Design of Experiments (DOE) Tutorial Design of experiments (DOE) is a powerful tool that can be used in a variety of experimental situations. DOE allows for multiple input factors to be manipulated determining their effect on a desired output (response). By manipulating multiple inputs at the same time, DOE can identify important interactions that may be missed when experimenting with one factor at a time. When to Use DOE Use DOE when more than one input factor is suspected of influencing an output. DOE can also be used to confirm suspected input/output relationships and to develop a predictive equation suitable for performing what-if analysis. DOE Procedure Acquire a full understanding of the inputs and outputs being investigated. The negative effect of the interaction is most easily seen when the pressure is set to 50 psi and Temperature is set to 100 degrees. Conduct and Analyze Your Own DOE Conduct and analyze up to three factors and their interactions by downloading the 3-factor DOE template (Excel, 104 KB). Summary

ChemWiki: The Dynamic Chemistry E-textbook - Chemwiki Data Evaluation and Comparisons Introduction → Presentation of data comparison techniques, and the steps for evaluating set of data Hypotheses → Definition of statistical hypotheses about datasets t-tests → t-tests for comparing the means of different datasets One- & Two-tailed tests → Testing whether a mean is greater than, less than, or not equal to, another mean F-test → Testing differences between standard deviations of datasets, for comparing precision You have now seen how to generate a calibration curve for an instrument from a set of linear data, and then use the curve to determine the concentration of an unknown sample from a measured signal. Let's say you just taken a number of concentration readings from a sample of unknown concentration, and you want to determine whether the difference between your measured value and the stated value is statistically significant, or simply do to a random error. There are a few steps for evaluating a dataset or comparing multiple sets of data. © Dr.

Exploratory data analysis In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA),[1] which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA. Overview[edit] Exploratory data analysis, robust statistics, nonparametric statistics, and the development of statistical programming languages facilitated statisticians' work on scientific and engineering problems. EDA development[edit] John W.

'r' tag wiki Links: Allchem/IQ-USP « Pesquisas de Química Estruturando: Materiais e informações na WWW-Internet Selecionar da imensidão de informações sobre química e ciências afins existente na WWW-Internet, materiais de qualidade e que atendam ao amplo espectro de consulentes da AllChemy (de estudantes e profissionais de ensino médio e superior a pesquisadores e professores com pós-doutorado), este é o desafio que enfrentamos nesta página. Auxilie-nos a ampliar a lista dos melhores links, mandando indicações e críticas por e-mail. Química ExpoChemy – Exposição Virtual de Produtos Químicos, Reagentes, Equipamentos (AllChemy) Exposição e Base de Dados mantida pela AllChemy, que permite localizar rapidamente fabricantes ou fornecedores de cerca de uma centena de milhares de produtos químicos, reagentes, serviços, equipamentos, materiais, consultorias, etc…. Química na Internet (link @ Martindale, em inglês) Tabelas Periódicas Tabela periódica com algumas propriedades de cada elemento (link @ Pitágoras)WebElements (link @ Univ. Estruturas Químicas

What Can Classical Chinese Poetry Teach Us About Graphical Analysis? - Statistics and Quality Data Analysis | Minitab A famous classical Chinese poem from the Song dynasty describes the views of a mist-covered mountain called Lushan. The poem was inscribed on the wall of a Buddhist monastery by Su Shi, a renowned poet, artist, and calligrapher of the 11th century. Deceptively simple, the poem captures the illusory nature of human perception. Written on the Wall of West Forest Temple --Su Shi From the side, it's a mountain ridge. Our perception of reality, the poem suggests, is limited by our vantage point, which constantly changes. In fact, there are probably as many interpretations of this famous poem as there are views of Mt. Centuries after the end of the Song dynasty, imagine you are traversing a misty mountain of data using the Chinese language version of Minitab 17... From the interval plot, you are extremely (95%) confident that the population mean is within the interval bounds. From the individual value plot, the data may contain an outlier (which could bias the estimate the mean).

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

Physical Reference Data Elemental Data Index Provides access to the holdings of NIST Physical Measurement Laboratory online data organized by element. Periodic Table: Atomic Properties of the Elements Contains NIST critically-evaluated data on atomic properties of the elements. Suitable for high-resolution color printing for desk or wall-chart display. Contains values of the fundamental physical constants and a related bibliographic database. Contains databases for energy levels, wavelengths, and transition probabilities for atoms and ions and related bibliographic databases. Includes databases containing spectroscopic data for small molecules, hydrocarbons, and interstellar molecules. Contains databases on thermophysical properties of gases, electron-impact cross sections (of atoms & molecules), potential energy surfaces of group II dimers, and atomic weights and isotopic compositions. Contains databases on the interaction of x-rays and gamma-rays with elements and compounds.

Anscombe's quartet All four sets are identical when examined using simple summary statistics, but vary considerably when graphed Anscombe's quartet comprises four datasets that have nearly identical simple statistical properties, yet appear very different when graphed. Each dataset consists of eleven (x,y) points. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance of graphing data before analyzing it and the effect of outliers on statistical properties.[1] For all four datasets: The first scatter plot (top left) appears to be a simple linear relationship, corresponding to two variables correlated and following the assumption of normality. The quartet is still often used to illustrate the importance of looking at a set of data graphically before starting to analyze according to a particular type of relationship, and the inadequacy of basic statistic properties for describing realistic datasets.[2][3][4][5][6] The datasets are as follows. See also[edit]

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