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31 - DAB- Data Analytics & Business Intelligience
DAB 103 - Statistical Techniques using SPSS: From Beginner to Advanced (10 Days)
| Code | Start Date | Duration | Venue | |
|---|---|---|---|---|
| DAB 103 | 21 October 2024 | 10 Days | Istanbul | Registration Form Link |
| DAB 103 | 25 November 2024 | 10 Days | Istanbul | Registration Form Link |
| DAB 103 | 30 December 2024 | 10 Days | Istanbul | Registration Form Link |
Course Description
This course will introduce trainees to the logic and use of statistical techniques in social sciences. The will be able to evaluate social or business research problems themselves and to have a capability of deciding on the true statistical alternative after handling the business problems. It presents the basics of the Statistical Package for the Social Sciences (SPSS). It introduces the SPSS Windows environment, discusses how to create a dataset, variable transformations, data manipulations, and descriptive statistics. It will assist trainees in developing the data analysis skills necessary for autonomous and efficient computer processing, manipulation, and analysis of empirical data in the study of social science data or business data.
Then the course advances to statistical techniques used for inferential approaches to the business and social problems and helps the trainee to improve new aspects for the solutions under the significancies provided by SPSS analysing features. The techniques are especially necessary for the experiments(events) that have two or more factors which impact one or more factors of the subject concerned. In real life, the problems have some factors that each has an effect on to the result, either positively or negatively concluded. However, generally their effect is considered as contributed way with all other factors. On the other hand to evalute such an impact onto the result is a complex problem for the managers or researchers. The program will give a remarkable perspective to the trainee to make confident predictions for the solutions about such multivarite problems by some compact practises focused on to the technique.
Course Objectives
- Building on a basic understanding of statistics
- Using an omnibus statistical package like SPSS for computation
- Acquainting trainees to some to the mechanics of utilizing this package
- Discussing, analyzing, and interpreting Multivariate procedures such as Multiple Regression, Discriminant Function Analysis, Logistical Regression, Cluster Analysis, Multidimensional Scaling, Loglinear Analysis, Survival Analysis, Complex Analysis of Variance Designs, Multivariate Analysis of Variance, and the Analysis of Simple Effects
Who Should Attend?
- Statistical Managers
- SAP project team
- Inventory team personnel
- anyone wants to learn about the Statistical practises and using statistical methods
Course Details/Schedule
Day 1
- Decision making as a business approach
- Decision Making Procedure
- Five basic steps
Day 2
- Talking about Statistics, remember some basics of Statistics
- Speaking about reseach phases, propose, analysis and reporting results
- Learning about practises on data using statistical methods
- Emphasizing on the importance of data
Day 3
- Bing familiar with the features of SPSS
- The SPSS Windows environment
- Handling any research problem using SPSS
Day 4
- Determining the experiment of problem
- Practicing with statistical analysis tool of SPSS
- Processing data of research and extracting conclusions for the problem
- Interpreting the conclusions in business approach and decision making
Day 5
- Deterministic decisions
- Probabilistic decisions
- Hypothesis testing in SPSS
Day 6
- Review of descriptive statistics and exploratory data analysis
- Introducing to inferential statistics
- Multivariate statistics and analysing techniques in SPSS
Day 7
- Reliability analysis
- One-way Anova
- Two-way Anova
Day 8
- Multiple comparisons in ANOVA
- MANOVA
- ANCOVA
Day 9
- Nonparametric statistics Mann-whitney U test Wilkcoxon signed-ranks test
- Nonparametric statistics Chi-square tests and median test
- Factor analysis
- Log-linear analysis
Day 10
- Multiple regression
- Binomial logistic regression
- Multinominal logistic regression
