13/01/2026
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ูููุฑุณ ุงููุชูุญูููููู ุงูุงุญูุตูุงุฆู SPSS ุงูุดุงู
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ู ุงูุงุณุงุณูุงุช ุญุชู ุงูุงุญูุชูุฑุงู
SPSS Statistical Analysis Course | From Zero To Pro
Module 1: Getting Started with SPSS :-
โ Overview of the IBM SPSS environment
โ Installation, versions, and licensing explained
โ Supported data formats and file types
โ Data View, Variable View, and Output Viewer navigation
โ Importing data from Excel, CSV, and databases
โ Customizing preferences, layouts, and saving projects
Module 2: Data Entry, Cleaning & Preparation :-
โ Defining variables: types, labels, values, missing data
โ Manual and automated data entry methods
โ Handling missing values and outliers professionally
โ Recoding variables and computing new fields
โ Categorizing continuous variables
โ Merging datasets and splitting files
โ Data validation and quality assurance checks
Module 3: Descriptive Statistics & Visualization :-
โ Frequency tables and cross-tabulations
โ Measures of central tendency: mean, median, mode
โ Measures of dispersion: SD, variance, range, IQR
โ Z-scores and standardization
โ Bar charts, histograms, pie charts, box plots
โ Using Explore and Descriptives for deeper insights
Module 4: Inferential Statistics & Hypothesis Testing :-
โ Understanding p-values, significance levels, confidence intervals
โ One-sample, independent, and paired t-tests
โ One-way ANOVA with post-hoc tests (Tukey, L*D)
โ Two-way ANOVA and interaction effects
โ Pearson and Spearman correlation tests
โ Scatterplots and relationship interpretation
โ Normality tests: Shapiro-Wilk, Kolmogorov-Smirnov
โ Homogeneity testing using Leveneโs test
Module 5: Advanced Statistical Modeling :-
โ Simple linear regression and prediction
โ Interpreting coefficients, Rยฒ, adjusted Rยฒ
โ Multiple linear regression with multiple predictors
โ Multicollinearity diagnostics (VIF, tolerance)
โ Model selection methods (Enter, Stepwise, Forward, Backward)
โ Binary logistic regression (Yes/No outcomes)
โ Odds ratios and model fit (Nagelkerke Rยฒ, Hosmer-Lemeshow)
โ Optional: Ordinal & Multinomial logistic regression
Module 6: Machine Learning in SPSS :-
โ Decision Trees (C&RT, CHAID)
โ Classification rules, pruning, and validation
โ Neural Networks (MLP): structure and interpretation
โ Applications in classification and regression
โ Cluster analysis: K-means and hierarchical clustering
โ Determining optimal cluster numbers
โ Interpreting dendrograms and profiling clusters
Module 7: Data Visualization & Reporting :-
โ Advanced visualizations and 3D charts
โ Heatmaps and analytical graphics
โ Building dynamic dashboards
โ Customizing colors, labels, legends, and annotations
โ Exporting results to Word, PowerPoint, and PDF
โ Automated reporting and reusable templates
โ 15 J.D
โ Free delivery across Jordan
โ 079 208 5362
โ 077 963 7989