Introduction to ANOVA Introduction to ANOVA (Jump to: Lecture | Video ) An ANOVA has factors(variables), and each of those factors has levels: There are several different types of ANOVA: There are four main assumptions of an ANOVA: Hypotheses in ANOVA depend on the number of factors you're dealing with: Effects dealing with one factor are called main effects. Here's an example of an interaction effect in an ANOVA: Below we have a Factorial ANOVA with two factors: dosage(0mg and 100mg) and gender(men and women) . Dosage and gender are interacting because the effect of one variable depends on which level you're at of the other variable. If we reject the null hypothesis in an ANOVA, all we know is that there is a difference somewhere among the groups. When performing an ANOVA, we calculate an "F" statistic. If there are no treatment differences (that is, if there is no actual effect), we expect F to be 1. Introduction to ANOVA (Jump to: Lecture | Video ) There are several different types of ANOVA:
Time series Time series: random data plus trend, with best-fit line and different applied filters A time series is a sequence of data points, measured typically at successive points in time spaced at uniform time intervals. Examples of time series are the daily closing value of the Dow Jones Industrial Average and the annual flow volume of the Nile River at Aswan. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, and communications engineering . Time series data have a natural temporal ordering. Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.[2]). Methods for time series analyses[edit] Analysis[edit] Motivation[edit] Classification[edit]
Online Statistics Education: A Free Resource for Introductory Statistics Developed by Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University OnlineStatBook Project Home This work is in the public domain. If you are an instructor using these materials, I can send you an instructor's manual, PowerPoint Slides, and additional questions that may be helpful to you. Table of Contents Mobile This version uses formatting that works better for mobile devices. Rice Virtual Lab in Statistics This is the original classic with all the simulations and case studies. Version in PDF e-Pub (e-book) Partial support for this work was provided by the National Science Foundation's Division of Undergraduate Education through grants DUE-9751307, DUE-0089435, and DUE-0919818.
Introduction to the Scientific Method Introduction to the Scientific Method The scientific method is the process by which scientists, collectively and over time, endeavor to construct an accurate (that is, reliable, consistent and non-arbitrary) representation of the world. Recognizing that personal and cultural beliefs influence both our perceptions and our interpretations of natural phenomena, we aim through the use of standard procedures and criteria to minimize those influences when developing a theory. As a famous scientist once said, "Smart people (like smart lawyers) can come up with very good explanations for mistaken points of view." In summary, the scientific method attempts to minimize the influence of bias or prejudice in the experimenter when testing an hypothesis or a theory. I. 1. 2. 3. 4. If the experiments bear out the hypothesis it may come to be regarded as a theory or law of nature (more on the concepts of hypothesis, model, theory and law below). II. Error in experiments have several sources. III. IV. V.
determine sample size two-way ANOVA? Computing required sample size for experiments to be analyzed by ANOVA is pretty complicated, with lots of possiblilities. To learn more, consult books by Cohen or Bausell and Li, but plan to spend at least several hours. Two-way ANOVA, as you'd expect, is more complicated than one-way. The complexity comes from the many possible ways to phrase your question about sample size. The rest of this article strips away most of these choices, and helps you determine sample size in one common situation, where you can make the following assumptions: There are two levels of the first factor, say the factor is Drug and you either gave the drug or gave vehicle (placebo). If those limitations aren't a problem for you, then read on for a simple way to compute necessary sample size. Sample size is always determined to detect some hypothetical difference. What about units? Another way to look at this is to express the difference you expect to see as a fraction of the mean.
Information by Eurostat and National Bank of Belgium Download Demetra+ In case of any problems or questions, please contact Eurostat Unit B2 Methodology and Research at : estat-methodology@ec.europa.eu To download documentation about Demetra+ from CROS portal please click here Reference page to ESS guidelines on Seasonal adjustment here For info about the next ESTP course on DEMETRA+ for beginners and advanced users that will be held at Eurostat please consult here Description Seasonal adjustment is an important step of the official statistics business architecture and harmonisation of practices has proved to be key element of quality of the output. In 2008, ESS (European Statistical System) guidelines on SA have been endorsed by the CMFB and the SPC as a framework for seasonal adjustment of PEEIs and other ESS and ESCB economic indicators. Demetra+ is a family of modules on seasonal adjustment, which are based on the two leading algorithms in that domain (TRAMO&SEATS@ / X-12-ARIMA). Features Future plans
“The Daily Rind”, a Better Way to Plan the Day — A. King in Society Photo: A sample “daily rind” from my notebook For years my task and schedule management lived across various apps — OmniFocus, Basecamp, Google Calendar, and others (and more recently, as I pared down my “productivity” tools, a simple combination of The Hit List + iCal.) But mapping out what to do throughout my day in a reliable way has always been a problem. Really understanding how little time there was and seeing patterns in time usage proved next to impossible, despite all the technology at my fingertips. I think I’ve found a better way. I still track my projects and tasks digitally, and keep a calendar (with online sync + backup) for planning ahead, but for mapping out what I’m going to do in the day ahead of me, I’ve devised a decidedly low-tech system which I’m lovingly referring to as “The Daily Rind.” Re-introducing Analog -or- Can’t Get No Satisfaction? I’d never really been attracted to using a paper-based day-planner. Hacking the Muji Chronotebook 1. 2. 3. The First Rind — Day
The Statistics Homepage "Thank you and thank you again for providing a complete, well-structured, and easy-to-understand online resource. Every other website or snobbish research paper has not deigned to explain things in words consisting of less than four syllables. I was tossed to and fro like a man holding on to a frail plank that he calls his determination until I came across your electronic textbook...You have cleared the air for me. You have enlightened. You have illuminated. You have educated me." — Mr. "As a professional medical statistician of some 40 years standing, I can unreservedly recommend this textbook as a resource for self-education, teaching and on-the-fly illustration of specific statistical methodology in one-to-one statistical consulting. — Mr. "Excellent book. — Dr. "Just wanted to congratulate whoever wrote the 'Experimental Design' page. — James A. Read More Testimonials >> StatSoft has freely provided the Electronic Statistics Textbook as a public service since 1995. Proper citation:
Degrees of Freedom Tutorial | Ron Dotsch A lot of researchers seem to be struggling with their understanding of the statistical concept of degrees of freedom. Most do not really care about why degrees of freedom are important to statistical tests, but just want to know how to calculate and report them. This page will help. For those interested in learning more about degrees of freedom, take a look at the following resources: I couldn’t find any resource on the web that explains calculating degrees of freedom in a simple and clear manner and believe this page will fill that void. Let’s start with a simple explanation of degrees of freedom. Imagine a set of three numbers, pick any number you want. Now, imagine a set of three numbers, whose mean is 3. This generalizes to a set of any given length. This is the basic method to calculate degrees of freedom, just n – 1. Df1 Df1 is all about means and not about single observations. Let’s start off with a one-way ANOVA. Sticking to the one-way ANOVA, but moving on to three groups.
How do I fit an ARIMA model to a time series with XLSTAT-Time? - Tutorials - XLSTAT Dataset to fit an ARIMA model to a time series An Excel sheet with both the data and results can be downloaded by clicking here. The data have been obtained in [Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco], and correspond to monthly international airline passengers (in thousands) from January 1949 to December 1960. We notice on the chart, that there is a global upward trend, that every year a similar cycle starts, and that the variability within a year seems to increase over time. We can now fit an ARIMA(0,1, 1)(0,1,1)12 model which seems to be appropriate to remove the trend effect and the yearly seasonality of the data. Setting up the fitting of an ARIMA model to a time series After opening XLSTAT, select the XLSTAT / XLSTAT-Time / ARIMA command, or click on the corresponding button of the "XLSTAT-Time" toolbar (see below). Once you've clicked on the button, the ARIMA dialog box will appear. The ARIMA model writes:
Preparing for a Post Peak Life | Post Peak Living Version 3 released February 15, 2011 with new discussion on the global debt problem. No time to watch now? Watch the first ten minutes, which are a complete synopsis, then come back for the rest. Click on the lower right to expand the video to full screen. The previous version of this presentation is available in English here and in Slovak here. After Watching the Video If you're interested in learning more or are ready to being preparing, you can: Enroll in the Start Now Mini Course (look at the sidebar to the right). Notes Right-click here and choose "Save Target As..." Slide: What the Next Decade Will Bring Does peak oil mean we don't have to worry about climate change? Two meter sea level rise unstoppable (Reuters) Heat waves and extremely high temperatures could be commonplace in the U.S. by 2039, Stanford study finds (Stanford.edu) Slide: Peak Oil: Supply Falls Short of Demand Slide: U.S. The United States currently uses 25% of the world oil production but has only 2% of world reserves.
Margin of Error and Confidence Levels Made Simple Pamela Hunter February 26, 2010 A survey is a valuable assessment tool in which a sample is selected and information from the sample can then be generalized to a larger population. Surveying has been likened to taste-testing soup – a few spoonfuls tell what the whole pot tastes like. The key to the validity of any survey is randomness. Just as the soup must be stirred in order for the few spoonfuls to represent the whole pot, when sampling a population, the group must be stirred before respondents are selected. It is critical that respondents be chosen randomly so that the survey results can be generalized to the whole population. How well the sample represents the population is gauged by two important statistics – the survey’s margin of error and confidence level. In other words, Company X surveys customers and finds that 50 percent of the respondents say its customer service is “very good.” Sample Size and the Margin of Error Calculating Margin of Error for Individual Questions
Two-way anova - Handbook of Biological Statistics Summary Use two-way anova when you have one measurement variable and two nominal variables, and each value of one nominal variable is found in combination with each value of the other nominal variable. It tests three null hypotheses: that the means of the measurement variable are equal for different values of the first nominal variable; that the means are equal for different values of the second nominal variable; and that there is no interaction (the effects of one nominal variable don't depend on the value of the other nominal variable). When to use it You use a two-way anova (also known as a factorial anova, with two factors) when you have one measurement variable and two nominal variables. For example, here's some data I collected on the enzyme activity of mannose-6-phosphate isomerase (MPI) and MPI genotypes in the amphipod crustacean Platorchestia platensis. A two-way anova is usually done with replication (more than one observation for each combination of the nominal variables).