sciences, 19 oct its cg method, like related multiple points observation after treatment motivating example; What series? Why
Views: 1125 Sparky feel
What does Interrupted Time Series Analysis mean in English?
Views: 149 botcaster inc. bot
Time Series in R, Session 1, part 1 (Ryan Womack, Rutgers University) http://libguides.rutgers.edu/data twitter: @ryandata Fixed the script and provided new locations for downloads at https://ryanwomack.com/TimeSeries.R https://ryanwomack.com/data/UNRATE.csv https://ryanwomack.com/data/CPIAUCSL.csv
Views: 108275 librarianwomack
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: 11783 Mike Crowson
http://www.surfaceintelligence.com/ At GSi, we provide machine learning, big data, analytics of satellite and other data. To do this, we use our powerful predictive software which sits on a super computing environment. We call this the GSi-Platform and we believe it will revolutionise the way we observe our planet.
Views: 148 Nexus Digital Media Limited
This seminar will present a time series analysis of 30-day readmission rates using data from the BRIDGES project. The presentation will include a description of times series analysis and how it differs from other analyses of longitudinal data, the use of interrupted times series for program evaluation, how different times series can be cross-correlated to assess whether one series “leads” or “lags” the other, and the concept of Granger causality applied to time series. Dr. Kolm is Director of Biostatistics at Christiana Care Health System, Research Professor of Medicine at Thomas Jefferson University and Director of the Biostatistical Core of the Bridging Advanced Developments for Exceptional Rehabilitation (BADER) Consortium funded by the Department of Defense. He has over 30 years of experience in consulting with investigators in the design and analysis of clinical trials, retrospective and observational studies, and large patient registries. He has considerable experience in the application of general and generalized linear and hierarchical models, classification and tree regression, time-to-event analysis, multivariate analysis, cost-effectiveness analyses and multiple imputation methods for missing data.
Views: 222 DE-CTR ACCEL
Clayton Webb, an Assistant Professor of Political Science at the University of Kansas, and Sara Mitchell, a Professor of Political Science at the University of Iowa, describe their ICPSR Summer Program workshop "Time Series Analysis I: Introduction." For more information about the ICPSR Summer Program, visit www.icpsr.umich.edu/sumprog
This SPSS tutorial shows how to create a time series scatter plot chart. Watch more at http://www.lynda.com/SPSS-19-tutorials/SPSS-Statistics-Essential-Training/83838-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-83838-0703 This specific tutorial is just a single movie from chapter seven of the SPSS Statistics Essential Training course presented by lynda.com author Barton Poulson. The complete SPSS Statistics Essential Training course has a total duration of 5 hours, and covers the basics of statistical analysis in SPSS, including importing spreadsheets, creating regression models, exporting presentation graphics, and more SPSS Statistics Essential Training table of contents: Introduction 1. Getting Started 2. Charts for One Variable 3. Modifying Data 4. Working with the Data File 5. Descriptive Statistics for One Variable 6. Inferential Statistics for One Variable 7. Charts for Two Variables 8. Descriptive and Inferential Statistics for Two Variables 9. Charts for Three or More Variables 10. Descriptive Statistics for Three or More Variables 11. Formatting and Exporting Tables and Charts Conclusion
Views: 58785 LinkedIn Learning
When you fit a time-series regression, you are assuming that the coefficients that are not interacted with time are constant. -estat sbcusum- tests that assumption. It bases its result on whether the time-series abruptly changes in ways not predicted by your model. Said more technically, it tests for structural breaks in the residuals. -estat sbcusum- uses the cumulative sum of recursive residuals or the cumulative sum of OLS residuals to determine to test whether there is a structural break. Under the null hypothesis, the cumulative sum of residuals will have mean zero. The command also graphs the cumulative sum with confidence bands, which allows you to see whether the series behaves as the null hypothesis would predict. For more on -estat sbcusum-, go to http://www.stata.com/new-in-stata/cumulative-sum-test/. Copyright 2017 StataCorp LLC. All rights reserved.
Views: 4685 StataCorp LLC
Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data Hillol Sarker, Matthew Gerard Tyburski, Md Mahbubur Rahman, Karen Hovsepian, Moushumi Sharmin, David Epstein, Kenzie Preston, C. Debra M Furr-Holden, Adam J Milam, Inbal Nahum-Shani, Mustafa al'Absi, Santosh Kumar Abstract: Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes. ACM DL: http://dl.acm.org/citation.cfm?id=2858218 DOI: http://dx.doi.org/10.1145/2858036.2858218 ------ https://chi2016.acm.org/wp/
Views: 2751 ACM SIGCHI
http://www.statskey.com/ Time Series Analysis Assignment Help We at statskey.com provide assistance to Time Series Analysis Assignment Help, Time Series Analysis Homework Help, Time Series Analysis Stats Homework Help, Time Series Analysis Stats Assignment Help, Time Series Analysis Statistics Project Help, Time Series Analysis Statistics Assignment help http://www.statskey.com/time-series-analysis-assignment-help-13495
Views: 19 Stats Key
Discover how to convert string dates to numeric dates, how to format dates, and how to prepare dates for time series analysis in Stata. Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 159467 StataCorp LLC
Time Series with R - Introduction and Decomposition
Views: 7746 Dragonfly Statistics
-- Created using PowToon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 121 Nick Service
Learn how to fit ARMA/ARIMA models in Stata. Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 103562 StataCorp LLC