Reacquaint yourself with simple yet important data analysis tools to transition from abstract numbers to quantifiable success. In today’s digital era, we are inundated with information—it’s overwhelming. Leaders at the top helm make decisive decisions daily. Sorting through avalanche of data to assist management’s decision making is the key. Objective is to sort and  interpret  and report findings. Toward this end, tools or techniques for statistical data analyses achieve this objective. Buzzwords we hear today are big data, hadoop, and AI; however, rudimentary tools need to be deployed to their maximum capability, which is not the case in most firms. Propel your  data analysis   program by mastering the following 5 basic techniques. Review these mundane statistical techniques, yet so vital. Techniques Merits Demerits 1. Hypothesis Testing Hypothesis tests are used in multiple disciplines such as science, research, economics, etc. Popularly kn...

Significance of Quantitative Statistical Data Analysis Statistical Data analysis in quantitative research  generally involves varied statistical techniques such as regression analysis, multivariate analysis, significance testing, and so on. These can be efficiently performed by expert analysts having quantitative skills and extensive knowledge in statistics. Data can statistically be inferred only after performing the  Quantitative Data Analysis . You have to turn raw numbers into meaningful data in  Quantitative Data Analysis  by applying critical and rational thinking. As same figure within a data-set may be interpreted in different ways, it becomes vital to apply careful and fair judgment.  Data analysis in quantitative research  must be performed by professionals having relevant experience and skill. “Statistics is the grammar of science.” Karl Pearson Quantitative Research Methods for PhD Students – the Challenges: It is usual for...

Tremendous amount of data has been collected by organizations all around the world thanks primarily to the World Wide Web! This collection is useless unless it is analyzed to see a pattern. It is important for decision makers, researchers and policy makers throughout industry, academia and Governments to integrate the huge collected data to suit their needs.  A good software  provider will seek to understand your organization’s unique needs and work with you to find the most cost-effective solution. How does one decide the best statistical software to use? There are questions of price, time available to learn to use it and most importantly the nature of data. Excel is quite a powerful instrument often used in biomedical research to combine large volumes of data. It does contain some  basic statistical analysis  and could serve for simple inferences. Data can be exported into an excel spreadsheet, making it easy to test or determine the dispersion...

Statistics are handled efficiently in myriad ways in the field of data analysis. Statistical analysis is a module of data analytics. A representative selection from a total population is drawn and the data collected is scrutinized so that the sample data speak for a set of items dealt with, in the framework of business intelligence. It involves a description of the nature of data that is to be analysed, drawing a parallel between the sample data selected and the underlying population, coming up with a model that recapitulates the understanding of how the sample data is in regard to the total population, substantiating the standpoint that the model is reliable, and making use of predictive analytics in order to enlist possible scenarios that make the study relevant, paving the way for further research. Demography and statistics are yet another link that is often explored, making utmost use of statistical data analysis services for the population study, research, administratio...