Predictive Modeling And Regression Analysis Using Spss

Master Logistic Regression, Linear, Multinomial and Multiple Regression Modeling, Correlation Techniques using SPSS

What you'll learn
The course works across multiple software packages such as SPSS, MS Office, PDF writers, and Paint.
This course is to specifically learn about Descriptive Statistics, Means, Standard Deviation and T-test Understanding Means, Standard Deviation, Skewness, Kurtosis and T-test concepts
Learn Importing Dataset and Correlation Techniques
Learn Linear Regression Modeling
Learn Multiple Regression Modeling
Learn Logistic Regression
Learn Multinomial Regression
Requirements
Prior knowledge of Quantitative Methods, MS Office and Paint is desired
Description
Predictive modeling course aims to provide and enhance predictive modeling skills across business sectors/domains. Quantitative methods and predictive modeling concepts could be extensively used in understanding the current customer behavior, financial markets movements, and studying tests and effects in medicine and in pharma sectors after drugs are administered. The course picks theoretical and practical datasets for predictive analysis. Implementations are done using SPSS software. Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the training. The course also emphasizes on the higher order regression models such as quadratic and polynomial regressions which aren’t covered in other online courses.Essential skillsets – Prior knowledge of Quantitative methods and MS Office, PaintDesired skillsets — Understanding of Data Analysis and VBA toolpack in MS Excel will be useful

Overview

Section 1: Importing Dataset

Lecture 1 Importing Datasets in Text and CSV

Lecture 2 Importing Datasets xlsx, xls Formats

Lecture 3 Importing Datasets xlsx, xls Formats Continue

Lecture 4 Understanding User Operating Concepts

Lecture 5 Software Menus

Lecture 6 Understanding Mean Standard Deviation

Lecture 7 Other Concepts of Understanding Mean SD

Lecture 8 Implementation Using SPSS

Lecture 9 Implementation using SPSS Continues

Section 2: Correlation Techniques

Lecture 10 Basic Correlation Theory

Lecture 11 Interpretation

Lecture 12 Implementation

Lecture 13 Data Editor

Lecture 14 Simple Scatter Plot

Lecture 15 Heart Pulse

Lecture 16 Statistics Viewer

Lecture 17 Heart Pulse (Before and After RUN)

Lecture 18 Interpretation and Implementation on Datasets Example 1

Lecture 19 Interpretation and Implementation on Datasets Example 2

Lecture 20 Interpretation and Implementation on Datasets Example 3

Lecture 21 Interpretation and Implementation on Datasets Example 4

Section 3: Linear Regression Modeling

Lecture 22 Introduction to Linear Regression Modeling Using SPSS

Lecture 23 Linear Regression

Lecture 24 Stock Return

Lecture 25 T-Value

Lecture 26 Scatter Plot Rril v/s Rbse

Lecture 27 Create Attributes for Variables

Lecture 28 Scatter Plot – Rify v/s Rbse

Lecture 29 Regression Equation

Lecture 30 Interpretation

Lecture 31 Copper Expansion

Lecture 32 Copper Expansion Example

Lecture 33 Copper Expansion Example Continue

Lecture 34 Energy Consumption

Lecture 35 Observations

Lecture 36 Energy Consumption Example

Lecture 37 Debt Assessment

Lecture 38 Debt Assessment Continue

Lecture 39 Debt to Income Ratio

Lecture 40 Credit Card Debt

Lecture 41 Basic Multiple regression Theory

Lecture 42 Basic Multiple regression Theory Continue

Section 4: Multiple Regression Modeling

Lecture 43 Multiple Regression Example Part 1

Lecture 44 Multiple Regression Example Part 2

Lecture 45 Multiple Regression Example Part 3

Lecture 46 Multiple Regression Example Part 4

Lecture 47 Multiple Regression Example Part 5

Lecture 48 Multiple Regression Example Part 6

Lecture 49 Multiple Regression Example Part 7

Lecture 50 Multiple Regression Example Part 8

Lecture 51 Multiple Regression Example Part 9

Lecture 52 Multiple Regression Example Part 10

Lecture 53 Multiple Regression Example Part 11

Lecture 54 Multiple Regression Example Part 12

Lecture 55 Multiple Regression Example Part 13

Lecture 56 Multiple Regression Example Part 14

Section 5: Logistic Regression

Lecture 57 Understanding Logistic Regression Concepts

Lecture 58 Working on IBM SPSS Statistics Data Editor

Lecture 59 SPSS Statistics Data Editor Continues

Lecture 60 IBM SPSS Viewer

Lecture 61 Variable in the Equation

Lecture 62 Implementation Using MS Excel

Lecture 63 Smoke Preferences

Lecture 64 Heart Pulse Study

Lecture 65 Heart Pulse Study Continues

Lecture 66 Variables in the Equation

Lecture 67 Smoking Gender Equation

Lecture 68 Generating Output and Observations

Lecture 69 Generating Output and Observations Continues

Lecture 70 Interpretation of Output Example

Section 6: Multinomial Regression

Lecture 71 Introduction to Multinomial-Polynomial Regression

Lecture 72 Example 1 Health Study of Marathoners

Lecture 73 Note

Lecture 74 Case Processing Summary

Lecture 75 Model Fitting Information

Lecture 76 Asymptotic Correlation Matrix

Lecture 77 Understanding Dataset

Lecture 78 Generating Output

Lecture 79 Parameters Estimates

Lecture 80 Asymptotic Correlations Metrics

Lecture 81 Interpretation of Output

Lecture 82 Interpretation of Output Continues

Lecture 83 Interpretation of Estimates

Lecture 84 Understand Interpretation

Students,Quantitative and Predictive Modellers and Professionals,CFA’s and Equity Research professionals,Pharma and research scientists