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An introduction to Data Mining & its Applications in Bioinformatics

With the increasing importance of bioinformatics in agriculture, molecular medicine, microbial genome applications, etc. the research in this field has gained momentum than ever before. Bioinformatics, also known as computational biology, deals with interpreting biological data by using computer science and information technology. Of lately, research in bioinformatics has produced vast amounts of data and will continue to generate proteomic, genomic, etc. data. To analyze and gain deep insights into such biological data necessitates making sense of the information by inferring the data. For instance, gene classification, protein structure prediction, clustering of gene expression data, protein-protein interactions, etc. These processes, in turn, increases the need for interaction between bioinformatics and data mining.   read...

Spoiler Alert Ahead: 6 Great Steps for Conducting Factor Analysis using SPSS

Statistics, a scientific approach to investigating statistical data, is employed to determine associations among the phenomena to define, predict and control their occurrence. To successfully perform statistical tests, it is a must to identify the underlying factors or variables under study. This is when the factor analysis comes into the picture.   Factor analysis, known as a dimension reduction technique, helps to reduce the dimension creating new factors from the old ones by checking the correlations and eigenvalue.  read...

One-Way MANOVA Test: How to Assess If Mean Differences Exist Between the Samples Using SPSS?

It is extended version an ANOVA with two or more dependent variables. ANOVA test is used for evaluating the difference in means between two or more related groups, while a MANOVA test is used for evaluating the difference in two or more vectors of factors.  ASSUMPTIONS: All the observations should be statistically independent. We should have an adequate sample size. As a larger sample size, the better it is. We should be having more cases than the number of variables in each group. In ANOVA, the Dependent variables are normally distributed within the group. whereas in MANOVA,the Dependent variables have multivariate normality within the groups. There are no univariate or multivariate outliers. Univariate outliers are often just called outliers.In one-way MANOVA, we see how to:         (1) detect univariate outliers using box plots using SPSS statistics in order to check outliers         (2) check for multivariate outliers using Mahalanobis distance, which we can do in SPSS  read...