كـورس الـتـحـلـيـل الاحـصـائي SPSS الشامل الاحترافي من الاساسيات حتى الاحـتـرافS

كـورس الـتـحـلـيـل الاحـصـائي SPSS الشامل الاحترافي من الاساسيات حتى الاحـتـراف SPSS Statistical Analysis Course | From Zero To Pro Module 1: Getting Started with SPSS :- — Overview of the IBM SPSS environment — Installation, versions, and licensing explained — Supported data formats and file types — Data View, Variable View, and Output Viewer navigation — Importing data from Excel, CSV, and databases — Customizing preferences, layouts, and saving projects Module 2: Data Entry, Cleaning & Preparation :- — Defining variables: types, labels, values, missing data — Manual and automated data entry methods — Handling missing values and outliers professionally — Recoding variables and computing new fields — Categorizing continuous variables — Merging datasets and splitting files — Data validation and quality assurance checks Module 3: Descriptive Statistics & Visualization :- — Frequency tables and cross-tabulations — Measures of central tendency: mean, median, mode — Measures of dispersion: SD, variance, range, IQR — Z-scores and standardization — Bar charts, histograms, pie charts, box plots — Using Explore and Descriptives for deeper insights Module 4: Inferential Statistics & Hypothesis Testing :- — Understanding p-values, significance levels, confidence intervals — One-sample, independent, and paired t-tests — One-way ANOVA with post-hoc tests (Tukey, LSD) — Two-way ANOVA and interaction effects — Pearson and Spearman correlation tests — Scatterplots and relationship interpretation — Normality tests: Shapiro-Wilk, Kolmogorov-Smirnov — Homogeneity testing using Levene’s test Module 5: Advanced Statistical Modeling :- — Simple linear regression and prediction — Interpreting coefficients, R², adjusted R² — Multiple linear regression with multiple predictors — Multicollinearity diagnostics (VIF, tolerance) — Model selection methods (Enter, Stepwise, Forward, Backward) — Binary logistic regression (Yes/No outcomes) — Odds ratios and model fit (Nagelkerke R², Hosmer-Lemeshow) — Optional: Ordinal & Multinomial logistic regression Module 6: Machine Learning in SPSS :- — Decision Trees (C&RT, CHAID) — Classification rules, pruning, and validation — Neural Networks (MLP): structure and interpretation — Applications in classification and regression — Cluster analysis: K-means and hierarchical clustering — Determining optimal cluster numbers — Interpreting dendrograms and profiling clusters Module 7: Data Visualization & Reporting :- — Advanced visualizations and 3D charts — Heatmaps and analytical graphics — Building dynamic dashboards — Customizing colors, labels, legends, and annotations — Exporting results to Word, PowerPoint, and PDF — Automated reporting and reusable templates — 15 J.D — Free delivery across Jordan — 079 208 5362 — 077 963 7989 #SPSS #DataAnalysis #Statistics #DataScience #MachineLearning #UniversityStudents #GraduationProject #ResearchMethods #RegressionAnalysis #ANOVA #TTest #LogisticRegression #SelfLearning #StudentLife #AcademicSuccess #JordanStudents #Analytics #CareerSkills #LearnSPSS