data science training
                     Data Science using R, 
Minitab & XLMiner 
© 2013 - 2016 ExcelR Solutions. All Rights Reserved 
R, Minitab XLMiner for Forecasting 
My Introduction 
Name: Bharani Kumar 
Education: IIT Hyderabad 
Indian School of Business 
Professional certifications: 
PMP PMI-ACP PMI-RMP CSM LSSGB 
Project Management Professional 
Agile Certified Practitioner 
Risk Management Professional 
Certified Scrum Master 
Lean Six Sigma Green Belt LSSBB SSMBB ITIL 
Lean Six Sigma Black Belt 
Six Sigma Master Black Belt 
Information Technology Infrastructure Library Agile PM Dynamic 
System Development Methodology Atern 
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My Introduction 
3 
RESEARCH in ANALYTICS, DEEP LEARNING & IOT 
4 
2 Deloitte 1 Driven using US policies DATA
SCIENTIST 
Infosys Driven using Indian policies under Large enterprises 
ITC Infotech Driven using Indian policies SME 
HSBC Driven using UK policies 
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Tuckman Model 
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AGENDA Data Visualization using Tableau 
Data Mining – Supervised & 
Unsupervised (Machine 
Learning) 
Text Mining & NLP AGENDA 
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What does it take to be a DATA 
SCIENTIST? 
Domain All Agenda 
Data 
Knowledge Topics Statistical Analysis 
Minin g 
Practice 
Forecasting 
Successful Data Scientist 
Data Visualizatio n 
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Welcome to the Information 
Age ... ... drowning in data and 
starving for Knowledge 
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BIG DATA! 
https://www.techinasia.com/alibaba-crushes-records-
brings-143-billion-singles-day 
500 million tweets every day, 1.3 billion accounts 
YouTube users upload 100 hours of video every minute 
100 terabytes of data uploaded daily
http://www.dnaindia.com/scitech/report-facebook-saw-
one-billion-simultaneous-users-on-aug-24-2119428 
Processing 100 petabytes a day (1 petabyte = 1000 
terabytes) 
More than 1 million customer transactions every hour 
306 items are purchased every second 26.6 Million 
transactions per day 
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Why Tableau? 
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Why Tableau? 
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Why Tableau? 
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Why Tableau? 
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Agenda – Basic Statistics 
Graphical representation
– Barplot, Histogram, 
12345 Boxplot, Scatter diagram
Data Types – Simple Linear 
Continuous, Discrete, Regression 
Nominal, Ordinal, 
Interval, Ratio, Random 
Variable, Probability, Hypothesis Testing 
Probability Distribution 
First, second, third & © 2013 - 2016 ExcelR Solutions. 
fourth moment business All Rights Reserved 
decisions 
Data Types – Continuous & 
Discrete 
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Data Types – Preliminaries 
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Random Variable 
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Probability 
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Probability Distribution 
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Probability Applications 
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Sampling Funnel 
Population 
Sampling Frame 
SRS 
Sample 
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Measures of Central Tendency 
“Every American should have above 
average income, and my Administration is 
going to see they get it.” – American 
President 
Central Tendency Population Sample 
Mean / Average 
Median Middle value of the data 
Mode Most occurring value in the data 
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Measures of Dispersion 
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Measures of Dispersion 
Dispersion Population Sample 
Variance 
Standard Deviation 
Range Max – Min 
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Expected Value 
◆ For a probability distribution, the mean of the distribution
is known as the expected 
value 
◆ The expected value intuitively refers to what one would 
find if they repeated the 
experiment an infinite number of times and took the 
average of all of the outcomes 
◆ Mathematically, it is calculated as the weighted average
of each possible value 
The formula for calculating The variance of a discrete 
the  expected  value  for  a random variable X, 
discrete  random  variable denoted by σ2 is 
X, denoted by μ, is: 
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Graphical Techniques – Bar 
Chart 
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Graphical Techniques – 
Histogram 
A Histogram Represents the frequency distribution, i.e., 
how many observations take the value within a certain 
interval. 
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Skewness & Kurtosis 
Third and Fourth moments 
Skewness Kurtosis 
• A measure of the • A measure of asymmetry in 
“Peakedness” of the the distribution 
distribution • Mathematically it is given by 
• Mathematically it is given by E[(x-μ/σ)]3 
E[(x-μ/σ)]4 -3 • Negative skewness implies 
• For Symmetric distributions, mass of the distribution is 
negative kurtosis implies wider concentrated on the right 
peak and thinner tails 
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Graphical Techniques – Box Plot
Box  Plot  :  This  graph  shows  the you  ignore  outliers,  the  range  is
distribution of data by dividing the data illustrated  by  the  distance
into four groups with the same number between the opposite ends of the
of data points in each group. The box
contains  the  middle  50%  of  the  data whiskers 
points  and  each  of  the  two  whiskers Range(IQR): The middle half
contain  25%  of  the  data  points.  It of a data set falls within the
displays two common measures of the inter- quartile range 
variability or spread in a data set 
Range : It is represented on a box
plot by the distance between the
smallest  value  and  the  largest Inter- quartile 
value,  including  any  outliers.  If
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Normal Distribution 
▪ The normal random variable takes values 
from -∞ to +∞ 
▪ The Probability associated with any single value of 
a random variable is always zero 
▪ Area under the entire curve is always equal to 1 
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Characterized by a bell shaped curve 
Normal Has the following properties: 
Distribution 
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68.26% of values lie within ±1 σ from the 99.73% of the values lie within ± 3σ from
mean the mean 
95.46% of the values lie within ±2 σ from 99.73% of the values lie within ± 3σ from
the mean the mean 
Normal Distribution 
Characterized by mean, μ, and standard deviation, σ 
X~N(μ,σ) 
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Z scores, Standard Normal 
Distribution 
• For every value (x) of the random variable X, we can 
calculate Z score: 
X−μ
Z = σ 
• Interpretation − How many standard deviations 
away is the value from the mean ? 
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Calculating Probability from Z 
distribution 
Suppose GMAT scores can be reasonably modelled using
a normal distribution 
− μ = 711 σ = 29 
What is p(x ≤ 680)? 
Step 1: Calculate Z score corresponding to 680 
- Z = (680-711)/29 = -1.06 
Step 2: Calculate the probabilities using Z – Tables 
- P(Z ≤ -1) = 0.14 
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Calculating Probability from Z 
distribution 
• What is P( 697 ≤ X ≤ 740) ? 
• Step 1 : Use P(x1 ≤ X ≤ x2) = Use P( X ≤ x2) − P(
X ≤ x1) 
• Step 2 : Calculate P( X ≤ x2) and P( X ≤ x1) as 
before 
P( X ≤ 740) = P( Z ≤ 1) = 0.84 ; P( X ≤ 697) = P( Z 
≤ - 0.5) = 0.31 
• Step 3 : Calculate P( 697 ≤ X ≤ 740 ) = 0.84 – 
0.31 = 0.53 
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Normal Quantile (Q-Q) Plot 
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ample Quantiles 
S
Theoretical Quantiles 
Sampling variation 
▪ Sample mean varies from one sample to another 
▪ Sample mean can be (and most likely is) different from 
the population mean 
▪ Sample mean is a random variable 
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Central Limit Theorem 
The Distribution of the sample mean 
- will be normal when the distribution of data in the population is 
normal 
- will be approximately normal even if the distribution of data in the 
population is not normal 
if the “sample size” is fairly large 
_ Mean ( X ) = μ ( the same as the population mean of the raw 
data) 
Standard Deviation (X) = √n 
σ , where σ is the population standard deviation and n is the sample size 
- This is referred to as standard error of mean 
The standard error of the mean estimates the variability 
between samples whereas the standard deviation 
measures the variability within a single sample 
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Sample Size Calculation 
A Sample Size of 30 is considered large enough, but that 
may /may not be adequate 
More Precise conditions 
- n > 10( K3 )2 , where ( K3 ) is sample skewness and - n > 
10( K4 ) , where ( K4) is sample kurtosis 
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Confidence Interval 
• What is the Probability of tomorrow’s temperature being 
42 degrees ? 
Probability is ‘0’ 
• Can it be between [-500C & 1000C] ? 
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Case Study: Confidence Interval 
• A University with 100,000 alumni is thinking of offering a 
new affinity credit card to its alumni. 
• Profitability of the card depends on the average balance 
maintained by the card holders. 
• A Market research campaign is launched, in which about 
140 alumni accept the card in a pilot launch. 
• Average balance maintained by these is $1990 and the
standard deviation is $2833. Assume that the population
standard deviation is $2500 from previous launches. 
• What we can say about the average balance that 
will be held after a full−fledged market launch ? 
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Interval estimates of parameters 
• Based on sample data 
− The point estimate for mean balance = $1990 
− Can we trust this estimate ? 
• What do you think will happen if we took another random
sample of 140 alumni ? 
• Because of this uncertainty, we prefer to provide the 
estimate as an interval (range) and associate a level of 
confidence with it 
Point Estimate ± 
Interval Estimate = 
Margin of Error 
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Confidence Interval for the 
Population Mean 
Start by choosing a confidence level (1-α) % (e.g. 95%, 
99%, 90%) 
Then, the population mean will be with in 
X _ 
±Z1-ᾳ √n σ where Z1-ᾳ satisfies p( -Z1-ᾳ ≤ Z ≤ Z1-ᾳ) = 1-ᾳ 
Point Estimate ± 
Interval Estimate = 
Margin of Error 
Margin of error depends on the underlying uncertainty, 
confidence level and sample size 
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Calculate Z value - 90%, 95% & 
99% 
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Confidence Interval Calculation 
• Based on the survey and past data 
− n = 140; σ = $2500; _ 
X = $ 1990 − σ - X 
2500 
= σ √n = 
= 211.29 
√140 
• Construct a 95% confidence interval for the mean card 
balance and interpret it ? 
• Construct a 90% confidence interval for the mean card 
balance and interpret it ? 
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Confidence Interval 
Interpretation 
Consider the 95% Confidence interval for the mean 
income : [$1576, $2404] 
Does this mean that 
- The mean balance of the population lies in the range ? 
- The mean balance is in this range 95% of the time ? 
- 95% of the alumni have balance in this range ? 
Interpretation 1 : Mean of the population has a 95% chance of being
in this range for a random sample 
Interpretation 2 : Mean of the population will be in this range for 
95% of the random samples 
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What if we don’t know Sigma? 
• Suppose that the alumni of this university are very 
different and hence population standard deviation from 
previous launches can not be used 
We replace σ with our best guess (point estimate) s, which is the 
standard deviation of the sample: 
Calculate 
• If the underlying population is normally distributed , T is a
random variable distributed according to a t-distribution 
with n-1 degrees of freedom Tn-1 
• Research has shown that the t-distribution is fairly robust
to deviation of the population of the normal model 
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Student’s t-distribution 
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As n ꝏ 
tn N(0,1) 
i.e., as the degrees of the freedom increase, the t-
distribution approaches the standard normal distribution 
Confidence Interval for mean 
with unknown Sigma 
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Calculating t-value 
• Construct a 95% confidence interval for the mean card 
balance and interpret it? 
− n = 140; σ = $2500; X = $ 1990 
− σ X 
_ 
- = 2833 
= 239.46 
√140 
Calculate t0.95, 139 = 1.98 
Then the 95% confidence interval for balance is 
[$1516, $2464] 
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Hypothesis Testing 
Start with Hypothesis about a Population Parameter 
1-α Collect Sample Information 
Reject/Do Not Reject Hypothesis 
1-β 
The factors that affect the power of a test include sample size, effect
size, population variability, and α. Power and α are related as 
increasing α decreases β. Since power is calculated by 1 minus β, if
you increase α,You also increase the power of a test. The maximum
power a test can have is 1, whereas the minimum value is 0. 
Right Decision Confidence 
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Ho is TRUE H1 is TRUE 
Fail to Reject Ho 
Reject Ho 
Type II error 
Type I error 
Right Decision Power 
Hypothesis Testing 
Our quality will not improve after the consulting project 
We will acquire 8,000 new customers if I open a store in this area 
Our potential customers do The retail market will grow by 
not spend more than 60 50% in the next 5 years 
minutes on the web every day 
Less than 5% clients will default on their loans 
We will need 400 more person hours to finish this project 
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Hypothesis Testing 
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Hypothesis Testing 
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1-Sample Z test 
1 
2 3 
Normality Test 
Population Standard Deviation Known or Not Stat > Basic Statistics > 
Graphical Summary 
Fabric Data 
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The  length  of  25  samples  of  a  fabric  are  taken  at  random.  Mean  and
standard  deviation  from  the  historic  2  years  study  are  150  and  4
respectively.  Test  if  the current  mean is greater than the historic mean.
Assume α to be 0.05 
1 Sample Z Test 
Stat > Basic Statistics > 1 Sample Z 
1-Sample Z test – Write 
Hypothesis 
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We are comparing mean with Population standard deviation is 
external standard of 150mm known=4 
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Data was shown to be normal All Rights Reserved 
Y: Fabric Length is continuous X: 
Discrete 1 Population 
1-Sample t Test 
1 
2 3 
Normality Test 
Population Standard Deviation Known or Not Stat > Basic Statistics > 
Graphical Summary 
Bolt Diameter 
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The mean diameter of the bolt manufactured should be 10mm to be able to
fit into the nut. 20 samples are taken at random from production line by a
quality inspector.  Conduct a test  to check with 95% confidence that the
mean is not different from the specification value. 
1 Sample t Test 
Stat > Basic Statistics > 1 Sample t 
1-Sample t Test – Write 
Hypothesis 
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Y: Bolt Diameter is continuous X: Discrete 1 Population We are comparing 
mean with external standard of 10mm 
Data was given to be Normal 
Population standard deviation is NOT known 
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1-Sample Sign Test 
1 
3 
Normality Test 
1 Sample Sign Test 
Stat > Basic Statistics > 
Stat > Non Parametric > Graphical Summary 
1 Sample sign 
Student Scores 
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The scores of 20 students for the statistics exam are provided. Test if the
current median is not equal to historic median of 82. Assume ‘α’ to be 0.05 
1-Sample Sign Test – Write 
Hypothesis 
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2-Sample t Test 
1 
2 3 
Normality Test 
Variance Test 
Stat > Basic Statistics > 
Stat > Basic Statistics > Graphical Summary 
2 Variance 
Marketing Strategy 
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A financial analyst at a Financial institute wants to evaluate a recent credit
card  promotion.  After  this  promotion,  450  cardholders  were  randomly
selected. Half received an ad promoting a full  waiver of interest rate on
purchases made over the next three months, and half received a standard
Christmas advertisement.  Did the ad promoting full  interest  rate waiver,
increase purchases? 
2 Sample t Test 
Stat > Basic Statistics > 2-Sample t 
2-Sample t Test – Write 
Hypothesis 
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Hypothesis Testing 
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Paired T Test 
• This test is used to compare the means of two sets of 
observations when all the other external conditions are the
same 
• This is a more powerful test as the variability in the 
observations is due to differences between the people or 
objects sampled is factored out 
Example: To find out if medication A lowers blood 
pressure 
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Trigger your Compare the power output of two wind mills next to each 
other simultaneously when you 
thoughts! use motor A on one wind mill 
and motor B on another 
Comparing the performance of 
machine A vs. machine B by Identifying resistor defects and
feeding different raw materials capacitor defects in same PCB
to each machine by  collecting  such  data  using
20 PCB units 
Compare the performance of for 1 month and motor B for 1 
machine A vs. machine B when month 
the same raw material is fed to 
each machine 
Identifying resister defects on 
Compare the power output of a 20 PCB’s and capacitor defects 
wind mill when you use motor A on 20 (different) PCB’s 
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2-Sample t test or Paired T test 
Effect of fuel additive on vehicles is being studied. Out of a
total of 20 vehicles, 10 vehicles are chosen randomly and
mileage is recorded. In rest of the 10 vehicles, additive to
be  tested  is  added  with  the  fuel  and  their  mileage  is
recorded. Find if the mileage increases by adding the fuel
additive. 
2-Sample t test 
Assume the same data was recorded if only 10 vehicles
were chosen and mileage was recorded before and after
adding the additive. What method will you choose to find
the result. 
Paired T test 
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Mann-Whitney test 
1 
2 
Normality Test 
Mann – Whitney test for Medians Stat > Basic Statistics > Graphical
Summary 
Stat > Non Parametric > Mann Whitney 
Vehicle with & without Additives 
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Effect  of  fuel  additive on vehicles is being studied.  Out of  a total  of  20
vehicles, 10 vehicles are chosen randomly and mileage is recorded. In rest
of the 10 vehicles, additive to be tested is added with the fuel and their
mileage  is  recorded.  Find  if  the  mileage  increases  by  adding  the  fuel
additive. 
Mann-Whitney Test – Write 
Hypothesis 
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Paired T test 
1 
2 
Normality Test 
Paired T Test 
Stat > Basic Statistics > 
Stat > Basic Statistic > Graphical Summary 
Paired T 
Vehicle with 
& without Additives 
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Effect  of  fuel  additive on vehicles is being studied.  Out of  a total  of  20
vehicles, 10 vehicles are chosen randomly and mileage is recorded. In rest
of the 10 vehicles, additive to be tested is added with the fuel and their
mileage  is  recorded.  Find  if  the  mileage  increases  by  adding  the  fuel
additive.  Assume the same data was recorded if  only 10 vehicles were
chosen and mileage was recorded before and after adding the additive. 
• Since the data was not normal, the cause of non-normality was investigated and it was
found that the first data point for “with additive” was wrongly entered. This value should 
have been 20. Now, proceed with the rest of the analysis. 
• If the data were truly non-normal our analysis would stop here. 
Paired T test – Write Hypothesis 
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One-Way ANOVA 
1 
2 3 
Normality Test 
Variance Test 
Stat > Basic Statistics > 
Stat > ANOVA > Graphical Summary 
Test for Equal Variances 
Contract Renewal 
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A marketing organization outsources their back-office operations to three
different suppliers. The contracts are up for renewal and the CMO wants to
determine whether they should renew contracts with all  suppliers or any
specific supplier. CMO want to renew the contract of supplier with the least
transaction time.  CMO will  renew all  contracts if  the performance of  all
suppliers is similar 
ANOVA 
Stat > ANOVA > One-Way.... 
Example : More weight reduction
programs 
• Suppose the nutrition expert would like to do a 
comparative evaluation of three diet programs(Atkins, 
South Beach, GM) 
• She randomly assigns equal number of participants to 
each of these programs from a common pool of volunteers
• Suppose the average weight losses in each of the 
groups(arms) of the experiments are 4.5kg, 7kg, 5.3kg 
• What can she conclude? 
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Two kinds of variation matter 
• Not every individual in each program will respond 
identically to the diet program 
• Easier to identify variations across programs if variations 
within programs are smaller 
• Hence the method is called Analysis of 
Variance(ANOVA) 
• With-in group variation = Experimental Error 
• Between group variation 
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Formalizing the intuition behind 
variations 
• It should be obvious that for every observation : Totij = ti +
eij 
• What is more surprising and useful is: 
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Statistically test for equality 
means 
• n subjects equally divided into r groups 
• Hypothesis - H0: μ1 = μ2 = μ3 = ... = μr - Not all μi are 
equal 
• Calculate - Mean Square Treatment MSTR = SSTR / (r‐
1) - Mean Square Error MSE = SSE / (n‐r) - The ratio of 
two squares f = MSTR/MSE = Between group 
variation/Within group variation - Strength of this evidence 
p‐value = Pr(F(r‐1,n‐r) ≥ f) 
• Reject the null hypothesis if p‐value < α 
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Analysis of variance(ANOVA) 
• ANOVA can be used to test equality of 
means when there are more then 2 
populations 
• ANOVA can be used with one or two 
factors 
• If only one factor is varying, then we would
use a one-way ANOVA 
– Example: We are interested in comparing the mean performance of 
several departments within a company. Here the only factor is the name of 
department 
– If there are two factors, we would use a two way ANOVA. Example: One 
factor is department and the second factor is the shift.(day vs. Night) 
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Analysis of variance(ANOVA) 
Source of Variation Sum of Squares (SS) Degrees of Freedom Mean 
Square (MS) F Test Statistic 
Between Treatments SSFactor K-1 DFFactor 
MSFactor = SSFactor / F = MSFactor / MSError 
One Way ANOVA 
Within Treatment SSError N-k MSError = SSError / 
DFError 
Total SSTotal N-1 
Two Way ANOVA 
Source of Variation Sum of Squares (SS) Degrees of Freedom Mean 
Square (MS) F Test Statistic 
Factor A SSA nA- 1 MSA = SSA / (nA– 1) FA = MSA / MSE 
Factor B SSB nB- 1 MSB = SSB / (nB– 1) FB = MSB / MSE 
Interaction A * B SSAB (nA– 1) (nB– 1) MSAB = SSAB / (nAB– 1) FAB = MSAB / MSE 
Error SSE n – nA * nB MSE = SSE / (n – nA * nB) 
Total SST n -1 
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Is the Transaction time dependent on
Dichotomies whether  person  A  or  B  processes
thetransaction? 
Is medicine 1 effective or medicine 2 
at reducing heart stroke? Three different sale closing methods 
were used. Which one is most 
Is the new branding program more effective? 
effective in increasing profits? 
Does  the  productivity  of  employees Four types of  machines are used. Is
vary  depending  on  the  three  levels? weight of the Rugby ball dependent on
(Beginner,  Intermediate  and the type of machine used? 
Advanced) 
2 Sample t-test ANOVA – One Way 
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Non-Parametric equivalent to 
ANOVA 
• When the data are not normal or if the data points
are very few to figure out if the data are normal and
we have more than 2 populations, we can use the
Mood’s Median or Kruskal Wallis test to compare the
populations 
Ho : All the medians are the same Ha: One of the 
medians is different 
•  Mood’s  median  assigns  the  data  from  each
population that is higher than the overall median to
one group, and all points that are equal or lower to
another  group.  It  then  uses  a  Chi-Square  test  to
check  if  the  observed  frequencies  are  close  to
expected frequencies 
• Kruskal Wallis is another test that is non-
parametric equivalent of ANOVA. Kruskal Wallis is 
the extension of Mann-Whitney test 
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