Multiple Regression Modeling using SPSS:

Predictive modelling course aims to provide and enhance predictive modelling skills across business sectors/domains. Quantitative methods and predictive modelling 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 . Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the . The course also emphasizes on the higher order regression models such as quadratic and polynomial regressions which aren’t covered in other

 Essential skillsets – Prior knowledge of Quantitative methods and MS Office, PaintØ
 Desired skillsets — Understanding of Data Analysis and VBA toolpack in MS Excel will be usefulØ

The course works across multiple packages such as SPSS, MS Office, PDF writers, and Paint.
Regression modelling forms the core of Predictive modelling course. The core objective of this course is to provide skills in understand the regression model and interpreting it for predictions. The associated parameters of the regression model will be interpreted and tested for significance and test the goodness of fit of the given regression model.

Through this course we are going to understand

• Interpretation of regression attributes such as R-Squared (correlation coefficient), t and p values
• m (slope) and c (intercept),
• Dependent (Y) variables and independent (X1, X2, X3……) variables
• Examining significance/relevance of X-variables in regression model (equations) for goodness of fit of regression model
• Predicting Y-variable upon varying values of X-variables
• Understanding Multi-Collinearity and its disadvantages
• Implementation on sample datasets using SPSS and output simulation in MS Excel

Target Customers:

This course is not focused on specific set of sectors and domains because it can used by professionals across sectors. However, the list of professionals bulleted below should be able to make the best use of it

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

Pre-Requisites:

Detailed in course description below, Prior knowledge of Quantitative Methods, MS Office and Paint is desired.

For any query call/miss call-

Mob-+918587999769

Section 1: Introduction

1                   Introduction to Basic Multiple Regression

2                   Important Output Variables

Section 2 Example

3                   Multiple Regression Example Part 1

4                   Multiple Regression Example Part 2

5                  Multiple Regression Example Part 3

6                   Multiple Regression Example Part 4

7                   Multiple Regression Example Part 5

8              Multiple Regression Example Part 6

9              Multiple Regression Example Part 7

10                Multiple Regression Example Part 8

11                Multiple Regression Example Part 9

12               Multiple Regression Example Part 10

13                Multiple Regression Example Part 11

14                Multiple Regression Example Part 12

15           Multiple Regression Example Part 13

16           Multiple Regression Example Part 14

Here is a sample for the course completion certificate which you will receive after complete the course. This certificate is widely accepted across industries and will boost your chances to grab the job opportunities.

Mail us at: [email protected] with below details to receive your certificate:

• Name
• Course Name
• Date of Completion
• Address

Course Certificate:-