HE Plots for Repeated Measures Designs

Hypothesis error (HE) plots, introduced in Friendly (2007), provide graphical methods to visualize hypothesis tests in multivariate linear models, by displaying hypothesis and error covariation as ellipsoids and providing visual representations of effect size and significance. These methods are implemented in the heplots for R (Fox, Friendly, and Monette 2009a) and SAS (Friendly 2006), and apply generally to designs with fixed-effect factors (MANOVA), quantitative regressors (multivariate multiple regression) and combined cases (MANCOVA). This paper describes the extension of these methods to repeated measures designs in which the multivariate responses represent the outcomes on one or more âÂÂwithin-subjectâ factors. This extension is illustrated using the heplots for R. Examples describe one- sample profile analysis, designs with multiple between-S and within-S factors, and doubly- multivariate designs, with multivariate responses observed on multiple occasions.

Data Ellipses, HE Plots and Reduced-Rank Displays for Multivariate Linear Models: SAS Software and Examples

This paper describes graphical methods for multiple-response data within the framework of the multivariate linear model (MLM), aimed at understanding what is being tested in a multivariate test, and how factor/predictor effects are expressed across multiple response measures. In particular, we describe and illustrate a collection of SAS macro programs for: (a) Data ellipses and low-rank biplots for multivariate data, (b) HE plots, showing the hypothesis and error covariance matrices for a given pair of responses, and a given effect, (c) HE plot matrices, showing all pairwise HE plots, and (d) low-rank analogs of HE plots, showing all observations, group means, and their relations to the response variables.

Perancangan Mikroprosesor 8 Bit dengan Menggunakan Bahasa Vhdl pada Fpga Xilinx Spartan 3

Kebutuhan perancangan perangkat mikroprosesor yang saat ini didominasi oleh negara maju disebabkan biaya produksi yang tinggi. Untuk mengatasi hal tersebut, beberapa penyedia peralatan semikonduktor mengembangkan FPGA. FPGA ( Field Programmable Gate Array ) merupakan salah satu perangkat digital yang dapat diprogram ulang dengan jumlah gerbang antara 50.000 sampai dengan 5.000.000. Pemanfaatan FPGA untuk membuat dan merancang peralatan-peralatan untuk aplikasi yang rumit dimudahkan dengan banyaknya bahasa yang dapat dipakai untuk memrogramnya. VHDL adalah salah satu dari banyak bahasa yang ditawarkan untuk mengkonfigurasikan sebuah FPGA. Pemanfaatan FPGA dalam merancangan mikroprosesor dapat membantu mengurangkan biaya yang diperlukan untuk membuat perancangan awal. Dalam tulisan ini dipaparkan desain dan perancangan mikroprosesor 8 bit yang akan dapat bekerja seperti halnya mikroprosesor 8 bit lainnya

STRUKTUR DAN MAKNA PARTIKEL KA DALAM KALIMAT BAHASA JEPANG 日本語の文における助詞『か』の構造と意味

ABSTRACT Friendly, Novie. 2018. "Structure and Meaning of Ka Particles in Japanese Sentence". Thesis, Japanese Language and Culture Studies Program, Faculty of Cultural Sciences, Diponegoro University. Supervisor Lina Rosliana, S.S, M.Hum. In writing this thesis, the author examines the "Structure and Meaning of Particle Ka in Japanese Sentence". The purpose of this research is to describe Structure and Meaning of Particle Ka in Japanese Sentence. The author obtained data from novel from the internet as well as novel from the print media. The data were collected using the note technique. Then, to analyze the structure and meaning of ka particles using qualitative descriptive method. Based on the data analysis, it can be concluded that the ka particles can be placed in the middle of the sentence or at the end of the sentence. The ka particles can be attached to the word class of verbs, nouns, i-adjectives, na-adjectives and also the adverb class. The ka particles can also lead to the addition of meaning whether, whether, whether, whether or not, whether or not. Keywords: Structure, Mean, Particle K

The Golden Age of Statistical Graphics

Statistical graphics and data visualization have long histories, but their modern forms began only in the early 1800s. Between roughly 1850 and 1900 ($\pm10$), an explosive growth occurred in both the general use of graphic methods and the range of topics to which they were applied. Innovations were prodigious and some of the most exquisite graphics ever produced appeared, resulting in what may be called the ``Golden Age of Statistical Graphics.'' In this article I trace the origins of this period in terms of the infrastructure required to produce this explosive growth: recognition of the importance of systematic data collection by the state; the rise of statistical theory and statistical thinking; enabling developments of technology; and inventions of novel methods to portray statistical data. To illustrate, I describe some specific contributions that give rise to the appellation ``Golden Age.''Comment: Published in at http://dx.doi.org/10.1214/08-STS268 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org