Section 1
##### Introduction to Analytics

1

Introduction to Excel

2

Conditional Formatting

3

Data Summarization techniques

4

Graphical summary using SAS/GRAPH: Introduction to Bar graph

5

Graphical summary using SAS/GRAPH: Introduction to Pie graph

6

Graphical summary using SAS/GRAPH introduction to Histogram, Box plots, Scatter diagram

7

Descriptive Statistics-Introduction to various measures of Central Tendency

8

Introduction to the measures of Dispersion, Range, Mean Deviation , Standard Deviation

Section 2
##### Understanding Probability and Probability Distribution

9

Introduction to Probability theory

10

Types of probability distribution – Discrete Distribution and Continuous distribution

11

Understanding Probability Mass Function and Probability Density Function

12

Normal Distribution and Standard Normal Distribution

13

Normal plot using Proc GPLOT procedure in SAS

14

Application of Normal distribution in Analytics with real life examples

15

Binomial Distribution and Binomial plot using PROC GPLOT procedure in SAS

16

Poisson distribution and Poisson plot using Proc GPLOT procedure in SAS

17

Application of Binomial and Poisson distribution in Analytics with real life examples

Section 3
##### Introduction to Sampling Theory and Estimation

18

Concept of Population and Sample

19

Use of PROC SURVEYSELECT procedure in SAS

20

Introduction to Some important terminologies

21

Parameter and Statistic

22

Properties of a good estimator

23

Standard Deviation and Standard Error

24

Point and Interval Estimation

25

Confidence level and level of Significance

26

Constructing Confidence Intervals

27

Formulation of Null and Alternative hypothesis

28

Performing simple test of Hypothesis

Section 4

Section 5
##### Statistical Significance of T-Tests Chi Square Tests and Analysis of Variance

29

Performing test of one sample mean using Proc ttest

30

Difference between two group means (independent sample) using Proc ttest

31

difference between two group means (Paired sample) using Proc ttest

32

Performing Chi-square tests: Test of Independence

33

Performing one-way ANOVA with PROC ANOVA and PROC GLM procedure

34

Performing post-hoc multiple comparisons tests in PROC

35

GLM using Tukey’s mean test

Section 6
##### Introduction to Segmentation Techniques: Factor Analysis

36

Introduction to Factor Analysis and various techniques

37

Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA)

38

Application of Factor Analysis using Proc Factor procedure

39

KMO MSA test, Bartlett’s Test Sphericity

40

The Mineigen Criterion, Scree plot

41

Introduction to Factor Loading Matrix

42

Various rotation techniques like Varimax

Section 7
##### Introduction to Segmentation Techniques: Cluster Analysis

43

Introduction to Cluster Analysis and various techniques

44

Hierarchical and Non – Hierarchical Clustering techniques

45

Using Hierarchical Clustering by Proc Tree procedure in SAS

46

Performing K – means Clustering in SAS

47

Divisive Clustering, Agglomerative Clustering

48

Application of Cluster Analysis in Analytics with profiling of the clusters and interpretation of the clusters

Section 8
##### Correlation and Linear Regression

49

Introduction to Pearson’s Correlation coefficient using PROC CORR procedure

50

Correlation and Causation – Fitting a simple linear regression model with the Proc REG procedure

51

Understanding the concepts of Multiple Regression

52

Using automated model selection techniques in PROC REG to choose the best model

53

Interpretation of the model: overall fit of the model and finding out the influential variables

54

Linear Regression diagnostics

55

Examining Residual

56

Assessing Collinearity, Heteroskedasticity and Auto – Correlation

Section 9
##### Introduction to Categorical Data Analysis and Logistic Regression

57

Comparison between Liner Regression and Logistic Regression

58

Performing Logistic regression using Proc Logistic Procedure in SAS

59

Performing Goodness of ft of the model

60

Introduction to Percent Concordant, AIC, SC, and Hosmer – Lemeshow

61

Receiver Operating Characteristics (ROC) Curve and Area under Curve (AUC)

62

Interpretation of the model: overall fit of the model and finding out the influential variables using Odds ratio criteria

63

Using automated model selection techniques in PROC Logistic to choose the best model using AIC criteria

Section 10
##### Introduction to Time Series Analysis

64

What is Time series Analysis, Objectives and Assumptions of Time Series

65

Identifying pattern in Time series data: Decomposition of the time series data and general aspect of the analysis

66

Introduction to Various Smoothing techniques: Simple Moving Average, Weighted Moving Average, Exponential Smoothing, Holt’s Linear Exponential Smoothing

67

Examples of Seasonality and detecting Seasonality in Time series data

68

Introduction to Proc Forecast to generate forecast for time series data

69

Autoregressive models and Stepwise Autoregression (STEPAR) procedure

70

Autoregressive and Moving Average models and Introduction to Box Jenkins Methodology

71

Introduction to Autoregressive Moving Average (ARMA) model

72

Autoregressive Integrated Moving Average (ARIMA) model

73

Building an ARIMA Model

74

Detection of Stationarity, Seasonality in ARIMA Model

75

Detecting the order of AR and MA of ARIMA model by Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)

76

Detecting the order by using AIC and BIC criterion

77

Estimation and forecast using Proc ARIMA in SAS

**Measures of Dispersion**

Dispersion is the measure of the extent to which the individual observation in a data set varies. It relates to those measures which capture the degree of heterogeneity of a set of statistical observation from a central value. Measuring heterogeneity involves construction of estimators, which pro-vide a standard or a representative value of the scatterings, as a function of all the sample observation. But, the heterogeneity of the data affects the efficiency of the estimator adversely, i.e. greater the dispersion in a data set lesser is the efficiency of the estimator. Therefore, to form an estimator of sufficient efficiency it is necessary to form an idea of the dispersion present in the data. The main classes of the measures of dispersion are:

*Absolute measure of Dispersion**Relative measure of Dispersion*

**Absolute Measure of Dispersion
**Absolute measures of dispersion refer to those measures of dispersion which depend on units of measurement. Hence, if the variability of two or more distributions with the same unit of measurement is to be compared then the absolute measures are helpful. The three main absolute measures of dispersion are:

*Range**Mean Deviation**Standard Deviation*

**Range
**The range of a set of statistical observations is defined as the highest and the lowest values in the set. This is the simplest method of measuring dispersion. Range is defined as: Range (X) = Xmax – Xmin where Xmax = Maximum value of the variable X, Xmin = Minimum value of the set X, X is a set containing observations x1, x2 …xn. Range can compare the variability of two or more distributions with the same units of measurement, but to compare the variability of the distribution given in the different units of measurement, the formula of range cannot be used.

**Mean Deviation
**Mean Deviation is defined as the arithmetic average of the deviations of various items from a measure of central tendency, may be mean, median or mode. Generally, mean deviation is calculated either from mean or median. Mean Deviation can also be calculated about any arbitrary average A.

**Standard Deviation
**Standard Deviation is considered to be an improvement over the mean deviation, since the former gets rid of signs, by taking instead of the absolute value of the deviation, the squares of the deviation of the variable about A. Standard Deviation is defined as: The positive square-root of the arithmetic mean of these quantities, i.e. it is the root-mean-squared deviation about A. The Standard Deviation is measured about the arithmetic mean of the data set since standard deviation is the least about mean. This is a striking feature of the measure of Standard deviation as a measure of dispersion.

**Relative Measure of Dispersion
**Relative Measures of Dispersion is defined as: Measures independent of the units of measurement and used for comparing dispersions of two or more distributions given in different units. Some of the most important measures of Relative dispersion are:

*Co-efficient of Range**Co-efficient of Variation**Co-efficient of Mean Deviation*

**Co-efficient of Range
**The compare the variability of a distribution with another, where the units of measurements are given in different units, it is not possible to use the absolute measure, range. The relative version of measuring the variability between the distributions is called the Coefficient of Range. Coefficient of Range is the ratio of difference between two extreme observations of their distribution to their sum.

**Co-efficient of Variation
**The Relative Measures of Dispersion based on Standard Deviation is called the Coefficient of Variation. This is a pure number independent of the units of measurement, and thus, it is suitable for comparing the variability, homogeneity or uniformity of two or more distributions. A distribution with smaller C.V. is said to be more homogeneous or less variable than the other, and a distribution with more C.V. is said to be more heterogeneous.

** Co-efficient of Mean Deviation
**The Coefficient of Mean Deviation is the relative measure associated with Mean-Deviation. It is de-fined as the ratio of the Mean Deviation and the Average about which it has been calculated.