Skip to main content

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.
Post a Comment

Statistical Programming using R

Hello Data Experts,

Let me continue from my last blog http://outstandingoutlier.blogspot.in/2017/08/dataset-using-r.html  “Dataset using R” where we discussed how to load Dataset from CSV and work on basic operations over that dataset.   

Given R is the statistical language, it is important for us to unleash the power of R for statistical analysis. This blog will help you learn statistical calculations such as Mean, Median, Mode, Variance, SD and few other formulas. Let us move forward and understand how to get these values.

Before we delve into statistical programming, let us understand why do we need to calculate all three mean, Median and Mode and not just anyone. Mean represent average value which can get influenced by outlier (higher or lower) value. Median reflects middle most value with less probability of getting influenced by outlier. Mode is the maximum repeated value in the dataset. Standard Deviation derives level of variation. It is very commonly used as 1 sigma,2 Sigma, 3 Sigma, 4 Sigma, 5 Sigma and 6 Sigma. Healthcare and airline industry should meet 6 Sigma hence any industry which can have huge impact either on life or intensity of disaster higher sigma is good. 

To keep this session simple, I will populate an object CarsMileage with certain set of values and later derive statistical values.

Let us populate CarsMileage with 20 random values, which defines mileage for last 20 weeks.

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 calculate Statistical value:

**********
Minimum value is the least value all numbers in the dataset
min(CarsMileage)
This will result in the minimum value i.e., 10

**********
Maximum value is the maximum value all numbers in the dataset
max(CarsMileage)
This will result in the maximum value i.e., 15

**********
Mean value is the average of all values
mean(CarsMileage)
This will result in the mean value i.e., 12.8

**********
Median value is the middle most value of the dataset. Dataset gets sorted in the memory, post that middle value is derived. In case of even number of data points Median is derive as the average of middle 2 values, whereas in case of odd number of values middle most value is the Median.
median(CarsMileage)
Median value of this dataset will be 12.5

***********
Mode is the value which is maximum times there in the dataset for string and data type of numeric.
mode(CarsMileage)
Mode value of this dataset is “numeric”.
 
**********
Standard Deviation of the dataset
sd(CarsMileage)
Standard Deviation value of this dataset is 1.499123

**********
Variance of this dataset
var(CarsMileage)
Variance value of this dataset is 2.247368

**********

Let us find out the probability of 10 mph using standard normal distribution.
pnorm(10, mean(CarsMileage), sd(CarsMileage))

Similarly, if one would like to understand if standardization value follows normal distribution.
qqnorm(CarsMileage)
qqline(CarsMileage)

***********
If in one shot we would like to have a summary of key statistical values, it is very easy to achieve using R programing language.

summary(CarsMileage)

This will get us below set of details:
Min.    1st Qu.    Median    Mean    3rd Qu.    Max.
10.0    12.0        12.5       12.8      14.0        15.0

I hope first glimpse of statistical power must have been very helpful. Now that we have got key statistical formulas handy we should explore more. We should explore more statistical power with R programming in coming sessions. In my next blog, I will cover “Graphical representation of statistical values using R Studio”.

Thank you for sparing time and going through this blog I hope it helped you grasp basics of statistical power 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".    
Labels:

Comments

Post a Comment

Popular posts from this blog

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
Post a Comment
Read more

Practical usage of RStudio features

Hello Data Experts, Let me continue from my last blog Step by Step guide to install R :: “Step by Step guide to install R” where I had shared steps to install R framework and R Studio on windows platform. Now that we are ready with Installation and R Studio, I will take you through R Studio basics. R Studio has primarily 4 sections with multiple sub tabs in each window: Top Left Window: Script editor: It is for writing, Saving and opening R Scripts. Commands part of Script can also be executed from this window. Data viewer: Data uploaded can be viewed in this window.   Bottom Left Window: Console: R Commands run in this window.   Top Right Window: Workspace: workspace allow one to view objects and values assigned to them in global environment. Historical commands: There is an option to search historical commands from beginning till last session. Beauty of this editor is that historical commands are searchable. Once historical commands are searched they can be
Post a Comment
Read more

Code Branch and Merge strategies

Learn Git in a Month of Lunches Hello Everyone, IT industry is going through a disruptive evolution where being AGILE and adopting DevOps is the key catalytic agent for accelerating the floor for success. As explained in my earlier blog, they complement each other rather than competing against one another. If Leaders will at the crossroad where in case they need to pick one what should be their pick. There is no right or wrong approaching, it depends on the scenario and dynamics for the program or project. I would personally pick #DevOps over Agile as its supremacy lies in ACCELERATING delivery with RELIABILITY and CONSISTENCY . This path will enable and empower development teams to be more productive and prone to less rework. Does this mean adopting DevOps with any standard will help reap benefits? In this blog, I will focus on importance of one of the standard and best practice around Code branching and merging strategy to get the desired outcome by adopting DevOps. To
Post a Comment
Read more