With more and more companies using big data, demand for professional data analysts has witnessed exponential growth in the recent years. “Learners of data analysis and interpretation need to have an in-depth understanding of the subject along with the statistical acumen and working knowledge of tool sets to make significant progress in the field of data analytics” – Dr. Venkatesh, Statistician. To gain good expertise in Data analysis and interpretation, the beginner learner requires good foundation in basic statistics, and better skills of how to input, process and analyze data by employing a range of data analytical and visualization tools. The objectives of data analysis and interpretation are to manipulate the data, both qualitative as well as quantitative, to obtain utilizable information that is critical to making informed decisions. It involves the usage of many different methods and procedures that can, Describe and summarize data Evaluate and enhance qua...

The main purpose of this blog is to understand the Testing Group Differences using T-tests, ANOVA, and Non parametric Measures. Choosing the right test for your data analysis is a very difficult task particularly identifying the Different methods from testing group differences is the Biggest challenging task. It is important to have to in-depth Knowledge to understand and calculate T-tests, ANOVA, and Nonparametric, brief interpretation of the output. In order to choose the right statistical test, when analyzing the data from an experiment, we must have a good understanding of some basic statistical terms and concepts: Test for Normality Every data must follow certain distribution. But we have to find the appropriate distribution from goodness of fit test. So, our data is checked through each and every distribution. Hence, goodness of fit test is very tedious. This way of estimation of data is called by parametric tests. Parametric tests always give the reliable estimated...

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