background preloader

Interactive Statistical Calculation Pages

Interactive Statistical Calculation Pages

Determinación del tamaño muestral Todo estudio epidemiológico lleva implícito en la fase de diseño la determinación del tamaño muestral necesario para la ejecución del mismo (1-4). El no realizar dicho proceso, puede llevarnos a dos situaciones diferentes: primera que realicemos el estudio sin el número adecuado de pacientes, con lo cual no podremos ser precisos al estimar los parámetros y además no encontraremos diferencias significativas cuando en la realidad sí existen. La segunda situación es que podríamos estudiar un número innecesario de pacientes, lo cual lleva implícito no solo la pérdida de tiempo e incremento de recursos innecesarios sino que además la calidad del estudio, dado dicho incremento, puede verse afectada en sentido negativo. Para determinar el tamaño muestral de un estudio, debemos considerar diferentes situaciones (5-7): A. Estudios para determinar parámetros. Es decir pretendemos hacer inferencias a valores poblacionales (proporciones, medias) a partir de una muestra (Tabla 1).

Gouttes... et toiles d&#039;araignées Chapelet de gouttes sur des fils de tension d'une toile d'araignée: revenons sur la régularité dans le diamètre et l'espacement des gouttelettes... tout ceci a donc une explication physique: La voici, issue du livre de P-G de Gennes "Gouttes, bulles, perles et ondes"(1), pp.108-111: "Instabilité de Plateau-Rayleigh: Un second type d'instabilité classique liée aux interfaces est celle des cylindres de liquide. Considérons ainsi une fibre (un cheveu par exemple, dont le diamètre vaut typiquement 100 microns) gainée de liquide. D'une façon très générale, le film est instable: il ondule (avec une longueur d'onde ), tout en conservant son axisymétrie. Afin de connaître le critère de formation des gouttelettes on calcule la différence d'énergie dE entre la surface modulée et le cylindre dont elle est issue. "On voit que l'énergie est abaissée (dE < 0) si (...) la longueur d'onde est supérieure au périmètre du cylindre de départ: (avec R = b + e0)".

RStats Resources - RStats Institute Statistics Tutoring Undergraduate students who need assistance with statistics homework can receive one-on-one tutoring through Missouri State University's Bear CLAW (Center for Learning and Writing). Click here to access Bear CLAW Statistics Tutoring. Instructional Videos Tables and Calculators Click here to access: Normal Distribution TableT Distribution TableCritical Pearson's r ValuesF Distribution TableChi Square Distribution Table and CalculatorCohen's D Effect Size Calculator Notes from Previous RStats Workshops Information About RStats

Wettability, Spreading, and Interfacial Phenomena in High-Temper The deposition of a coating on a solid generates new interfaces between dissimilar materials and involves considerations of wettability, spreading, interface evolution, and adhesion. The wettability of a solid by a liquid is characterized in terms of the angle of contact that the liquid makes on the solid. 1 , 2 The contact angle, q , is obtained from a balance of interfacial tensions ( Figure 1 ) and is defined from Young's equation, according to which s lv .cos q + s ls = s sv where s lv , s ls , and s sv are the interfacial tensions at the boundaries between liquid (l), solid (s), and vapor (v). A large body of useful scientific information about the wettability of and spreading upon high-temperature metal and ceramic coatings comes from the application of Young's equation to metallurgical systems at elevated temperatures. If the liquid vapor is adsorbed on the solid's surface, the surface tension of the solid, s sv , decreases. cos q w = rcos q y F = 2(2) 1/2 s lv cos q /R DeGennes 10

How To Determine Sample Size, Determining Sample Size In order to prove that a process has been improved, you must measure the process capability before and after improvements are implemented. This allows you to quantify the process improvement (e.g., defect reduction or productivity increase) and translate the effects into an estimated financial result – something business leaders can understand and appreciate. If data is not readily available for the process, how many members of the population should be selected to ensure that the population is properly represented? If data has been collected, how do you determine if you have enough data? Determining sample size is a very important issue because samples that are too large may waste time, resources and money, while samples that are too small may lead to inaccurate results. When sample data is collected and the sample mean is calculated, that sample mean is typically different from the population mean . is the maximum difference between the observed sample mean where: is the sample size. . .

StatLib :: Data, Software and News from the Statistics Community R Tutorials--Logistic Regression Preliminaries Model Formulae You will need to know a bit about Model Formulae to understand this tutorial. Odds, Odds Ratios, and Logit When you go to the track, how do you know which horse to bet on? p(one outcome) p(success) p odds = -------------------- = ----------- = ---, where q = 1 - p p(the other outcome) p(failure) q So for Sea Brisket, odds(winning) = (1/9)/(8/9) = 1/8. The natural log of odds is called the logit, or logit transformation, of p: logit(p) = loge(p/q). If odds(success) = 1, then logit(p) = 0. Logistic regression is a method for fitting a regression curve, y = f(x), when y consists of proportions or probabilities, or binary coded (0,1--failure,success) data. y = [exp(b0 + b1x)] / [1 + exp(b0 + b1x)] Logistic regression fits b0 and b1, the regression coefficients (which were 0 and 1, respectively, for the graph above). logit(y) = b0 + b1x Odds ratio might best be illustrated by returning to our horse race. Logistic Regression: One Numerical Predictor I'm impressed!

Inverse Filtering If we know of or can create a good model of the blurring function that corrupted an image, the quickest and easiest way to restore that is by inverse filtering. Unfortunately, since the inverse filter is a form of high pass filer, inverse filtering responds very badly to any noise that is present in the image because noise tends to be high frequency. In this section, we explore two methods of inverse filtering - a thresholding method and an iterative method. Method 1: Thresholding Theory We can model a blurred image by where f is the original image, b is some kind of a low pass filter and g is our blurred image. But how do we find h? In the ideal case, we would just invert all the elements of B to get a high pass filter. So the higher we set , the closer H is to the full inverse filter. Implementation and Results Since Matlab does not deal well with infinity, we had to threshold B before we took the inverse. where n is essentially and is set arbitrarily close to zero for noiseless cases. where

StatThink - Statistical Thinking Diagrams and Models From: Pfannkuch, M., Regan, M., Wild, C. and Horton, N.J. (2010) Telling Data Stories: Essential Dialogues for Comparative Reasoning.Journal of Statistics Education, 18(1). Looking at data Download as a png or an eps From: Forster, M. and Wild, C. Data analysis cycle Download as a png or an eps From: : Wild, C.J. and Pfannkuch, M. (1999) "Statistical thinking in empirical enquiry" (with discussion). Learning via statistics Download as a png or an eps Investigative Cycle (Statistical investigation cycle/PPDAC cycle) Download as a png or an eps Types of Thinking Download as a png or an eps Interrogative Cycle Download as a png or an eps Dispositions Download as a png or an eps From Inkling to Plan Download as a png or an eps Shuttling between the spheres Download as a png or an eps Using any technique Download as a png or an eps Distillation and Encapsulation Download as a png or an eps Sources of variation in data Download as a png or an eps Practical responses to variation Download as a png or an eps

Blind Deconvolution To this point, we have studied restoration techniques assuming that we knew the blurring function h . Actually, we have also assumed that we knew the image spectral density Suu and Spectral noise Snn as well. This section will focus on some techniques for estimating h based on our degraded image. For comparison, we will demonstrate how the MSE between our restored image and the original image changes depending on whether or not we know h, Suu, or Snn. Two restoration filters will be the basis for our procedures. We use as our degradation model the standard idea that our input image is blurred through convolution with a low pass LSI filter (h) and then Gaussian Noise is added to the result. where H is the Fourier Transform of h, and Suu and Snn are defined as above. The above images were generated using wien.m. Next we will examine the effectiveness of Power Spectrum Equalization. Note the similarity to Wiener Filtering, but we only use the magnitude of H.

copyright - J.P.Gourret - 2000 - Maillage multirésolution de sur (Chapitre I-Part 1)....Chapitre I - Introduction (Chapitre II-Part 1)...Chapitre II - Programmation (Chapitre II-Part 2)..........II-1- La bibliothèque graphique et son interface de programmation (Chapitre II-Part 3)....................II-1-1- Ossature classique d'un programme (Chapitre II-Part 4)....................II-1-2- Opérations successives réalisées sur les données (Chapitre II-Part 5)....................II-1-3- Commandes ..........................................................II-1-3-a- Mode immédiat ..........................................................II-1-3-b- Liste d'affichage (Chapitre II-Part 6)....................II-1-4- Exemple de programme (Chapitre II-Part 7)..........II-2- Fonctions pour modéliser l.éclairement et les sources de lumière (Chapitre II-Part 8)....................II-2-1- Réflexion diffuse due à la lumière ambiante (Chapitre II-Part 9)....................II-2-2- Réflexion diffuse due à une source localisée (Chapitre II-Part 14).........II-4- Calcul matriciel

The Geomblog Bill the Lizard: Six Visual Proofs 1 + 2 + 3 + ... + n = n * (n+1) / 2 1 + 3 + 5 + ... + (2n − 1) = n2 Related posts:Math visualization: (x + 1)2 Further reading:Proof without Words: Exercises in Visual ThinkingQ.E.D.: Beauty in Mathematical Proof Also, my thanks go out to fellow redditor cnk for improvements made to the 1/3 + 1/9 + 1/27 + 1/81 + ... = 1/2 graphic.