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# Advanced Data Science Techniques in SPSS

سرفصل های دوره

Hone your SPSS skills to perfection - grasp the most high level data analysis methods available in the SPSS program.

1. Getting Started
• 1. Introduction

• 2. Advanced Linear Regression Techniques
• 1. Introduction to Stepwise Regression
• 2. Our Practical Example
• 3. Executing the Stepwise Regression Method
• 4. Interpreting the Results of the Stepwise Method
• 5. Executing the Forward Selection Regression
• 6. Interpreting the Results of the Forward Selection Method
• 7. Executing the Backward Selection Regression
• 8. Interpreting the Results of the Backward Selection Method
• 9. Comparing Nested Models Using the Remove Method
• 10. Executing the Regression Analysis with the Remove Method
• 11. Interpreting the Results of the Remove Method

• 3. Nonlinear Regression Analysis
• 1. Types of Nonlinear Functions
• 2. An Important Classification of the Nonlinear Relationships
• 3. Performing a Quadratic Regression in SPSS (1)
• 4. Performing a Quadratic Regression in SPSS (2)
• 5. Performing a Cubic Regression in SPSS (1)
• 6. Performing a Cubic Regression in SPSS (2)
• 7. Performing an Inverse Regression in SPSS (1)
• 8. Performing an Inverse Regression in SPSS (2)
• 9. Performing a Nonlinear Regression With an Exponential Relationship
• 10. Performing a Nonlinear Regression With a Logistic Relationship

• 4. K Nearest Neighbor in SPSS
• 1. Introduction to K Nearest Neighbor (KNN)
• 2. Selecting the Optimal Number of Neighbors
• 3. Our Practical Example
• 4. Performing the KNN technique
• 5. Interpreting the results of the KNN analysis
• 6. Finding the Optimal Number of Neighbors with Cross-Validation
• 7. Interpreting the Cross-Validation Results
• 8. Using the KNN Model for Future Predictions

• 5. Introduction to Decision Trees
• 1. What Are Decision Trees
• 2. Binary Trees (CART)
• 3. Non-Binary Trees (CHAID)

• 6. Growing Binary Trees (CART) in SPSS
• 1. Growing a Binary Regression Tree (CART)
• 2. Intepreting a Binary Regression Tree (1)
• 3. Intepreting a Binary Regression Tree (2)
• 4. Computing the R Squared
• 5. Growing a CART Regression Tree with Cross-Validation
• 6. Interpreting the Cross-Validation Results for a Regression Tree
• 7. Growing a CART Classification Tree in SPSS
• 8. Interpreting the CART Classification Tree
• 9. Growing a CART Classification Tree with Cross-Validation
• 10. Interpreting the Cross-Validation Results for a Classification Tree
• 11. Using Binary Trees for Future Predictions

• 7. Growing Non-Binary Trees (CHAID) in SPSS
• 1. Building a CHAID Regression Tree
• 2. Interpreting a CHAID Regression Tree
• 3. Growing a CHAID Regression Tree with Cross-Validation
• 4. Building a CHAID Classification Tree
• 5. Interpreting a CHAID Classification Tree
• 6. Growing a CHAID Classification Tree with Cross-Validation
• 7. Using Non-Binary Trees for Future Predictions

• 8. Introduction to Neural Networks
• 1. The Architecture of an Artificial Neural Network
• 2. What Happens Inside of a Neuron
• 3. Activation Functions
• 4. Neural Network Learning Process

• 9. Training a Multilayer Perceptron (MLP) in SPSS
• 1. Building a Multilayer Perceptron
• 2. Interpreting the Multilayer Perceptron
• 3. Interpreting the ROC Curve
• 4. Using the Multilayer Perceptron for Future Predictions

• 10. Training a Radial Basis Function (RBF) Neural Network in SPSS
• 1. Building an RBF Neural Network
• 2. Interpreting the RBF Network
• 3. Using the RBF Network for Future Predictions

• 11. Two-Step Cluster Analysis
• 1. What is Two-Step Clustering
• 2. Executing the Two-Step Cluster Analysis
• 3. Interpreting the Output of the Two-Step Cluster Analysis (1)
• 4. Interpreting the Output of the Two-Step Cluster Analysis (2)
• 5. Examining the Evaluation Variables
• 6. Using Your Clustering Model for Future Predictions

• 12. Survival Analysis
• 1. What Is the Survival Analysis
• 2. Introduction to the Kaplan-Meier Method
• 3. Introduction to the Cox Regression
• 4. Our Practical Example
• 5. Executing the Kaplan-Meier Procedure
• 6. Interpreting the Results of the Kaplan-Meier Method (1)
• 7. Interpreting the Results of the Kaplan-Meier Method (2)
• 8. Executing the Cox Regression
• 9. Interpreting the Cox Regression

• 13. Practical Exercises
• 1.1 Practice 01 linear regression.pdf
• 1. Practical Exercises for the Linear Regression.html
• 2.1 Practice 02 nonlinear regression.pdf
• 2. Practical Exercises for the Nonlinear Regression.html
• 3.1 Practice 03 KNN.pdf
• 3. Practical Exercises for the KNN Method.html
• 4.1 Practice 04 regression trees.pdf
• 4. Practical Exercises for the Regression Trees.html
• 5.1 Practice 05 classification trees.pdf
• 5. Practical Exercises for the Classification Trees.html
• 6.1 Practice 06 neural networks.pdf
• 6. Practical Exercises for the Neural Networks.html
• 7.1 Practice 07 cluster.pdf
• 7. Practical Exercises for the Cluster Analysis.html
• 8.1 Practice 08 survival.pdf
• 8. Practical Exercises for the Survival Analysis.html

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تولید کننده:
شناسه: 20353
حجم: 910 مگابایت
مدت زمان: 402 دقیقه
تاریخ انتشار: 15 مهر 1402 دسته بندی محصول

45,900 تومان