Skip to main content

Posts

Showing posts with the label Z Distribution

Is today's world all about creativity and ideation?

Are they the seeds to be nurtured to bring in automation, innovation and transformation.  There is a saying, necessity is the mother of invention. I would say, innovation is amalgamation of creativity and necessity.  We need to understand the ecosystem, to apply creativity and identify the ideas to bring in change. We need to be competent with changing ecosystem and think beyond the possible. What is the biggest challenge in doing this? "Unlearning and Learning", we think the current ecosystem is the best. Be it health, finserve, agriculture or mechanical domain, we need to emphasize with the stakeholders, to come up with the strategy to drive. The very evident example here is the quality of life is changing every millisecond. Few decades back the phone connection was limited to few, but today all the millennials are having a mobile phone. Now phone is not just a medium to talk, but are so powerful devices that an innovative solution can be developed on it....

Z and T distribution values using R

Hello Data Experts, Let me continue from my last blog http://outstandingoutlier.blogspot.in/2017/08/normality-test-for-data-using-r.html “ Normality test using R as part of advanced Exploratory Data Analysis where I had covered four moments of statistics and key concept around probability distribution, normal distribution and Standard normal distribution. Finally, I had also touched upon how to transform data to run normality test. I will help recap all those 4 moments. Those 4 moments of statistics. First step covers Mean, Median and Mode, it is a measure of central tendency. Second step covers Variance Standard Deviation, Range, it is a measure of dispersion. Third step covers Skewness, it is a measure of asymmetry. Fourth step covers Kurtosis, it is a measure of peakness. To get standardized data use “scale” command using R whereas run “pnorm” command to get probability of a value using Z distribution. To understand if data follows normality we can e...