Statistical Analysis With SPSS

Why Attend

IBM SPSS is one of the most popular tools used for the statistical analysis of data in research units, administration and business. The training consists of the most frequently used elements of analysis of data, which may be modified and suited for a target group. The training starts with an introduction part during which statistical concepts are being discussed then, in the practical part the examples of using techniques of analysis of data in SPSS are presented.

Event Date: 14/08/2025 – 16/08/2025

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More Dates

10/04/2025 – 12/04/2025	Price: ₦230,000.0000 VAT
08/05/2025 – 10/05/2025	Price: ₦230,000.0000 VAT

Overview
Course Outline

Who is this training for?

Due to the wide range of IBM SPSS users every training is prepared exclusively for a client. Owing to this, you can be sure that after finishing the training you will be able to start working with data in SPSS. This training will meet your expectations even if you have no experience.

What will I learn?

Use IBM SPSS for statistical data analysis
Apply appropriate statistical data analysis techniques to given business problem
Import, filter and manipulate data in SPSS
Interpret results of statistical test and models
Apply linear regression approach to forecast and analyse relationship between variables
Get actionable insight from statistical models

Course outline

Getting started with IBM SPSS
- Introduction to IBM SPSS
- Overview of statistical methods and options
- Data Window
- Output Viewer
- Plots window
- Getting help and documentation
- Exercises
Working with data and files
- Data Import
- Data types in SPSS
- Creating new variables
- Data sampling
- Exporting data to SPSS and other formats
- Exercise
Manipulating data
- Sorting
- Transformations
- Filtering
- Conditional filtering
- Generating samples
- Missing observations
- Exercises
Exploratory data analysis
- Data Visualization
  - Qualitative data – frequency plot, bar plot
  - Quantitative data – histogram, box & whisker plot
- Central tendency
  - Mode
  - Median, Quartile, Percentile
  - Mean
- Variability measures
  - Variance, Standard deviation
  - Range
  - Interquartile range
  - Coefficient of variability
- Exercises
Data transformations
- Standardization
- Normalization
- date type operations
- Exercises
First steps in statistical analysis
- Outliers detection
- Distribution analysis – normality, skewness, kurtosis
- Exercises
Dependency analysis between variables
- Scatterplot
- Pearson’s correlation coefficient
- Spearman’s correlation coefficient
- Kendall’s tau correlation coefficient
- Exercises
Regression analysis – numerical variables
- Simple linear regression
- Multiple linear regression
- Polynomial regression
- Variable selection
- Binary variables
- Exercises
Regression analysis – qualitative variables
- Logistic regression
- ROC curve
- Multinomial logistic regression
- Exercises
Causality analysis
- Causality analysis with linear regression models
- Structural modelling with SPSS
- Causality analysis in structural model
- Exercises
Principal components and factor analysis
- Principal components
- Exploratory factor analysis
- Confirmatory factor analysis
- Exercises
Cluster analysis
- Hierarchical methods
- k-means methods
- Exercises
Analysis of variance
- Single-factor analysis – ANOVA oraz ANCOVA
- Multi-factor analysis – MANOVA
- Exercises