Predictive Modeling and Regression Analysis using SPSS
4.5 (12 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,001 students enrolled

Predictive Modeling and Regression Analysis using SPSS

Master Logistic Regression, Linear, Multinomial and Multiple Regression Modeling, Correlation Techniques using SPSS
4.5 (12 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,001 students enrolled
Last updated 12/2018
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This course includes
  • 12.5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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, Paint
Desired skillsets — Understanding of Data Analysis and VBA toolpack in MS Excel will be useful

Who this course is for:
  • Students
  • Quantitative and Predictive Modellers and Professionals
  • CFA’s and Equity Research professionals
  • Pharma and research scientists
Course content
Expand all 84 lectures 12:19:44
+ Importing Dataset
9 lectures 01:09:35
Importing Datasets xlsx, xls Formats
05:49
Importing Datasets xlsx, xls Formats Continue
05:32
Software Menus
04:52
Understanding Mean Standard Deviation
10:57
Other Concepts of Understanding Mean SD
10:45
Implementation Using SPSS
11:57
Implementation using SPSS Continues
08:05
+ Correlation Techniques
12 lectures 01:51:22
Basic Correlation Theory
10:35
Interpretation
10:51
Implementation
10:42
Data Editor
09:19
Simple Scatter Plot
05:34
Heart Pulse
09:48
Statistics Viewer
10:30
Heart Pulse (Before and After RUN)
09:48
Interpretation and Implementation on Datasets Example 1
05:50
Interpretation and Implementation on Datasets Example 2
10:12
Interpretation and Implementation on Datasets Example 3
09:31
Interpretation and Implementation on Datasets Example 4
08:42
+ Linear Regression Modeling
21 lectures 03:08:19
Introduction to Linear Regression Modeling Using SPSS
07:35
Linear Regression
08:19
Stock Return
09:31
T-Value
08:57
Scatter Plot Rril v/s Rbse
10:10
Create Attributes for Variables
10:19
Scatter Plot – Rify v/s Rbse
04:40
Regression Equation
09:17
Interpretation
09:32
Copper Expansion
11:57
Copper Expansion Example
08:45
Copper Expansion Example Continue
10:06
Energy Consumption
11:38
Observations
07:53
Energy Consumption Example
04:49
Debt Assessment
10:46
Debt Assessment Continue
08:16
Debt to Income Ratio
11:48
Credit Card Debt
10:58
Basic Multiple regression Theory
06:08
Basic Multiple regression Theory Continue
06:55
+ Multiple Regression Modeling
14 lectures 02:07:50
Multiple Regression Example Part 1
09:32
Multiple Regression Example Part 2
10:27
Multiple Regression Example Part 3
11:15
Multiple Regression Example Part 4
09:06
Multiple Regression Example Part 5
08:17
Multiple Regression Example Part 6
08:13
Multiple Regression Example Part 7
08:39
Multiple Regression Example Part 8
10:01
Multiple Regression Example Part 9
08:04
Multiple Regression Example Part 10
07:16
Multiple Regression Example Part 11
11:24
Multiple Regression Example Part 12
08:18
Multiple Regression Example Part 13
08:46
Multiple Regression Example Part 14
08:32
+ Logistic Regression
14 lectures 02:01:38
Understanding Logistic Regression Concepts
07:47
Working on IBM SPSS Statistics Data Editor
09:23
SPSS Statistics Data Editor Continues
09:07
IBM SPSS Viewer
07:11
Variable in the Equation
08:24
Implementation Using MS Excel
07:40
Smoke Preferences
07:22
Heart Pulse Study
10:34
Heart Pulse Study Continues
07:20
Variables in the Equation
08:56
Smoking Gender Equation
11:28
Generating Output and Observations
08:16
Generating Output and Observations Continues
06:00
Interpretation of Output Example
12:10
+ Multinomial Regression
14 lectures 02:01:00
Introduction to Multinomial-Polynomial Regression
09:08
Example 1 Health Study of Marathoners
06:33
Note
07:10
Case Processing Summary
11:05
Model Fitting Information
10:23
Asymptotic Correlation Matrix
12:57
Understanding Dataset
06:15
Generating Output
07:18
Parameters Estimates
10:25
Asymptotic Correlations Metrics
11:35
Interpretation of Output
05:42
Interpretation of Output Continues
07:20
Interpretation of Estimates
08:05
Understand Interpretation
07:04