StatNotes: Topics in Multivariate Analysis, from North Carolina State University. Looking for Statnotes? StatNotes, viewed by millions of visitors for the last decade, has now been converted to e-books in Adobe Reader and Kindle Reader format, under the auspices of Statistical Associates Publishers. The e-book format serves many purposes: readers may cite sources by title, publisher, year, and (in Adobe Reader format) page number; e-books may be downloaded to PCs, Ipads, smartphones, and other devices for reference convenience; and intellectual property is protected against piracy, which had become epidemic. Click here to go to the new Statnotes website at .
Or you may use the Google search box below to search the website, which contains free e-books and web pages with overview summaries and tables of contents. Or you may click on a specific topic below to view the specific overview/table of contents page. Wiki: Statistical Methods. Basic statistics help: Correspondence Analysis Factor Analysis Some nice explanations: KMO and Bartlett's Test of Sphericity (Factor Analysis) The Kaiser-Meyer-Olkin measure of sampling adequacy tests whether the partial correlations among variables are small.
Bartlett's test of sphericity tests whether the correlation matrix is an identity matrix, which would indicate that the factor model is inappropriate. -- from the SPSS on-line help. Path Analysis Structural Equation Modeling Software, including AMOS (which looks good, but kind of expensive): have been seeing several papers (both as a reviewer and as a reader of published work) that use AMOS for CFA, path analysis, or SEM models. Hi Matthew, Thanks very much for sending me the messages on the CRTNET listserv related to Amos. If any CRTNET members want to follow up, please ask them to get in touch with me at ggwebm@amosdevelopment.com. 9.3 Nonhierarchical Clustering. Contents.pdf (application/pdf Object) Cluster Analysis. R has an amazing variety of functions for cluster analysis. In this section, I will describe three of the many approaches: hierarchical agglomerative, partitioning, and model based.
While there are no best solutions for the problem of determining the number of clusters to extract, several approaches are given below. Data Preparation Prior to clustering data, you may want to remove or estimate missing data and rescale variables for comparability. # Prepare Data mydata <- na.omit(mydata) # listwise deletion of missing mydata <- scale(mydata) # standardize variables Partitioning K-means clustering is the most popular partitioning method.
. # Determine number of clusters wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var)) for (i in 2:15) wss[i] <- sum(kmeans(mydata, centers=i)$withinss) plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") A robust version of K-means based on mediods can be invoked by using pam( ) instead of kmeans( ). Hierarchical Agglomerative. Google Search of ATS Stat Computing Pages. Метод на най-малките квадрати » Физичен практикум. Експерименталните данни често се придружават от някакъв шум. Дори да успеем да постигнем точни и постоянни стойности на контролните величини, измерените резултантни величини винаги варират. Необходим е процес, известен като регресия или пасване на крива, за получаване количествена оценка на тенденцията на измерените експериментални величини. В процеса на пасване на крива се избира такава крива, която да дава добро приближение с експерименталните данни.
Изобщо казано, през даден набор от експериментални точки може да се прекарат повече от една крива. Ние ще търсим такава крива, която има минимално отклонение (девиация) за всички точки. Идеята на метода е проста. Където са стойностите на контролната величина, са съответните измерени стойности на резултатната величина, а е избраната функционална зависимост, която трябва да бъде пасната. Тук ще се спрем на случая на линейна зависимост между една независима контролна величина и една резултатна величина, т.е. тя има вида: от вида: . , където. Department of Statistics Homepage.