Statistical Data Analysis with SPSS

Introduction 

IBM SPSS is one of the most popular tools used for statistical analysis of data in research units, administration and bussiness. 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. 

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 

1. 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 
2. Working with data and files  
   • Data Import 
   • Data types in SPSS 
   • Creating new variables 
   • Data sampling 
   • Exporting data to SPSS and other formats 
   • Exercise 
3. Manipulating data  
   • Sorting 
   • Transformations 
   • Filtering 
   • Conditional filtering 
   • Generating samples 
   • Missing observations 
   • Exercises 
4. 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 
5. Data transformations  
   • Standarization 
   • Normalization 
   • date type operations 
   • Exercises 
6. First steps in statistical analysis  
   • Outliers detection 
   • Distribution analysis – normality, skewness, kurtosis 
   • Exercises 
7. Dependency analysis between variables  
   • Scatterplot 
   • Pearson’s correlation coefficient 
   • Spearman’s correlation coefficient 
   • Kendall’s tau correlation coefficient 
   • Exercises 
8. Regression analysis – numerical variables  
   • Simple linear regression 
   • Multiple linear regression 
   • Polynomial regression 
   • Variable selection 
   • Binary variables 
   • Exercises 
9. Regression analysis – qualitative variables  
   • Logistic regression 
   • ROC curve 
   • Multinomial logistic regression 
   • Exercises 
10. Causality analysis  
    • Causality analysis with linear regression models 
    • Structural modelling with SPSS 
    • Causality analysis in structural model 
    • Exercises 
11. Principal components and factor analysis  
    • Principal components 
    • Exploratory factor analysis 
    • Confirmatory factor analysis 
    • Exercises 
12. Cluster analysis  
    • Hierarchical methods 
    • k-means methods 
    • Exercises 
13. Analysis of variance  
    • Single-factor analysis – ANOVA oraz ANCOVA 
    • Multi-factor analysis – MANOVA