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

__Histogram__

__Quantitative Data __

__ __

Quantitative data refers to the data comprising of numerical observations like Sales, profits etc. The main techniques of presenting quantitative data are:

- Histogram
- Scatter Plot

In this section we would learn in depth about histograms and then see how we can create histograms in SAS.

__What is a Histogram?__

__ __

A histogram is a graphical representation of the distribution of data, which is an estimate of the probability distribution of a continuous variable, usually in bar graph form, and was first introduced by Karl Pearson in 1891.

The first step in creating a histogram is to divide the entire value range into a series of intervals called “bins” and then to “drop” the individual values into the bins that they belong to. The width of the bin is determined by the range and may or may not be equal to the other bins. If the bins are of equal width, then the height or vertical axis of the bar determines the frequency of the occurrence for that set, but if the bins are not of equal width, then the area of the bar or rectangle represents the frequency of occurrence while the vertical axis represents the density. In both cases, all the bars in the histogram touch to indicate that the variable or data is continuous.

This can be used to visualize data or phenomena with both a contiguous factor and an occurrence factor. For example, a histogram can be used to visualize the commute time of people going to work with the horizontal axis representing time, so the bins are divided according to time, while the vertical axis represents the number of people that fall under that specific travel time.

A histogram is a display of statistical information that uses rectangles to show the frequency of data items in successive numerical intervals of equal size. In the most common form of histogram, the independent variable is plotted along the horizontal axis and the dependent variable is plotted along the vertical axis. The data appears as colored or shaded rectangles of variable area.

Applications of Histograms

**Identifying the most common process outcome:**By simply collecting all data related to the final state of the process and organizing it in a histogram, any special trends will quickly become apparent.**Identifying data symmetry:**A histogram can help us in realising that whether a particular variable is symmetric (normal) or not. In Analytics it’s very important that the variables are all normally distributed, otherwise, we can’t apply any analytical technique on them**Spotting deviations:**the histogram is easily the most useful tool for spotting oddities and identifying worrying trends. Keeping a list of histograms that have been produced in the course of your work and referring back to it can further make things easy to analyze, as you will additionally know when a deviation is potentially caused by old issues, or by a recent change in your operations.**Spotting areas that require little effort**: Last but definitely not least, a histogram can be helpful in determining when you’re wasting too much effort or resources on a specific task. Sometimes, a certain part of your process will not require as much attention as you think it does, and a histogram depicting the current resource allocation can immediately reveal that.

Let’s now turn our focus on how we can create histograms in SAS.

**PROC UNIVARIATE **DATA=mylib.CANDY_SALES_SUMMARY;

VAR SALE_AMOUNT;

HISTOGRAM SALE_AMOUNT;

**RUN; **

This is the representation of quantitative data. The univariate keyword is used to generate all the key descriptive statistics related to a particular variable. Here, the variable under consideration is sale_amount. The code to generate histogram is histogram. If no dimension is mentioned then, it is by default, a 2-dimensional diagram.

**PROC UNIVARIATE **DATA=mylib.CANDY_SALES_SUMMARY;

VAR SALE_AMOUNT;

HISTOGRAM SALE_AMOUNT;

CLASS SUBCATEGORY;

**RUN; **

The univariate option in the code generates all the descriptive statistics associated with the variable sale_amount in the data set candy_sales_summary. Another objective of the code is to construct a histogram for the same variable using the key-word histogram. The total amount of sales is generated for each of the subcategories, which is specified using the keyword class.

The diagram below would be the output of the above code.