A Study On Importance of Microsoft Excel Data Analysis Statistical Tools in Research Works

Abstract

In the current era statistical for highly developments new techniques and software’s during the find out numerous applications in various expanded field for Agriculture, Industry, Business, Sociology, Share market value, Environmental, Commerce, Medical, Education, Planning, Sciences, Economics, Management, Behaviour, Banking & Financial Sectors etc. It is very useful for data analysis presentation, chart and analysis. The study examines how to add-ins data analysis to Microsoft Excel and analyze statistical tools in research works. Suggestion for tool analysis Descriptive Statistics are central measures to find mean, median, and mode. The arithmetic mean or average is calculated by adding all the values within the data set and then sorting them by the values. Analysis of variance (ANOVA) finds out the relations between two are more groups. Correlation is finding out the relations between two are more groups for positive or negative relationships. Covariance presents a constructive number if the variables are absolutely related. High covariance essentially indicates there is a strong relationship between the variables. At the same time low value means the lowest relationship. T-Test in essence allows the use of comparing average values between two group sets to determine and to find the null hypothesis to be eligible to or not. T-Test Paired is comparative for past and current value for best relationship or not. F-Test is significant to indicate the linear regression representation gives a better fit to the data than a test that contains no independent variables. R-squared value proves that the model fits the data. Z-test is greatest used for large sample sizes (n > 30) mostly utilized to find standard deviation. But this technically is to measure the mean a raw score and z-score find the reason is normal distribution curve. Regression methods consist of deciding a statistical relationship between measurements of two variables y axis and x axis. The value of variables to measurement of R Square value, f test and P value is considered that R-squared values less than 50% than 90% is highly very good measurements data and P value is > 0.05 is highly significant. The described guidelines for creating more awareness strategies and providing information to utilize Microsoft excel data access for the correct tool.