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Variance(Analysis of) --- Experiments design in statistics --- Latin squares

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The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exe

Analysis of variance. --- Chemistry --- Statistical methods. --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design

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Analysis of variance. --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design

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Analysis of variance. --- Spline theory. --- Spline functions --- Approximation theory --- Interpolation --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design

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The analysis of variance (ANOYA) models have become one of the most widely used tools of modern statistics for analyzing multifactor data. The ANOYA models provide versatile statistical tools for studying the relationship between a dependent variable and one or more independent variables. The ANOYA mod­ els are employed to determine whether different variables interact and which factors or factor combinations are most important. They are appealing because they provide a conceptually simple technique for investigating statistical rela­ tionships among different independent variables known as factors. Currently there are several texts and monographs available on the sub­ ject. However, some of them such as those of Scheffe (1959) and Fisher and McDonald (1978), are written for mathematically advanced readers, requiring a good background in calculus, matrix algebra, and statistical theory; whereas others such as Guenther (1964), Huitson (1971), and Dunn and Clark (1987), although they assume only a background in elementary algebra and statistics, treat the subject somewhat scantily and provide only a superficial discussion of the random and mixed effects analysis of variance.

Analysis of variance --- 519.233.4 --- #ABIB:astp --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design --- Variance analysis. Covariance analysis --- 519.233.4 Variance analysis. Covariance analysis --- Applied mathematics. --- Engineering mathematics. --- Probabilities. --- Statistics . --- Mathematical analysis. --- Analysis (Mathematics). --- Applications of Mathematics. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Analysis. --- 517.1 Mathematical analysis --- Mathematical analysis --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk --- Engineering --- Engineering analysis