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Use of Statistical Packages for Designing and Analysis of Experiments in Agriculture Latika Sharma and Nitu Mehta* (Ranka) Dept

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Use of Statistical Packages for Designing and Analysis of Experiments in Agriculture Latika Sharma and Nitu Mehta* (Ranka) Dept Popular Article Popular Kheti Volume -2, Issue-1 (January-March), 2014 Available online at www.popularkheti.info © 2014 popularkheti.info ISSN:2321-0001 Use of Statistical Packages for Designing and Analysis of Experiments in Agriculture Latika Sharma and Nitu Mehta* (Ranka) Dept. of Agricultural Economics & Management, Rajasthan College of Agriculture, MPUAT, Udaipur-313001, India *Email of corresponding author: [email protected] As long as analysis of data generated is concerned there are many statistical software packages that come very handy. But as far as generation of design is concerned there are not many software packages available for this purpose. And when we talk of generation of the design, we mean an algorithm that is capable of generating the design already available in the literature. Using computer algorithms, it has not been possible to generate new designs. This is an open area of research. So the purpose of the present article is to give an exposure to various statistical software packages that are useful for designing and analysis of experiments. Introduction Statistical Science is concerned with the twin aspect of theory of design of experiments and sample surveys and drawing valid inferences there from using various statistical techniques/methods. The art of drawing valid conclusions depends on how the data have been collected and analyzed. Depending upon the objective of the study, one has to choose an appropriate statistical procedure to test the hypothesis. When the number of observations is large or when the researcher is interested in multifarious aspects or some time series study, such calculations are very tedious and time consuming on a desk calculator. In this context, it is essential that the manpower engaged in teaching and research is to be trained in the applications of various statistical techniques/methods through the use of computer. An attempt has been made to cover computer aided analysis (using various statistical packages) related to Descriptive Statistics, Test of Significance, Design and Analysis of Experiment, Non parametric method, Forecasting through time-series models and some Financial analysis etc. In agricultural research, the key questions to be answered are generally expressed in terms of hypothesis that has to be verified or disapproved through experimentation. Whenever we want to ascertain the validity of any assertion, we need to generate data and then on the basis of data generated we draw valid conclusions. Thus, any experimentation has two major components, viz., designing the experiment and the analysis of data generated to draw meaningful and valid conclusions. In the earlier days of experimentation, designs were generated in such a way that there was ease in the data analysis. But with the advent of high-speed computers, mere ease in analysis cannot be a strong reason for the generation and then ultimate use of the design. With many statistical Popular Kheti ISSN:2321-0001 112 Popular Sharma and Mehta, 2014, Pop. Kheti, 2(1):112-117 Article software packages available, analyzing the data is not a problem worth naming. Now there are other considerations that go in the choice of a design for a given experimental situation. The design should be cost effective keeping in view the scarce and expensive resources. The design should be such that it provides precise estimates of the comparisons of interest to the experimenter. The design should be able to absorb various shocks like loss of data, presence of outliers, interchange and/or exchange of treatments, model inadequacy, etc., besides providing as small an experimental error as possible, or in other words as small a CV value as possible. So long as the analysis of data generated is concerned there are many statistical software packages that come very handy. But as far as generation of design is concerned there are not many software packages available for this purpose. And when we talk of generation of the design, we mean an algorithm that is capable of generating the design already available in the literature. Using computer algorithms, it has not been possible to generate new designs. This is an open area of research. So the purpose of the present article is to give an exposure to various statistical software packages that are useful for designing and analysis of experiments. 2. Statistical Packages for Designing and Analysis of Experiments The software packages are useful to create cutting edge methodologies and build revealing graphics that can lead to important discoveries from the experimental data. Computers can also help in the cataloguing of the designs, generation of the design, generation of the randomized layout of the design besides providing the analysis of the data generated for drawing statistically meaningful conclusions. One may also use the computers in teaching the subject of design and analysis of experiments in the classroom. For the design and analysis of experiments a number of software packages are available. Some the statistical packages are Statistical Analysis System (SAS), JMP, Statistical Package for Social Sciences (SPSS), SYSTAT, GENSTAT, GLIM, MINITAB, MS- EXCEL, STATISTICA Design Expert Software, MICROSTA, MSTATC, Statistical Package for block designs SPBD etc. SAS : SAS/STAT software, an integral component of the SAS System, provides extensive statistical capabilities with tools for both specialized and enterprise-wide analytical needs. Ready-to-use procedures handle a wide range of statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, and nonparametric analysis. 1. Analysis of Variance :Analysis of variance is a technique for analyzing experimental data. With SAS/STAT software, you can perform analysis of variance for balanced or unbalanced designs, multivariate analysis of variance, and repeated measurements analysis of variance. You can also fit general linear models and mixed models for a variety of data situations, including random effects, repeated measurements, and unbalanced designs. 2. Regression : Regression analysis examines the relationship between a response variable and a set of explanatory variables. The relationship is expressed as an equation that predicts the response Popular Kheti ISSN:2321-0001 113 Popular Sharma and Mehta, 2014, Pop. Kheti, 2(1):112-117 Article variable from a function of the explanatory variables and a set of parameters. SAS/STAT software offers a general regression procedure that uses least squares to estimate the parameters, includes nine different model selection methods, such as stepwise regression, and produces a variety of diagnostic measures. More specialized procedures fit generalized linear models, mixed linear models, nonlinear models, and quadratic response surface models. 3. Categorical Data Analysis : Categorical data are those where the outcome of interest reflects categories, rather than the typical interval scale. The data are often presented in tabular form, known as contingency tables. With SAS/STAT software, you can investigate the association in a contingency table as well as produce measures that indicate the strength of that relationship. You can also use parametric models to investigate the variation of a function of the outcome variable across the various levels of the contingency table, analyzing functions such as means, logits, and proportions. Typical analyses include log-linear models, logistic regression, and bioassay analysis. 4. Multivariate Analysis : Multivariate analysis encompass a wide variety of methods for modelling data with two or more response variables or for identifying relationships among several variables without designating particular variables as response or explanatory variables. You can use common factor analysis to explain the correlations among a set of variables in terms of a limited number of unobservable, or latent, variables. Principal component analysis summarizes a large number of variables with a small number of linear combinations. SAS/STAT software also performs canonical correlation, discriminant analysis, path analysis, and structural equation modelling. 5. Nonparametric Analysis: Nonparametric analysis provides methods for analyzing data that don't require specific distributional assumptions such as normality. Many nonparametric methods are based on the ranks of the observations. SAS/STAT software performs nonparametric analysis of variance, including the Kruskal-Wallis, Wilcoxon-Mann-Whitney, and Friedman tests, as well as other rank tests for balanced or unbalanced one-way or two-way designs. Exact probabilities are computed for many nonparametric statistics. 6. Other Statistical Components in the SAS System: Several other components in the SAS System also provide statistical support. SAS/INSIGHT software is a highly interactive tool for data visualization and interactive data analysis. SAS/QC software provides tools for statistical quality improvement, including tools for statistical quality control and design of experiments. SAS/ETS software includes tools for econometrics and time series analysis. SAS/IML software is a powerful matrix-programming language with extensive mathematical operators and built-in functions that allow you to program statistical algorithms easily. SAS/OR software provides a wide range of optimization methods with numerous statistical applications. Popular Kheti ISSN:2321-0001 114 Popular Sharma and Mehta, 2014, Pop. Kheti, 2(1):112-117 Article JMP JMP is a statistical discovery software
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