Decision Science in Action

In the statistics, we use data to derive the information. It helps in business, research and governance.  We also develop to predict the value or behaviour of any dependent variable based on the historical data. We have following categories of the model based on the type of the variable When the dependent variable is continuous variable: 1.OLS  Linear Regression Model :  When the dependent variable is continuous variable and independent variables is/are continuous variable(s) 2.ANOVA:  When the dependent variable is continuous variable and independent variables is/are categorical variable(s) 3.ANCOVA :  When the dependent variable is continuous variable and as independent variables, we have   continuous  as well as categorical variables as independent variable. When the dependent variable is categorical variable: 1. Maximum Likelihood Logistic Regression Model :  When the dependent variable is categorical variable and independent ...

Today ‘big data’ has become a buzz word. Everyone is talking about it. Big data are characterized by three attributes, which are high volume, high velocity and high variety. A few researchers also recently proposed "high volatility" as the fourth attribute of big data. We can define big data as the collection of data which are so large, complex and ever growing that they cannot be processed and stored using traditional methods. The size of big data is greater than petabytes. This makes storage of big data very difficult. Examples of the big data are web data, telecom data, sensor data of jet engines, RNA-DNA data etc. The challenges in processing big data led to the development of new technologies such as Hodoop and Map Reduce. People claim that big data can be processed in reasonable time using these technologies. But I have yet to use the above technologies. Hence I cannot judge the efficiency of these technologies. When we deal with any data, then we come across ...