In current scenario, we are constantly being bombarded with statistics and statistical information. This bombardment includes political polls, customer surveys, sales forecasts Stock market projections, sports statistics, marketing information etc. on daily basis. But have we ever thought how much of these statistics are we able to interpret?

 

Though some of us are able to interpret the stats quickly, it is not the case with everyone. Is it possible for everyone to make sense out of this data?

 

YES!!! Why not? Statistics is a fun & not at all a boring, complex subject. Through the series of my blogs on statistics, I will provide some of the glimpses of statistics to prove that “statistics is fun”

 

So let’s discuss about statistics.

 

“Statistics is a way to get information from data.”

 

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DATA is nothing but facts, especially numerical facts, collected together for reference or information

INFORMATION can be defined as Knowledge communicated concerning some particular facts

So, statistics is the tool which creates understanding from set of data.

 

An Example:-

The statistics course is considered to be a difficult course in an engineering college. Some of the students of that college were very anxious about the same course. The professor has provided last semester’s marks to the students. What can be incurred from the data?

 

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Marks: 95, 87, 86, 80 etc. This is the Data given

 

Information: Class average, Proportion of class achieving A, Distribution of marks etc.  This is the information that can be incurred

 

 

Some Key Statistical Concepts. . .

 

Population

— a population is the group of all items of interest to a statistician.

— Frequently very large; sometimes infinite.

E.g. All 2 Crore people of Mumbai

 

Sample

— A sample is a set of data drawn from the population.

— Potentially very large, but less than the population.

E.g. a sample of 1000 voters exit polled on election day.

 

Parameter

— A descriptive measure of a population.

 

Statistic

— A descriptive measure of a sample.

 

Descriptive Statistics

It is method of organizing, summarizing, and presenting data in a convenient and informative way. These methods include:

Graphical Techniques

Numerical Techniques

 

The actual method used depends on what information we would like to extract. Are we interested in measure(s) of central location? and/or measure(s) of variability (dispersion)? Descriptive Statistics helps to answer these questions.

 

Inferential Statistics

Descriptive Statistics describe the data set that’s being analyzed, but doesn’t allow us to draw any conclusions or make any inferences about the data. Hence we need another branch of statistics: inferential statistics. Inferential statistics is also a set of methods, but it is used to draw conclusions or inferences about characteristics of populations based on data from a sample.

 

 

Statistical Inference. . .

Statistical inference is the process of making an estimate, prediction, or decision about a population based on a sample.

 

So friends, I have introduced you to an exciting science called statistics. I hope you are enjoying the journey along with me.

In the next part, I will cover inferential statistics in details along with concepts like confidence level and significance level…… so enjoy reading………!

 

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Naveen Narayanan

Naveen is a Mechanical Engineer with an MBA in International Business from Thunderbird School of Global Management, USA. He has over seven years experience in the world of Strategy and Change Management and is a Certified Six Sigma Black Belt and Quality Improvement Associate from American Society for Quality. Over the years, he has led several corporate wide transformation programs, mentored change agents, and produced sustainable customer and business results for clients throughout Asia-Pacific. As a Lean Six Sigma Master Black Belt, he has a strong grasp of the fundamentals of statistics and problem solving techniques. He is a certified assessor for Business Excellence conforming to EFQM model and a Certified Internal Auditor for TS 16949:2002 standard. View LinkedIn Profile

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