LearnOpex

Information

• You have made a good decision and chosen the right software. Now learn how to maximize the value it provides to you and your organization. SPSS Training will help you accomplish this.

• The objective of this module is to impart the data analytical capabilities to the faculty,project leaders, business analysts,research scholars, and post graduate students, who want the enhance the quality of their work by using statistics .

• Our Training in SPSS offers you a wide range of options for learning how to optimize your use of SPSS software.

• With innovative courses, topics ranging from statistical analysis and survey research to data mining and predictive analytics, you will find courses that will help you provide greater value to your organization and further develop your own skills.

Course Content

Fundamentals of Statistics

• Hypothesis Testing	• The Comparison of Two Populations
• Analysis of Variance(ANOVA)	• Discriptive statistics
• Parametric & Non parametric tests, t test, F test, chi squae tests

Advanced Statistics

• GLM Multivariate	• Variance Components Analysis
• Linear Mixed Models	• Generalized Linear Models (GZLM)
• General Loglinear Analysis	• Logit Loglinear Analysis
• Survival analysis

Data Preparation, Data Mining

• Data View and variable view	• Missing Value
• Variable scale	• Variable Labels
• Select Cases & weight Cases	• Define variable properties
• Split file	• Identify duplicate & unusual cases

Forecasting

• Time series modeling and forecasting	• Bulk forecasting with expert modeler
• Bulk reforecasting by applying saved models	• Using the expert modeler to determine significant predictors
• Experimenting with predictors by applying saved models	• Seasonal Decomposition
• Spectral Plots

Cross Tabulation

• Display contingency tables	• Relationship between categorical variables
• Application of Statistical tests	• Application of Pivote table
• Monte Carlo and Exact method

Decision Tree

• Display contingency tables	• Relationship between categorical variables
• Application of Statistical tests	• Application of Pivote table
• Monte Carlo and Exact method

Conjoint Analysis

• Measures consumer preference about the attributes	• Full profile approach
• Orthogonal array and design	• Part worths & factor level
• Reversal & Simulation cases	• Maximum utility model
• BTL (Bradley-Terry-Luce) model	• Logit model

Cluster Analysis

• Identify groups of samples that behave similarly	• Number of cluster & clustering criterias
• Cluster Method	• Two step cluster analysis
• K-means cluster analysis	• Hierarchical cluster analysis (HCA)

Factor Analysis

• Used for data reduction or structure detection	• Data reduction remove highly correlated variables
• Structure detection examine the underlying relationships	• Extraction(eigenvalues) & Rotation in Factor analysis
• Factor score coefficient matrix	• Anti-image and KMO and Bartlett's test of sphericity.
• Kaiser-Meyer-Olkin Measure of Sampling Adequacy

Regression Analysis

• Modeling and analysis of numerical data consisting.	• Used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships.
• Linear regression	• Partial Correlations
• Bivariate correlations	• Ordinal regression
• Curve estimation	• Partial least square regression

Discriminant Analysis

• Used to model the value of a dependent categorical variable	• Active generator & active generator initialization
• Prior probabilities & covariance matrix	• Leave-one-out classification

Reliability Analysis

Neural Analysis

• Preferred tool for many predictive data mining applications	• Multilayer perception
• Radial basic function	• Network structure & network performance
• ROC cure : Sensitivity & Specificity	• Application : Detection of medical phenomena ,Stock market prediction, Credit assignment, Monitoring the condition of machinery, Engine management.