Data Analysis Plan for Quantitative and Qualitative Research: Data Analytics is the quest of excavating an understanding or inference from unprocessed data via dedicated computer applications. These applications alter, shape, and model the data to infer deductions and ascertain patterns. Although Data Analytics can be uncomplicated, nowadays the term is most frequently used to define the study of huge capacities of data ( quantitative and qualitative data ) and/or high-speed data, which offers unique mathematical and data-juggling obstacles. Proficient data analytics pros who essentially possess a strong know-how in business statistics, are known as data scientists. Data Analytics is a generic word for any type of data manipulation that considers historical data over a time frame; however, as the volume of organizational data increases, the concept of data analytics is developing to support big data-capable systems. The dawn of big data dramatically altered the need f...

Statistics is an integral part of all research studies, a discipline that all researchers and scientists commonly use to analyze their data and arrive at the desired research findings and effectively present meaningful conclusions. Statistical techniques , presently, play a central part in the majority of scientific disciplines and are frequently utilized to support hypotheses in order to provide credibility to research methodology and conclusions arrived from these studies. Thus, many researchers and scholars are keenly interested In Statistics and its various methods and analytical techniques to ascertain the credibility and usefulness of the data and information collected from their respective research studies. This short guide provides essential information relating to important statistical concepts like sampling methods, the role of statistics in scientific research to facilitate them to conduct a well-designed statistical research. What is Statistics? Statistics...

Significance of Statistics in Research Being an integral part of any research related to most subjects, Statistics and its importance in research have been recognized since long. Quality research is invariably in most topic cover portions of statistics. Statistics is linked to a wide range of research activities. This is where scholars feel the need for effective  Ph.D. statistics help  for pursuing their research. Apart from subject knowledge, researchers need to have expertise in statistical methods also. For validating the findings, statistical portions are being made mandatory by institutions across the globe. It is by having theoretical knowledge as well as skill in the application of statistics that researchers will be able to gain an extra edge, making  PhD statistics help  a vital factor. “The goal is to turn data into information, and information into insight.” – Carly Fiorina, former CEO, Hewlett-Packard Co. Conducting Statistical Tests ...

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...