Linear Regression & Supervised Learning with SPSS

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EDUCBA Bridging the Gap

3:09:27

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01 - Introduction

001 Introduction to Linear Regression Modeling Using SPSS.mp4

07:36

02 - Interpretation of Attributes

001 Linear Regression.mp4

08:20

002 Stock Return.mp4

09:33

003 T-Value.mp4

08:57

004 Scatter Plot Rril vs Rbse.mp4

10:11

005 Create Attributes for Variables.mp4

10:26

006 Scatter Plot-Rify vs Rbse.mp4

04:50

007 Regression Equation.mp4

09:17

008 Interpretation.mp4

09:35

009 Copper Expansion.mp4

11:57

010 Copper Expansion Example.mp4

08:45

011 Copper Expansion Example Continue.mp4

10:06

012 Energy Consumption.mp4

11:39

013 Observations.mp4

08:17

014 Energy Consumption Example.mp4

04:49

015 Debt Assessment.mp4

10:46

016 Debt Assessment Continue.mp4

08:20

017 Debt to Income Ratio.mp4

11:49

018 Credit Card Debt.mp4

10:58

019 Predicted values Using MS Excel.mp4

06:59

020 Predicted values Using MS Excel Continue.mp4

06:17

Description

The core objective is to provide skills in understand the regression model and interpreting it for predictions

What You'll Learn?

The course works across multiple software packages such as SPSS, MS Office, PDF writers, and Paint.
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
Interpretation of regression attributes such as R-Squared (correlation coefficient), t and p values

Who is this for?

Students and Quantitative and Predictive Modellers and Professionals

What You Need to Know?

Prior knowledge of Quantitative Methods, MS Office and Paint is desired.

More details

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 arenâ€™t covered in other online courses
ïƒ˜ Essential skillsets â€“ Prior knowledge of Quantitative methods and MS Office, Paint
ïƒ˜ Desired skillsets -- Understanding of Data Analysis and VBA toolpack in MS Excel will be useful
The course works across multiple software 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
â€¢ Interpretation of regression attributes such as R-Squared (correlation coefficient), t and p values
â€¢ m (slope) and c (intercept),
â€¢ dependent (Y) and independent (X) variables
â€¢ Examining the significance of independent (X) variable to check the fitness of regression model
â€¢ Predicting Y-variable based on varying values of X-variable
â€¢ Implementation on sample datasets using SPSS and output simulation in MS Excel
Who this course is for:
Students and Quantitative and Predictive Modellers and Professionals

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