Investigation of Natural Ester based Insulating Liquid using Statistical Hypothesis Testing

2021 ◽  
Vol 141 (10) ◽  
pp. 560-566
Author(s):  
Niharika Baruah ◽  
Rohith Sangineni ◽  
Manas Chakraborty ◽  
Sisir Kumar Nayak
Author(s):  
Sach Mukherjee

A number of important problems in data mining can be usefully addressed within the framework of statistical hypothesis testing. However, while the conventional treatment of statistical significance deals with error probabilities at the level of a single variable, practical data mining tasks tend to involve thousands, if not millions, of variables. This Chapter looks at some of the issues that arise in the application of hypothesis tests to multi-variable data mining problems, and describes two computationally efficient procedures by which these issues can be addressed.


2019 ◽  
pp. 245-264
Author(s):  
Steven J. Osterlind

This chapter describes quantification during the late nineteenth century. Then, most ordinary people were gaining an overt awareness, and probability notions were seeping into everyday conversation and decision-making. However, new forms of abstract mathematics were being developed, albeit with some opposition from Lewis Carroll (Charles Dodgson), who wanted to preserve traditionalist views of Euclidian geometry. The chapter introduces William Gossett, who worked in the laboratory of the Guinness brewery and developed “t-distribution,” which was published as “Student’s t-test.” It also describes his friendship with Sir Ronald Fisher, who developed many statistical hypothesis testing methods, published in The Design of Experiments, such as the ANOVA procedure, and the F ratio. Fisher also developed many research designs for hypothesis testing, both simple and complex, including the Latin squares design, as well as providing a classic description of inferential testing in the thought experiment called “the lady tasting tea.”


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