Section 1
##### Introducing IBM SPSS Statistics

Section 2
##### Familiarization with the SPSS Data Editor

Section 3
##### Summarizing Individual Variables

Section 4
##### Modifying Data Values

Section 5
##### Graphical Presentation of Data

Section 6
##### Understanding Output in the Viewer

Section 7
##### Helpful Data Management Features

Section 8
##### Statistical Analysis and Tests on SPSS

29

Generating Descriptive Statistics: Frequencies Module

30

Descriptive Module, Explore

31

Checking the distribution: P-P Plots, Q-Q Plots

32

Chi Square test of Independence: Understanding the association between two categorical variables

33

Independent Samples T test: Comparing the means along with variance comparison

34

Paired Sample T Test: Comparing the means of same subject

35

One way ANOVA: Comparing means for more than two groups

36

Two Way ANOVA: Type I and III Sum of Squares, Post Hoc Tests

Section 9
##### Introduction to Regression Analysis

37

Doing Simple Regression on SPSS: Understanding the unilinear causality

38

Interpreting a Simple Regression on SPSS w.r.t. the assumptions of the Regression

39

Example of a Multiple Regression Model

40

Problem of Multicollinearity

41

Methods of Regression: Forced, Forward, Backward, Stepwise

42

Assessing the Regression Model: Goodness of Fit R Square, Adjusted R Square

43

Multiple Regression using SPSS

44

Interpreting Multiple Regression: Parameter Estimate Table

45

Variance Inflation Factor, Collinearity Diagnostics

Section 10
##### Introduction to Logistic Regression

46

Problems related to Logistic regression

47

Nature of Logistic Model: Odds, Odds ratio, Logit Link

48

Concept of Maximum Likelihood Estimation

49

Assessing the Model: The Log-Likelihood Statistic, Cox and Snell’s R Square and Negelkerke R Square

50

Hosmer Lemeshow Goodness of fit test

51

Assessing the Contribution of Predictors: The Wald Statistic

52

Methods of Logistic Regression: Forced Entry Method, Forward LR

53

Classification table: Overall Accuracy

54

Interpreting Logistic Regression

Section 11
##### Introduction to CHAID Decision Tree

Section 12
##### Bivariate Plots and Correlation for Scale Variables

Let’s say you have a worksheet with thousands of rows of data. It would be extremely difficult to see patterns and trends just from examining the raw information. Similar to charts, conditional formatting provides another way to visualize data and make worksheets easier to understand.

Thus, Conditional Formatting is a feature of Excel which allows you to apply a format to a cell or a range of cells based on certain criteria. Conditional Formatting are of the following types:

- Highlight Cell Rules
- Top/Bottom Rules
- Data Bars
- Color Scales
- Icon Sets

**To create a conditional formatting rule
**Say, we have a worksheet containing sales data, and we’d like to see which salespeople are meeting their monthly sales goals. The sales goal is $4000 per month, so we’ll create a conditional formatting rule for any cells containing a value higher than 4000.

- Select the desired cells for the conditional formatting rule.
- From the Hometab, click the Conditional Formatting A drop-down menu will appear.
- Hover the mouse over the desired conditional formatting type, then select the desired rule from the menu that appears. In our example, we want to highlight cells that are greater than $4000.
- A dialog box will appear. Enter then desired value(s) into the blank field. In our example, we’ll enter 4000 as our value.
- Select a formatting style from the drop-down menu. In our example, we’ll choose Green Fill with Dark Green Text, then click OK.
- The conditional formatting will be applied to the selected cells. In our example, it’s easy to see which salespeople reached the $4000 sales goal for each month.

**Conditional formatting presets**

Excel has several predefined styles—or presets—you can use to quickly apply conditional formatting to your data. They are grouped into three categories:

- Data Bars are horizontal bars added to each cell, much like a bar graph.
- Color Scales change the color of each cell based on its value. Each color scale uses a two- or three-color gradient. For example, in the Green-Yellow-Red color scale, the highest values are green, the average values are yellow, and the lowest values are red.

- Icon Sets add a specific icon to each cell based on its value.

**To use preset conditional formatting**

- Select the desired cellsfor the conditional formatting rule.
- Click the Conditional Formatting A drop-down menu will appear.
- Hover the mouse over thedesired preset, then choose a preset style from the menu that appears.
- The conditional formatting will be applied to the selected cells.