Predictive Analytics & Modeling Using Spss

Predictive Analytics & Modeling course aims to enhance predictive modelling skills across business sectors

What you'll learn

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

Requirements

Prior knowledge of Quantitative Methods, MS Office and Paint will be useful.

Description

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 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 usefulThe 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• Interpretation of descriptive statistics and t- values• Implementation on example/sample datasets using SPSSThis 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 itStudentsQuantitative and Predictive Modellers and ProfessionalsCFA’s and Equity Research professionalsPharma and research scientists

Overview

Section 1: Importing Dataset

Lecture 1 Importing Datasets in Text and CSV

Lecture 2 Importing Datasets xlsx and xls Formats

Lecture 3 Importing Datasets xlsx and 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 Implementation

Lecture 12 Data Editor

Lecture 13 Simple Scatter Plot

Lecture 14 Heart Pulse

Lecture 15 Statistics Viewer

Lecture 16 Heart Pulse (Before and After RUN)

Lecture 17 Interpretation and Implementation on Datasets Example 1

Lecture 18 Interpretation and Implementation on Datasets Example 2

Lecture 19 Interpretation and Implementation on Datasets Example 3

Lecture 20 Interpretation and Implementation on Datasets Example 4

Section 3: Linear Regression Modeling

Lecture 21 Introduction to Linear Regression Modeling Using SPSS

Lecture 22 Linear Regression

Lecture 23 Stock Return

Lecture 24 T-Value

Lecture 25 Scatter Plot Rril vs Rbse

Lecture 26 Create Attributes for Variables

Lecture 27 Scatter Plot Rify vs Rbse

Lecture 28 Regression Equation

Lecture 29 Interpretation

Lecture 30 Copper Expansion

Lecture 31 Copper Expansion Example

Lecture 32 Copper Expansion Example Continue

Lecture 33 Energy Consumption

Lecture 34 Observations

Lecture 35 Energy Consumption Example

Lecture 36 Debt Assessment

Lecture 37 Debt Assessment Continue

Lecture 38 Debt to Income Ratio

Lecture 39 Credit Card Debt

Lecture 40 Predicted values Using MS Excel

Lecture 41 Predicted values Using MS Excel Continue

Section 4: Multiple Regression Modeling

Lecture 42 Introduction to Basic Multiple Regression

Lecture 43 Important Output Variables

Lecture 44 Multiple Regression Example Part 1

Lecture 45 Multiple Regression Example Part 2

Lecture 46 Multiple Regression Example Part 3

Lecture 47 Multiple Regression Example Part 4

Lecture 48 Multiple Regression Example Part 5

Lecture 49 Multiple Regression Example Part 6

Lecture 50 Multiple Regression Example Part 7

Lecture 51 Multiple Regression Example Part 8

Lecture 52 Multiple Regression Example Part 9

Lecture 53 Multiple Regression Example Part 10

Lecture 54 Multiple Regression Example Part 11

Lecture 55 Multiple Regression Example Part 12

Lecture 56 Multiple Regression Example Part 13

Lecture 57 Multiple Regression Example Part 14

Section 5: Logistic Regression

Lecture 58 Understanding Logistic Regression Concepts

Lecture 59 Working on IBM SPSS Statistics Data Editor

Lecture 60 SPSS Statistics Data Editor Continues

Lecture 61 IBM SPSS Viewer

Lecture 62 Variable in the Equation

Lecture 63 Implementation Using MS Excel

Lecture 64 Smoke Preferences

Lecture 65 Heart Pulse Study

Lecture 66 Heart Pulse Study Continues

Lecture 67 Variables in the Equation

Lecture 68 Smoking Gender Equation

Lecture 69 Generating Output and Observations

Lecture 70 Generating Output and Observations Continues

Lecture 71 Interpretation of Output Example

Section 6: Multinomial Regression

Lecture 72 Introduction to Multinomial-Polynomial Regression

Lecture 73 Example 1 Health Study of Marathoners

Lecture 74 Note

Lecture 75 Case Processing Summary

Lecture 76 Model Fitting Information

Lecture 77 Asymptotic Correlation Matrix

Lecture 78 Understanding Dataset

Lecture 79 Generating Output

Lecture 80 Parameters Estimates

Lecture 81 Asymptotic Correlations Metrics

Lecture 82 Interpretation of Output

Lecture 83 Interpretation of Output Continues

Lecture 84 Interpretation of Estimates

Lecture 85 Understand Interpretation

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