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.
Hello Data Experts,
Mean is the average value of datasets. It can be influenced by the outliers. It could also be a measure of central tendency.
Median is the middle most value of the sorted data set, it is partially influenced by outlier. It reflects better distribution of data. It is a better representation of central tendency as there is a lower chance of it getting influenced by outlier. If there are odd number of values in a dataset, then (N+1)/2 th value will be median whereas if there are even number of values then average of N th and (N+1) th value will be the Median value.
Mode is the value which is there most of the times in the dataset i.e., search for most frequent value used.
Standard Deviation is calculated as the square root of variance. Statistically Standard deviation reflects how close are values to the mean and spread. SD helps us determines margin of error, confidence level and significance level as well.
Negative Skewness reflects left long tail and data is more distributed toward right, for example 3,4,5,6, 8,9
Positive Skewness reflects right long tail and data is more distributed toward left. for example, 4,6, 8,9, 10
Positive Kurtosis defined Thin Peak with no long tail. If it needs to be explain in retail domain thin peak covers items like bread and milk are sold maximum.
Negative Kurtosis defines wider peak with long tail.
First moment:
Mean:
# Get Mean value
mean(CarsMileage)
Output will be 12.8
median(CarsMileage)
mode(CarsMileage)
Output will be Numeric
var(CarsMileage)
sd(CarsMileage)
Output will be 1.499123
Let me continue from my last blog http://outstandingoutlier.blogspot.in/2017/08/object-types-and-visualization-using-r.htmll “ Data Types and Visualization using R”
where we discussed Object/Data Types like Vector, List, Factor, Data Frame, Array and Matrix. I did cover how to have a
different graphical representation like Line Graph, Scatter Graph, Pie chart, Bar
Graph, Histogram and Boxplot Graph.
Let us now move forward with core
statistical fundamentals for any statistical problem. We will learn about Exploratory
Data Analysis. Why do we need it and how to perform Exploratory Data Analysis
(EDA)?
Let me first help you understand
what it is EDA is important for Data Scientist because it will help us
-
Understand key attributes about the data like Mean, Median etc.
- EDA will help us visualize if there are any anomalies like outliers.
- Data visualization help us detect if there is any pattern like direct or indirect relationship between 2 set data points.
- Identify data errors, data inconsistency (like Skewness).
- It will help us validate assumptions if data (sample/Population) is appropriate for statistical modeling.
- It will help us identify the right statistical model and avoid biasness.
- It will help us assess strength and direction of data between Input and output variables.
-
First step covers Mean, Median and Mode, it is a measure of central tendency.
- Second step covers Variance, 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.
Mean is the average value of datasets. It can be influenced by the outliers. It could also be a measure of central tendency.
Median is the middle most value of the sorted data set, it is partially influenced by outlier. It reflects better distribution of data. It is a better representation of central tendency as there is a lower chance of it getting influenced by outlier. If there are odd number of values in a dataset, then (N+1)/2 th value will be median whereas if there are even number of values then average of N th and (N+1) th value will be the Median value.
Mode is the value which is there most of the times in the dataset i.e., search for most frequent value used.
Second Moment:
Variance is how spread out are value from
the mean. Sum of all residuals of values to Mean should be Zero. Standard Deviation is calculated as the square root of variance. Statistically Standard deviation reflects how close are values to the mean and spread. SD helps us determines margin of error, confidence level and significance level as well.
Third Moment:
It is a measure of Skewness where
it helps us depicts which side distribution of data is tapered, as against mean.
There could be Positive or Negative Skewness. Negative Skewness reflects left long tail and data is more distributed toward right, for example 3,4,5,6, 8,9
Positive Skewness reflects right long tail and data is more distributed toward left. for example, 4,6, 8,9, 10
Fourth Moment:
It is a measure of tailedness.Positive Kurtosis defined Thin Peak with no long tail. If it needs to be explain in retail domain thin peak covers items like bread and milk are sold maximum.
Negative Kurtosis defines wider peak with long tail.
Let us now take an object having
20 values
CarsMileage
<- c(12, 14, 12.5, 13.5, 15, 10, 11, 12, 12, 14, 12, 11.5, 12.5, 13.5, 15,
10.5, 15, 12, 14, 14)
Let us derive all 4 moments now:
First moment:
Mean:
# Get Mean value
mean(CarsMileage)
Output will be 12.8
Median:
#
Get Median value median(CarsMileage)
Output
will be 12.5
Mode:
#
Get mode value mode(CarsMileage)
Output will be Numeric
Second Moment:
Variance:
#
Get Variance var(CarsMileage)
Output
will be 2.24736
Standard Deviation:
#
Get Standard Deviationsd(CarsMileage)
Output will be 1.499123
Third moment and Fourth moment:
Skewness and Peakness there is no direction
function in R but can be derived using R. I will cover how to derive third and
fourth moment in my future blogs.
I hope you must have got the essence and importance
of four moments in statistical analysis. This is the foundation for rest of the
statistical word. 50-60% of the time of statistician goes in executing EDA to
make sure data captured is complete and unbiased which will help one take right
decision and right outcome. Getting
right data be it a population or sample it is very important for one to make a right
choice. Now that basics for EDA is clear I will help one with visualization to
explore advance statistical problems. In my next blog, I will cover “Advance
statistical concepts using R Studio”.
Thank you for sparing time and going through
this blog I hope it helped you built sound foundation of statistics using R. Kindly
share your valuable and kind opinion. Please do not forget to suggest what you
would like to understand and hear from me in my future blogs.
Thank you...
Outstanding Outliers::
"AG".
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