Predictive Modeling And Regression Analysis 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


Homepage

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