SPSS is a powerful tool that is capable of conducting just about any type of data analysis used in the social sciences, the natural sciences, or in the business world. While mathematics is generally thought to be the language of science, data analysis is the language of research. Research in many fields is crucial for human progress, as long as there is research, there will be the need to analyze data.
SPSS is a rigorous statistical package popular worldwide specifically for statistical data analysis. This course designed to introduce the participants with essential statistical techniques and way of applying those techniques in SPSS environment.
At the end of the course participants will grow and understanding and expertise on—
• Various statistical techniques that are required for conducting research works;
• Creating and editing data file in SPSS;
• Managing data file;
• Using graphs and charts;
• Generating auto report with data summary and graphical representations;
• Advanced statistical analysis such as ANOVA, Linear Regression and Multiple regression analysis.
The programme will be delivered using formal lectures combined with practical and interactive case studies and exercises. There will be a great emphasis on gaining practical experiences.
Contents of Training:
1.1 Scope and usage of statistical analysis in business, economics and social sciences;
1.2 Introduction to Business Cases and SPSS environment.
2.1 Creating and editing data files;
2.2 Managing data files;
2.3 Creating and editing graphs and charts;
2.4 Generating auto reports and exporting to word and acrobat;
2.5 Importing data from Excel.
2.6 Case Study: Importing an Excel database, editing as required and creating auto report in document format.
3.1 Frequencies: Frequencies, bar charts, histograms, percentiles.
3.2 Descriptive Statistics: Measures of central tendency, variability, deviation from normality, size and stability;
3.3 Case Study 1- Constructing frequency distribution and descriptive statistical analysis on consumer income for Automobile industry.
4.1 Crosstabulation and Chi-Square Analysis
4.2 Correlation Matrix
4.3 Analysis of Variance
4.4 Case Study: Application of Correlation Matrix in Stock Portfolio Analysis.
5.1 Linear Regression and Correlation
5.2 Case Study: Application of linear regression in cost management.
6.1 Multiple Regression and Correlations
6.2 Case Study: Application of multiple regression analysis in consumer behaviour research.
In this session participants will be provided with a real life research problem which will require knowledge of the techniques learnt in earlier sessions. This session is designed with objective of enabling the participants to understand the big picture while conducting a research.
8.1 Review of Session 1-6
8.2 Assessment Test