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)

4. Advantages and Disadvantages of Decision Trees

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

14. Download Your Resources Here

1. Download Links.html

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