World's Information

Some Commonly Used Business Terms and Their Meanings

PoV: Proof of value

Use case: Example of situations where a certain concept can be used

PoC: Proof of Concept is a demo or prototype to determine if a business tool/change is feasible and doing something to help the business problem that initiated a discovery of potential solutions

Gia Thi Nguyen - head of OE Siemens

Covid-19 Situation: What is it, Natural or Man made?

What do you think newest Covid-19 Corona Virus is an act of nature or is it man made?

We don't have any conclusive determination so as to what is the answer to this question but there could be numerous possibilities here. I have an interesting theory; in my opinion, it is something that has been created in a lab. I believe in existence of some entity who could be an individual, group of individuals or a consortium of entities who have come together to create and spread COVID-19 as an experiment. Mark it as Entity-X. Now specifics of the idea ahead:

Who are they? Really: who are they? Entity-X (individual/group of individuals/consortium)

They are very resourceful (scientific, intellectuals, politically connected with power of influencing and controlling the governments and economies, with their reach to internal organization of basic earth infrastructure such as UN, WHO, IMF, World Bank and what nots, well connected in industries, owners of large conglomerates, linked to cutting edge technological research)
They are unimaginably rich- they don't have any dearth of money and have to think about money whey they want to do something.
They are invisible: they are off the radar and know how to maintain low key existence
They are a mix of intellect and keen interest in design of human society as we know it. And this is the key, I think the whole current Corona virus situation is a very elaborate experiment which will design the future of human social order.

These are my thoughts; this is not an economic experience that we are going through (against all the theories that are propagating at the moment such as China spread the virus, it was created in a lab, it is for economic benefit of bio tech companies - and I wouldn't deny that this again is a by product of whole episode), this is rather a social science experiment in layman's term. It is done to see the limits of human society as we know it, consider following:

may be testing at what point does money lose its value,
at what point does civil rest or anarchy comes into play,
how does government chosen by people take care of them and stand with them
how do people behave with each other in constrained environment.

If you look analytically, you will find that:

crime has reduced significantly (over 95% I guess),
there are no criminal incidents reported on media and news papers)
are no deaths due to road accidents,
people are visiting hospitals lesser (and interestingly related deaths are reduced significantly)
so turns out that

This is an exploration of thoughts in my mind about COVID-19 situation, let me know your thoughts or any interesting observations that you may have via comments.

Stat Series- 1.1 What are Parametric and Non Parametric tests?

Question: What are Parametric and Non Parametric tests?
Answer:

Parametric Tests	Non-parametric Test
These tests make assumptions about nature of population which we are studying. E.g. we are assuming that the population follows a certain type of distribution, say Normal. Hence a lot of conclusions can be driven about population based on the sample data.	No such assumptions are made and we consider that data may or may not follow certain distribution.

An Easy Overview of Statistical Tests - 2 Inferential Statistics for Association

Pearson Correlation

EXAMPLES

An example of a positive correlation is the relationship between the speed of a wind turbine and the amount of energy it produces. As the turbine speed increases, electricity production also increases.

An example of a negative correlation is the relationship between outdoor temperature and heating costs. As the temperature increases, heating costs decrease.

Correlation is used to represent the linear relationship between two variables. On the contrary, regression is used to fit the best line and estimate one variable on the basis of another variable

“Covariance” indicates the direction of the linear relationship between variables. “Correlation” on the other hand measures both the strength and direction of the linear relationship between two variables.

Chi Square

Independence of observations.

There is no relationship between the subjects in each group.

The categorical variables are not "paired" in any way (e.g. pre-test/post-test observations).

H1: "[Variable 1] is not independent of [Variable 2]"
H1: "[Variable 1] is associated with [Variable 2]"

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

χ^{2} = \sum_{i = 1}^{R} \sum_{j = 1}^{C} \frac{(o_{i j} - e_{i j})^{2}}{e_{i j}}

o_{i j}

o_{i j}

o_{i j}

o_{i j}

is the observed cell count in the ith row and jth column of the table

e_{i j}

e_{i j}

e_{i j}

e_{i j}

is the expected cell count in the ith row and jth column of the table, computed as

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

e_{i j} = \frac{row i total * col j total}{grand total}

r_{i j}

r_{i j}

r_{i j}

r_{i j}

What is your idea of a great holiday (Beach, Cruise) by Genders (Men, Women)

Respondents were asked their gender and whether or not they were a cigarette smoker. There were three answer choices: Nonsmoker, Past smoker, and Current smoker. Suppose we want to test for an association between smoking behavior (nonsmoker, current smoker, or past smoker) and gender (male or female) using a Chi-Square Test of Independence (we'll use α = 0.05).

The null hypothesis (H0) and alternative hypothesis (H1) of the Chi-Square Test of Independence can be expressed in two different but equivalent ways:

H0: "[Variable 1] is independent of [Variable 2]"

H0: "[Variable 1] is not associated with [Variable 2]"

The test statistic for the Chi-Square Test of Independence is denoted Χ2, and is computed as:

where

o_{i j}

o_{i j}

o_{i j}

e_{i j}

e_{i j}

e_{i j}

The quantity (oij - eij) is sometimes referred to as the residual of cell (i, j), denoted

r_{i j}

r_{i j}

r_{i j}

The calculated Χ2 value is then compared to the critical value from the Χ2 distribution table with degrees of freedom df = (R - 1)(C - 1) and chosen confidence level. If the calculated Χ2 value > critical Χ2 value, then we reject the null hypothesis.

EXAMPLES

Great Reference and refresher on Stat and p values: https://www.mathsisfun.com/data/chi-square-test.html

Pages

Some Commonly Used Business Terms and Their Meanings

Covid-19 Situation: What is it, Natural or Man made?

Stat Series- 1.1 What are Parametric and Non Parametric tests?

An Easy Overview of Statistical Tests - 2 Inferential Statistics for Association

The Technical Knowhow of Web!