National Survey on Drug Use and Health: an Overview of Trend Testing Methods and Applications in NSDUH and Other Studies
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12.6 Sign Test (Web)
12.4 Sign test (Web) Click on 'Bookmarks' in the Left-Hand menu and then click on the required section. 12.6 Sign test (Web) 12.6.1 Introduction The Sign Test is a one sample test that compares the median of a data set with a specific target value. The Sign Test performs the same function as the One Sample Wilcoxon Test, but it does not rank data values. Without the information provided by ranking, the Sign Test is less powerful (9.4.5) than the Wilcoxon Test. However, the Sign Test is useful for problems involving 'binomial' data, where observed data values are either above or below a target value. 12.6.2 Sign Test The Sign Test aims to test whether the observed data sample has been drawn from a population that has a median value, m, that is significantly different from (or greater/less than) a specific value, mO. The hypotheses are the same as in 12.1.2. We will illustrate the use of the Sign Test in Example 12.14 by using the same data as in Example 12.2. Example 12.14 The generation times, t, (5.2.4) of ten cultures of the same micro-organisms were recorded. Time, t (hr) 6.3 4.8 7.2 5.0 6.3 4.2 8.9 4.4 5.6 9.3 The microbiologist wishes to test whether the generation time for this micro- organism is significantly greater than a specific value of 5.0 hours. See following text for calculations: In performing the Sign Test, each data value, ti, is compared with the target value, mO, using the following conditions: ti < mO replace the data value with '-' ti = mO exclude the data value ti > mO replace the data value with '+' giving the results in Table 12.14 Difference + - + ----- + - + - + + Table 12.14 Signs of Differences in Example 12.14 12.4.p1 12.4 Sign test (Web) We now calculate the values of r + = number of '+' values: r + = 6 in Example 12.14 n = total number of data values not excluded: n = 9 in Example 12.14 Decision making in this test is based on the probability of observing particular values of r + for a given value of n. -
Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box– Jenkins Modeling Procedure
International Journal of General Medicine Dovepress open access to scientific and medical research Open Access Full Text Article ORIGINAL RESEARCH Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box– Jenkins Modeling Procedure Yemane Asmelash Introduction: COVID-19, which causes severe acute respiratory syndrome, is spreading Gebretensae 1 rapidly across the world, and the severity of this pandemic is rising in Ethiopia. The main Daniel Asmelash 2 objective of the study was to analyze the trend and forecast the spread of COVID-19 and to develop an appropriate statistical forecast model. 1Department of Statistics, College of Natural and Computational Science, Methodology: Data on the daily spread between 13 March, 2020 and 31 August 2020 were Aksum University, Aksum, Ethiopia; collected for the development of the autoregressive integrated moving average (ARIMA) 2 Department of Clinical Chemistry, model. Stationarity testing, parameter testing and model diagnosis were performed. In School of Biomedical and Laboratory Sciences, College of Medicine and Health addition, candidate models were obtained using autocorrelation function (ACF) and partial Sciences, University of Gondar, Gondar, autocorrelation functions (PACF). Finally, the fitting, selection and prediction accuracy of the Ethiopia ARIMA models was evaluated using the RMSE and MAPE model selection criteria. Results: A total of 51,910 confirmed COVID-19 cases were reported from 13 March to 31 August 2020. The total recovered and death rates as of 31 August 2020 were 37.2% and 1.57%, respectively, with a high level of increase after the mid of August, 2020. In this study, ARIMA (0, 1, 5) and ARIMA (2, 1, 3) were finally confirmed as the optimal model for confirmed and recovered COVID-19 cases, respectively, based on lowest RMSE, MAPE and BIC values. -
A Unit-Error Theory for Register-Based Household Statistics
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Zhang, Li-Chun Working Paper A unit-error theory for register-based household statistics Discussion Papers, No. 598 Provided in Cooperation with: Research Department, Statistics Norway, Oslo Suggested Citation: Zhang, Li-Chun (2009) : A unit-error theory for register-based household statistics, Discussion Papers, No. 598, Statistics Norway, Research Department, Oslo This Version is available at: http://hdl.handle.net/10419/192580 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu Discussion Papers No. 598, December 2009 Statistics Norway, Statistical Methods and Standards Li-Chun Zhang A unit-error theory for register- based household statistics Abstract: The next round of census will be completely register-based in all the Nordic countries. -
Learn the Six Methods for Determining Moisture
SCIENCE OF SENSING Learn the Six Methods For Determining Moisture ADVANTAGES, DEFICIENCIES AND WHO USES EACH METHOD CONTENTS 1. Introduction 3 2. Primary versus Secondary Test Methods 4 3. Primary Methods 5 a. Karl Fisher 6 b. Loss On Drying 8 4. Secondary Methods 10 a. Electrical Methods 11 b. Microwave 13 c. Nuclear 15 d. Near-Infrared 17 5. Conclusion 19 6. About the Author 20 7. About Kett 20 8. Next Steps 21 Learn the Six Methods For Determining Moisture | www.kett.com 2 1. INTRODUCTION With three-quarters of the earth covered with water, almost everything we touch or eat has some water content. The accurate measurement of water content is very important, as it affects all aspects of a business and it’s supply chain. As discussed in our previous ebook “A Guide To Accurate and Reliable Moisture Measurement”, the proper (or improper) utilization of moisture meters to optimize water can be the difference between profitability and failure for a business. This ebook, the 6 Methods For Determining Moisture is for anyone interested in pursuing the goal of accurate moisture measurement, including: quality control, quality assurance, production management, design/build engineers, executive management and of course anyone wanting to learn more about this far reaching topic. The purpose of this ebook is to provide insight into the various major moisture measurement technologies and to help you identify which is the right type of instrument for your needs. Expect to learn the six major methods of moisture measurement, the positive aspects of each, as well as the drawbacks. -
Public Health & Intelligence
Public Health & Intelligence PHI Trend Analysis Guidance Document Control Version Version 1.5 Date Issued March 2017 Author Róisín Farrell, David Walker / HI Team Comments to [email protected] Version Date Comment Author Version 1.0 July 2016 1st version of paper David Walker, Róisín Farrell Version 1.1 August Amending format; adding Róisín Farrell 2016 sections Version 1.2 November Adding content to sections Róisín Farrell, Lee 2016 1 and 2 Barnsdale Version 1.3 November Amending as per Róisín Farrell 2016 suggestions from SAG Version 1.4 December Structural changes Róisín Farrell, Lee 2016 Barnsdale Version 1.5 March 2017 Amending as per Róisín Farrell, Lee suggestions from SAG Barnsdale Contents 1. Introduction ........................................................................................................................................... 1 2. Understanding trend data for descriptive analysis ................................................................................. 1 3. Descriptive analysis of time trends ......................................................................................................... 2 3.1 Smoothing ................................................................................................................................................ 2 3.1.1 Simple smoothing ............................................................................................................................ 2 3.1.2 Median smoothing .......................................................................................................................... -
Variation Factors in the Design and Analysis of Replicated Controlled Experiments - Three (Dis)Similar Studies on Inspections Versus Unit Testing
Variation Factors in the Design and Analysis of Replicated Controlled Experiments - Three (Dis)similar Studies on Inspections versus Unit Testing Runeson, Per; Stefik, Andreas; Andrews, Anneliese Published in: Empirical Software Engineering DOI: 10.1007/s10664-013-9262-z 2014 Link to publication Citation for published version (APA): Runeson, P., Stefik, A., & Andrews, A. (2014). Variation Factors in the Design and Analysis of Replicated Controlled Experiments - Three (Dis)similar Studies on Inspections versus Unit Testing. Empirical Software Engineering, 19(6), 1781-1808. https://doi.org/10.1007/s10664-013-9262-z Total number of authors: 3 General rights Unless other specific re-use rights are stated the following general rights apply: Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Read more about Creative commons licenses: https://creativecommons.org/licenses/ Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. LUND UNIVERSITY PO Box 117 221 00 Lund +46 46-222 00 00 Variation Factors in the Design and Analysis of Replicated Controlled Experiments { Three (Dis)similar Studies on Inspections versus Unit Testing Per Runeson, Andreas Stefik and Anneliese Andrews Lund University, Sweden, [email protected] University of Nevada, NV, USA, stefi[email protected] University of Denver, CO, USA, [email protected] Empirical Software Engineering, DOI: 10.1007/s10664-013-9262-z Self archiving version. -
Methods, Method Verification and Validation Volume 2
OOD AND RUG DMINISTRATION Revision #: 02 F D A Document Number: OFFICE OF REGULATORY AFFAIRS Revision Date: ORA-LAB.5.4.5 ORA Laboratory Manual Volume II 06/30/2020 Title: Page 1 of 32 Methods, Method Verification and Validation Sections in This Document 1. Purpose ....................................................................................................................................2 2. Scope .......................................................................................................................................2 3. Responsibility............................................................................................................................2 4. Background...............................................................................................................................3 5. References ...............................................................................................................................4 6. Procedure .................................................................................................................................5 6.1. Method Selection ...........................................................................................................5 6.2. Method Evaluation & Record .........................................................................................5 6.3. Standard Method Verification .........................................................................................5 6.3.1. Chemistry........................................................................................................ -
1 Sample Sign Test 1
Non-Parametric Univariate Tests: 1 Sample Sign Test 1 1 SAMPLE SIGN TEST A non-parametric equivalent of the 1 SAMPLE T-TEST. ASSUMPTIONS: Data is non-normally distributed, even after log transforming. This test makes no assumption about the shape of the population distribution, therefore this test can handle a data set that is non-symmetric, that is skewed either to the left or the right. THE POINT: The SIGN TEST simply computes a significance test of a hypothesized median value for a single data set. Like the 1 SAMPLE T-TEST you can choose whether you want to use a one-tailed or two-tailed distribution based on your hypothesis; basically, do you want to test whether the median value of the data set is equal to some hypothesized value (H0: η = ηo), or do you want to test whether it is greater (or lesser) than that value (H0: η > or < ηo). Likewise, you can choose to set confidence intervals around η at some α level and see whether ηo falls within the range. This is equivalent to the significance test, just as in any T-TEST. In English, the kind of question you would be interested in asking is whether some observation is likely to belong to some data set you already have defined. That is to say, whether the observation of interest is a typical value of that data set. Formally, you are either testing to see whether the observation is a good estimate of the median, or whether it falls within confidence levels set around that median. -
Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models
mathematics Article Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models Oscar Trull 1,* , Juan Carlos García-Díaz 1 and Alicia Troncoso 2 1 Department of Applied Statistics and Operational Research and Quality, Universitat Politècnica de València, 46022 Valencia, Spain; [email protected] 2 Department of Computer Science, Pablo de Olavide University, 41013 Sevilla, Spain; [email protected] * Correspondence: [email protected] Received: 8 January 2020; Accepted: 13 February 2020; Published: 18 February 2020 Abstract: The Holt–Winters models are one of the most popular forecasting algorithms. As well-known, these models are recursive and thus, an initialization value is needed to feed the model, being that a proper initialization of the Holt–Winters models is crucial for obtaining a good accuracy of the predictions. Moreover, the introduction of multiple seasonal Holt–Winters models requires a new development of methods for seed initialization and obtaining initial values. This work proposes new initialization methods based on the adaptation of the traditional methods developed for a single seasonality in order to include multiple seasonalities. Thus, new methods to initialize the level, trend, and seasonality in multiple seasonal Holt–Winters models are presented. These new methods are tested with an application for electricity demand in Spain and analyzed for their impact on the accuracy of forecasts. As a consequence of the analysis carried out, which initialization method to use for the level, trend, and seasonality in multiple seasonal Holt–Winters models with an additive and multiplicative trend is provided. Keywords: forecasting; multiple seasonal periods; Holt–Winters, initialization 1. Introduction Exponential smoothing methods are widely used worldwide in time series forecasting, especially in financial and energy forecasting [1,2]. -
Signed-Rank Tests for ARMA Models
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector JOURNAL OF MULTIVARIATE ANALYSIS 39, l-29 ( 1991) Time Series Analysis via Rank Order Theory: Signed-Rank Tests for ARMA Models MARC HALLIN* Universith Libre de Bruxelles, Brussels, Belgium AND MADAN L. PURI+ Indiana University Communicated by C. R. Rao An asymptotic distribution theory is developed for a general class of signed-rank serial statistics, and is then used to derive asymptotically locally optimal tests (in the maximin sense) for testing an ARMA model against other ARMA models. Special cases yield Fisher-Yates, van der Waerden, and Wilcoxon type tests. The asymptotic relative efficiencies of the proposed procedures with respect to each other, and with respect to their normal theory counterparts, are provided. Cl 1991 Academic Press. Inc. 1. INTRODUCTION 1 .l. Invariance, Ranks and Signed-Ranks Invariance arguments constitute the theoretical cornerstone of rank- based methods: whenever weaker unbiasedness or similarity properties can be considered satisfying or adequate, permutation tests, which generally follow from the usual Neyman structure arguments, are theoretically preferable to rank tests, since they are less restrictive and thus allow for more powerful results (though, of course, their practical implementation may be more problematic). Which type of ranks (signed or unsigned) should be adopted-if Received May 3, 1989; revised October 23, 1990. AMS 1980 subject cIassifications: 62Gl0, 62MlO. Key words and phrases: signed ranks, serial rank statistics, ARMA models, asymptotic relative effhziency, locally asymptotically optimal tests. *Research supported by the the Fonds National de la Recherche Scientifique and the Minist&e de la Communaute Franpaise de Belgique. -
ETA Visible Emissions Observer Training Manual
Eastern Technical Associates Visible Emissions Observer Training Manual June 2013 Copyright © 1997-2013 by ETA i Eastern Technical Associates © Welcome Visible Emissions Observer Trainee: During the next few days you will be trained in one of the oldest and most common source measurement techniques – visible emissions observations. There are more people measuring visible emissions today than at any time in the 100-plus-year history of emissions observations. We have prepared this manual to assist you in your training, certification, and most important, field observations. It is impossible to give proper credit to all who developed and supported the technology that contributed to this manual. The list would probably be longer than the manual itself. We have been working in this field since 1970 and stand in the footprints of many innovators. Much of the material in this manual comes from the contributions of the many visible emissions instructors we have been privileged to work with as well as U.S. EPA documents and the contributions of our staff for more than 30 years. The purpose of this manual is to give you a hands-on, readable reference for the topics addressed in the classroom portion of our training. It should also be useful as a reference document in the future when you are a certified observer performing measurements in the field. Unfortunately, we cannot cover all of the possible situations you might encounter, but the guidance in this document combined with the training you receive in ETA’s classroom and field programs should enable you to make valid observations for more than 90% of the sources you observe. -
6 Single Sample Methods for a Location Parameter
6 Single Sample Methods for a Location Parameter If there are serious departures from parametric test assumptions (e.g., normality or symmetry), nonparametric tests on a measure of central tendency (usually the median) are used. Recall: M is a median of a random variable X if P (X M) = P (X M) = :5. • ≤ ≥ The distribution of X is symmetric about c if P (X c x) = P (X c + x) for all x. • ≤ − ≥ For symmetric continuous distributions, the median M = the mean µ. Thus, all conclusions • about the median can also be applied to the mean. If X be a binomial random variable with parameters n and p (denoted X B(n; p)) then • n ∼ P (X = x) = px(1 p)n−x for x = 0; 1; : : : ; n x − n n! where = and k! = k(k 1)(k 2) 2 1: • x x!(n x)! − − ··· · − Tables exist for the cdf P (X x) for various choices of n and p. The probabilities and • cdf values are also easy to produce≤ using SAS or R. Thus, if X B(n; :5), we have • ∼ n P (X = x) = (:5)n x x n P (X x) = (:5)n ≤ k k X=0 P (X x) = P (X n x) because the B(n; :5) distribution is symmetric. ≤ ≥ − For sample sizes n > 20 and p = :5, a normal approximation (with continuity correction) • to the binomial probabilities is often used instead of binomial tables. (x :5) :5n { Calculate z = ± − : Use x+:5 when x < :5n and use x :5 when x > :5n. :5pn − { The value of z is compared to N(0; 1), the standard normal distribution.