Statistical Procedures Available in Statcrunch

Total Page：16

File Type：pdf, Size：1020Kb

Statistical Procedures Available in StatCrunch Data Menu o Outcome Z statistics Load data – o One sample o From file . With data o From paste . With summary o Sample data o Two sample Save data . With data Export Data . With summary Row selection – Proportions o Interactive tools o One sample o Select where . With data o Deselect all . With summary Simulate data – o Two sample o Bernoulli . With data o Beta . With summary o Binomial T statistics o Cauchy o One sample o Chi-Square . With data o Discrete Uniform . With summary o Exponential o Two sample o F . With data o Gamma . With summary o Normal o Paired o Poisson Variance o T o One sample o Uniform . With data o Weibull . With summary Transform data o Two sample Compute expression . With data Sequence data . With summary Split column Regression Stack columns o Simple linear Bin Column o Polynomial Sort columns o Multiple Linear Rank columns o Logistic Sample columns . With data Indicator columns . With summary ANOVA o One way Stat Menu o Two way Nonparametrics Summary Stats o Sign test Wilcoxon Signed Ranks o Row o Mann-Whitney o Columns o Kruskal-Wallis o Correlation o Goodness-of-fit o Covariance Tables o Chi-square test Control Charts o Frequency X-bar o Contingency o . With data o R . With summary o X-bar,R o np Page 1 of 2 Statistical Procedures Available in StatCrunch o p Applets (Under StatCrunch o c Menu) o u Calculators Bayes rule o Beta Confidence intervals Binomial o o For a mean Cauchy o o For a population o Chi-square Contingency table o Exponential Correlation by eye o F Distribution demos o Gamma Histogram with sliders o Hypogeometric Hypothesis tests Normal o o For a mean Poisson o o For a population o T Mean/Std. Dev. Vs Median/QR o Weibull Random numbers o Custom Regression Resample o By eye Statistic o o Influence o simulation Graphics Menu Resampling o Bootstrap a statistic Bar plot o Randomization test for o With data correlation o With summary o Randomization test for 2 Pie chart means o With data o Randomization test for 2 o With summary proportions Chart Sampling distributions o Columns Simulation o Group stats o Birthday problem Histogram o Coin flipping Stem and leaf o Dice rolling Boxplot o Poker hands Dotplot o Raffle winnings Means plot o Urn sampling Scatter plot Multiplot QQ Plot Index plot Parallel coordinates Pairs plot 3D rotating plot Stars plot Word Wall Maps o US States o US locations o Fetch locations o Google Color Schemes Page 2 of 2 .

Copy Link

Recommended publications

Wu Washington 0250E 15755.Pdf (3.086Mb)
© Copyright 2016 Sang Wu Contributions to Physics-Based Aeroservoelastic Uncertainty Analysis Sang Wu A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2016 Reading Committee: Eli Livne, Chair Mehran Mesbahi Dorothy Reed Frode Engelsen Program Authorized to Offer Degree: Aeronautics and Astronautics University of Washington Abstract Contributions to Physics-Based Aeroservoelastic Uncertainty Analysis Sang Wu Chair of the Supervisory Committee: Professor Eli Livne The William E. Boeing Department of Aeronautics and Astronautics The thesis presents the development of a new fully-integrated, MATLAB based simulation capability for aeroservoelastic (ASE) uncertainty analysis that accounts for uncertainties in all disciplines as well as discipline interactions. This new capability allows probabilistic studies of complex configuration at a scope and with depth not known before. Several statistical tools and methods have been integrated into the capability to guide the tasks such as parameter prioritization, uncertainty reduction, and risk mitigation. The first task of the thesis focuses on aeroservoelastic uncertainty assessment considering control component uncertainty. The simulation has shown that attention has to be paid, if notch filters are used in the aeroservoelastic loop, to the variability and uncertainties of the aircraft involved. The second task introduces two innovative methodologies to characterize the unsteady aerodynamic uncertainty. One is a physically based aerodynamic influence coefficients element by element correction uncertainty scheme and the other is an alternative approach focusing on rational function approximation matrix uncertainties to evaluate the relative impact of uncertainty in aerodynamic stiffness, damping, inertia, or lag terms. Finally, the capability has been applied to obtain the gust load response statistics accounting for uncertainties in both aircraft and gust profiles.
[Show full text]
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.
[Show full text]
Analysing Census at School Data
Analysing Census At School Data www.censusatschool.ie Project Maths Development Team 2015 Page 1 of 7 Excel for Analysing Census At School Data Having retrieved a class’s data using CensusAtSchool: 1. Save the downloaded class data which is a .csv file as an .xls file. 2. Delete any unwanted variables such as the teacher name, date stamp etc. 3. Freeze the top row so that one can scroll down to view all of the data while the top row remains in view. 4. Select certain columns (not necessarily contiguous) for use as a class worksheet. Aim to have a selection of categorical data (nominal and ordered) and numerical data (discrete and continuous) unless one requires one type exclusively. The number of columns will depend on the class as too much data might be intimidating for less experienced classes. Students will need to have the questionnaire to hand to identify the question which produced the data. 5. Make a new worksheet within the workbook of downloaded data using the selected columns. 6. Name the worksheet i.e. “data for year 2 2014” or “data for margin of error investigation”. 7. Format the data to show all borders (useful if one is going to make a selection of the data and put it into Word). 8. Order selected columns of this data perhaps (and use expand selection) so that rogue data such as 0 for height etc. can be eliminated. It is perhaps useful for students to see this. (Use Sort & Filter.) 9. Use a filter on data, for example based on gender, to generate further worksheets for future use 10.
[Show full text]
A Monte Carlo Simulation Approach to the Reliability Modeling of the Beam Permit System of Relativistic Heavy Ion Collider (Rhic) at Bnl* P
Proceedings of ICALEPCS2013, San Francisco, CA, USA MOPPC075 A MONTE CARLO SIMULATION APPROACH TO THE RELIABILITY MODELING OF THE BEAM PERMIT SYSTEM OF RELATIVISTIC HEAVY ION COLLIDER (RHIC) AT BNL* P. Chitnis#, T.G. Robertazzi, Stony Brook University, Stony Brook, NY 11790, U.S.A. K.A. Brown, Brookhaven National Laboratory, Upton, NY 11973, U.S.A. Abstract called the Permit Carrier Link, Blue Carrier Link and The RHIC Beam Permit System (BPS) monitors the Yellow Carrier link. These links carry 10 MHz signals health of RHIC subsystems and takes active decisions whose presence allows the beam in the ring. Support regarding beam-abort and magnet power dump, upon a systems report their status to BPS through “Input subsystem fault. The reliability of BPS directly impacts triggers” called Permit Inputs (PI) and Quench Inputs the RHIC downtime, and hence its availability. This work (QI). If any support system PI fails, the permit carrier assesses the probability of BPS failures that could lead to terminates, initiating a beam dump. If QI fails, then the substantial downtime. A fail-safe condition imparts blue and yellow carriers also terminate, initiating magnet downtime to restart the machine, while a failure to power dump in blue and yellow ring magnets. The carrier respond to an actual fault can cause potential machine failure propagates around the ring to inform other PMs damage and impose significant downtime. This paper about the occurrence of a fault. illustrates a modular multistate reliability model of the Other than PMs, BPS also has 4 Abort Kicker Modules BPS, with modules having exponential lifetime (AKM) that see the permit carrier failure and send the distributions.
[Show full text]
2 3 Practice Problems.Pdf
Exam Name___________________________________ MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) The following frequency distribution presents the frequency of passenger vehicles that 1) pass through a certain intersection from 8:00 AM to 9:00 AM on a particular day. Vehicle Type Frequency Motorcycle 5 Sedan 95 SUV 65 Truck 30 Construct a frequency bar graph for the data. A) B) 1 C) D) 2) The following bar graph presents the average amount a certain family spent, in dollars, on 2) various food categories in a recent year. On which food category was the most money spent? A) Dairy products B) Cereals and baked goods C) Fruits and vegetables D) Meat poultry, fish, eggs 3) The following frequency distribution presents the frequency of passenger vehicles that 3) pass through a certain intersection from 8:00 AM to 9:00 AM on a particular day. Vehicle Type Frequency Motorcycle 9 Sedan 54 SUV 27 2 Truck 53 Construct a relative frequency bar graph for the data. A) B) C) D) 3 4) The following frequency distribution presents the frequency of passenger vehicles that 4) pass through a certain intersection from 8:00 AM to 9:00 AM on a particular day. Vehicle Type Frequency Motorcycle 9 Sedan 20 SUV 25 Truck 39 Construct a pie chart for the data. A) B) C) D) 5) The following pie chart presents the percentages of fish caught in each of four ratings 5) categories. Match this pie chart with its corresponding Parato chart. 4 A) B) C) 5 D) SHORT ANSWER.
[Show full text]
Type Here Title of the Paper
ICME 11, TSG 13, Monterrey, Mexico, 2008: Dave Pratt Shaping the Experience of Young and Naïve Probabilists Pratt, Dave Institute of Education, University of London. [email protected] Summary This paper starts by briefly summarizing research which has focused on student misconceptions with respect to understanding probability, before outlining the author’s research, which, in contrast, presents what students of age 11-12 years(Grade 7) do know and can construct, given access to a carefully designed environment. These students judged randomness according to unpredictability, lack of pattern in results, lack of control over outcomes and fairness. In many respects their judgments were based on similar characteristics to those of experts. However, it was only through interaction with a virtual environment, ChanceMaker, that the students began to express situated meanings for aggregated long-term randomness. That data is then re-analyzed in order to reflect upon the design decisions that shaped the environment itself. Four main design heuristics are identified and elaborated: Testing personal conjectures, Building on student knowledge, Linking purpose and utility, Fusing control and representation. In conclusion, it is conjectured that these heuristics are of wider relevance to teachers and lecturers, who aspire to shape the experience of young and naïve probabilists through their actions as designers of tasks and pedagogical settings. Preamble The aim in this paper is to propose heuristics that might usefully guide design decisions for pedagogues, who might be building software, creating new curriculum approaches, imagining a novel task or writing a lesson plan. (Here I use the term “heuristics” with its standard meaning as a rule-of-thumb, a guiding rule, rather than in the specialized sense that has been adopted by researchers of judgments of chance.) With this aim in mind, I re-analyze data that have previously informed my research on students’ meanings for randomness as emergent during activity.
[Show full text]
Revisiting the Sustainable Happiness Model and Pie Chart: Can Happiness Be Successfully Pursued?
The Journal of Positive Psychology Dedicated to furthering research and promoting good practice ISSN: 1743-9760 (Print) 1743-9779 (Online) Journal homepage: https://www.tandfonline.com/loi/rpos20 Revisiting the Sustainable Happiness Model and Pie Chart: Can Happiness Be Successfully Pursued? Kennon M. Sheldon & Sonja Lyubomirsky To cite this article: Kennon M. Sheldon & Sonja Lyubomirsky (2019): Revisiting the Sustainable Happiness Model and Pie Chart: Can Happiness Be Successfully Pursued?, The Journal of Positive Psychology, DOI: 10.1080/17439760.2019.1689421 To link to this article: https://doi.org/10.1080/17439760.2019.1689421 Published online: 07 Nov 2019. Submit your article to this journal View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=rpos20 THE JOURNAL OF POSITIVE PSYCHOLOGY https://doi.org/10.1080/17439760.2019.1689421 Revisiting the Sustainable Happiness Model and Pie Chart: Can Happiness Be Successfully Pursued? Kennon M. Sheldona,b and Sonja Lyubomirskyc aUniversity of Missouri, Columbia, MO, USA; bNational Research University Higher School of Economics, Moscow, Russian Federation; cUniversity of California, Riverside, CA, USA ABSTRACT ARTICLE HISTORY The Sustainable Happiness Model (SHM) has been inﬂuential in positive psychology and well-being Received 4 September 2019 science. However, the ‘pie chart’ aspect of the model has received valid critiques. In this article, we Accepted 8 October 2019 start by agreeing with many such critiques, while also explaining the context of the original article KEYWORDS and noting that we were speculative but not dogmatic therein. We also show that subsequent Subjective well-being; research has supported the most important premise of the SHM – namely, that individuals can sustainable happiness boost their well-being via their intentional behaviors, and maintain that boost in the longer-term.
[Show full text]
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.
[Show full text]
Doing More with SAS/ASSIST® 9.1 the Correct Bibliographic Citation for This Manual Is As Follows: SAS Institute Inc
Doing More With SAS/ASSIST® 9.1 The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2004. Doing More With SAS/ASSIST ® 9.1. Cary, NC: SAS Institute Inc. Doing More With SAS/ASSIST® 9.1 Copyright © 2004, SAS Institute Inc., Cary, NC, USA ISBN 1-59047-206-3 All rights reserved. Produced in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. U.S. Government Restricted Rights Notice. Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227–19 Commercial Computer Software-Restricted Rights (June 1987). SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513. 1st printing, January 2004 SAS Publishing provides a complete selection of books and electronic products to help customers use SAS software to its fullest potential. For more information about our e-books, e-learning products, CDs, and hard-copy books, visit the SAS Publishing Web site at support.sas.com/publishing or call 1-800-727-3228. SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are registered trademarks or trademarks of
[Show full text]
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.
[Show full text]
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.
[Show full text]
Tests of Hypotheses Using Statistics
Tests of Hypotheses Using Statistics Adam Massey¤and Steven J. Millery Mathematics Department Brown University Providence, RI 02912 Abstract We present the various methods of hypothesis testing that one typically encounters in a mathematical statistics course. The focus will be on conditions for using each test, the hypothesis tested by each test, and the appropriate (and inappropriate) ways of using each test. We conclude by summarizing the di®erent tests (what conditions must be met to use them, what the test statistic is, and what the critical region is). Contents 1 Types of Hypotheses and Test Statistics 2 1.1 Introduction . 2 1.2 Types of Hypotheses . 3 1.3 Types of Statistics . 3 2 z-Tests and t-Tests 5 2.1 Testing Means I: Large Sample Size or Known Variance . 5 2.2 Testing Means II: Small Sample Size and Unknown Variance . 9 3 Testing the Variance 12 4 Testing Proportions 13 4.1 Testing Proportions I: One Proportion . 13 4.2 Testing Proportions II: K Proportions . 15 4.3 Testing r £ c Contingency Tables . 17 4.4 Incomplete r £ c Contingency Tables Tables . 18 5 Normal Regression Analysis 19 6 Non-parametric Tests 21 6.1 Tests of Signs . 21 6.2 Tests of Ranked Signs . 22 6.3 Tests Based on Runs . 23 ¤E-mail: [email protected] yE-mail: [email protected] 1 7 Summary 26 7.1 z-tests . 26 7.2 t-tests . 27 7.3 Tests comparing means . 27 7.4 Variance Test . 28 7.5 Proportions . 28 7.6 Contingency Tables .
[Show full text]