It 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
The course picks theoretical and practical datasets for predictive analysis
Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the training
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 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 arenβt covered in other online courses
ο Essential skillsets β Prior knowledge of Quantitative methods and MS Office, Paint
ο Desired skillsets — Understanding of Data Analysis and VBA toolpack in MS Excel will be useful
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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
β’ Interpretation of descriptive statistics and t- values
β’ Implementation on example/sample datasets using SPSS
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
- CFAβs and Equity Research professionals
- Pharma and research scientists
Importing Dataset
Importing Datasets in Text and CSV
Importing Datasets xlsx and xls Formats
Importing Datasets xlsx and xls Formats Continue
Understanding User Operating Concepts
Software Menus
Understanding Mean Standard Deviation
Other Concepts of Understanding Mean SD
Implementation Using SPSS
Implementation using SPSS Continues
Correlation Techniques
Basic Correlation Theory
Implementation
Data Editor
Simple Scatter Plot
Heart Pulse
Statistics Viewer
Heart Pulse (Before and After RUN)
Interpretation and Implementation on Datasets Example 1
Interpretation and Implementation on Datasets Example 2
Interpretation and Implementation on Datasets Example 3
Interpretation and Implementation on Datasets Example 4
Linear Regression Modeling
Introduction to Linear Regression Modeling Using SPSS
Linear Regression
Stock Return
T-Value
Scatter Plot Rril vs Rbse
Create Attributes for Variables
Scatter Plot Rify vs Rbse
Regression Equation
Interpretation
Copper Expansion
Copper Expansion Example
Copper Expansion Example Continue
Energy Consumption
Observations
Energy Consumption Example
Debt Assessment
Debt Assessment Continue
Debt to Income Ratio
Credit Card Debt
Predicted values Using MS Excel
Predicted values Using MS Excel Continue
Multiple Regression Modeling
Introduction to Basic Multiple Regression
Important Output Variables
Multiple Regression Example Part 1
Multiple Regression Example Part 2
Multiple Regression Example Part 3
Multiple Regression Example Part 4
Multiple Regression Example Part 5
Multiple Regression Example Part 6
Multiple Regression Example Part 7
Multiple Regression Example Part 8
Multiple Regression Example Part 9
Multiple Regression Example Part 10
Multiple Regression Example Part 11
Multiple Regression Example Part 12
Multiple Regression Example Part 13
Multiple Regression Example Part 14
Logistic Regression
Understanding Logistic Regression Concepts
Working on IBM SPSS Statistics Data Editor
SPSS Statistics Data Editor Continues
IBM SPSS Viewer
Variable in the Equation
Implementation Using MS Excel
Smoke Preferences
Heart Pulse Study
Heart Pulse Study Continues
Variables in the Equation
Smoking Gender Equation
Generating Output and Observations
Generating Output and Observations Continues
Interpretation of Output Example
Multinomial Regression
Introduction to Multinomial-Polynomial Regression
Example 1 Health Study of Marathoners
Note
Case Processing Summary
Model Fitting Information
Asymptotic Correlation Matrix
Understanding Dataset
Generating Output
Parameters Estimates
Asymptotic Correlations Metrics
Interpretation of Output
Interpretation of Output Continues
Interpretation of Estimates
Understand Interpretation
