About Workshop
Course Overview
SPSS and Microsoft Excel are the most commonly used applications in today’s modern business organizations to cater their data analysis and reporting needs. SPSS is a strong application package that provides excellent features to manage large stock of data and generating excellent classified reports instantly. It also provides wide range graphical and statistical analysis tools that meet demand for both business users and researchers. Microsoft Excel, undoubtedly world’s most powerful spreadsheet application, can be used in association with SPSS to enhance organizational productivity and reporting quality.
This course will provide the participants an experience of different data management and statistical analysis tools in SPSS. The course will also enable the participants to use Microsoft Excel to increase the SPSS advantage.
Delivery Method
Linear Demonstration - 50%,
Case and Practice Exercise - 50%
Workshop topics to be covered:
SESSION 1 - OUTLINE
Introduction
Assessing Current Skill Level of Participants
Establishing Objectives & Scopes
Introduction to Data Management & Statistical Analysis
Understanding Data Need for Organisations
Scope of Statistical Analysis
Data Management and Statistical Analysis – Practice Areas
Introduction to Sample Cases
Introduction to SPSS Windows Process
Data Window and Other Commonly Used Windows
The Output Window
Printing Outputs
Understanding Data & Variable View
Variable Characteristics
Entering & Editing Data
Using Microsoft Excel for Data Entry
Importing External Data
Excel, Text and Dbase Files
Using Excel for Data Entry
Exercises
Introduction to Managing Data
Listing Cases and Replacing Missing Values
Recoding Variables and Selecting Cases
Sorting Cases and Merging Files
SESSION 2 – OUTLINE
Understanding Crosstabulation
Counts vs. Percentage
Significance Testing For Cross Tabulations
Adding layer variable
Exercises
Modifying Data Value
Creating Categorical Variable from a Scale Variable
Computing New Variables
Working with Dates and Time
Exercises
Creating and Editing Charts
Chart Creation Basics
Chart Editing Basics
Examples
SESSION 3 – OUTLINE
Working with Outputs
Using the Viewer
Using Pivot Table Editor
Accessing Output Definitions
Pivoting Tables
Creating & Displaying Layers
Editing Tables
Hiding Rows & Columns
Changing Data Display Formats
Table Looks
Using Results in Other Applications
Graphical Representation of Data
Frequencies
Bar Charts
Histograms
Percentiles
Exercises
SESSION 4 - OUTLINE
Statistical Concept Development and Critical Analysis
Central Tendency and its Measures;
Population Mean and Sample Mean;
Measures of Dispersion and its Importance;
Variance and Standard Deviation;
Interpretation and Uses of the Standard Deviation;
Relative Dispersion;
Skewness;
Quartiles and Boxplots;
Normal Probability Distribution;
Sampling Method and Sampling Error;
Standard Error of the Mean;
Estimation and Confidence Interval;
One and two-sample test of hypothesis;
Analysis of Variance;
Linear Regression and Correlation;
Multiple Regression and Correlation Analysis;
Statistical Quality Control;
Time Series Forecasting
SESSION 5 - OUTLINE
Simple Linear Regression
Predicted Values
The Amount of Variance Explained
Curvilinear Relationships
Output: Linear Regression
Output: Curvilinear Regression
Case Study
Multiple Regression Analysis
The Regression Equation
The Amount of Variance Explained
Methods for Variable Selection
Output
Changes as each New Variable is Added
Definition of Terms
Case Study
SESSION 6 – OUTLINE
Time Series Forecasting
Least Square Method
Seasonal Variation
Case Study
Statistical Quality Control
Diagnostic Charts
Pareto and Fishbone Chart
Purpose and types of Quality Control Charts
Attribute Control Charts
Exercises
Case Study
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