Logistic Regression Supervised Learning using SPSS

Logistic Regression & Supervised Learning using SPSS, available at Free, has an average rating of 4.55, with 14 lectures, based on 17 reviews, and has 3800 subscribers.

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You will learn about course aims to provide and enhance predictive modelling skills across business sectors 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 The course also emphasizes on the higher order regression models such as quadratic and polynomial regressions This course is ideal for individuals who are Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers It is particularly useful for Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers.

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Summary

Title: Logistic Regression & Supervised Learning using SPSS

Price: Free

Average Rating: 4.55

Number of Lectures: 14

Number of Published Lectures: 14

Number of Curriculum Items: 14

Number of Published Curriculum Objects: 14

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

course aims to provide and enhance predictive modelling skills across business sectors

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

The course also emphasizes on the higher order regression models such as quadratic and polynomial regressions

Who Should Attend

Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers

Target Audiences

Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers

Logistic regression in SPSS is defined as the binary classification problem in the field of statistic measuring. The difference between a dependent and independent variable with the guide of logistic function by estimating the different occurrence of the probabilities, i.e., it is used to predict the outcome of the independent variable (1 or 0 either yes/no) as it is an extension of a linear regression which is used to predict the continuous output variables.

Logistic regression is a technique used in the field of statistics measuring the difference between a dependent and independent variable with the guide of logistic function by estimating the different occurrence of probabilities. They can be either binomial (has yes or No outcome) or multinomial (Fair vs poor very poor). The probability values lie between 0 and 1, and the variable should be positive (<1).

It targets the dependent variable and has the following steps to follow:

n- no. of fixed trials on a taken dataset.

With two outcomes trial.

The outcome of the probability should be independent of each other.

The probability of success and failures must be the same at each trial.

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 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:

Interpretation of regression attributes such as R-Squared (correlation coefficient), t and p values

m (slope) and c (intercept),

Dependent variables (Y), independent (A1, A2, A3……) variables, and Binary/Dummy B1, B2, B3 …..) variables

Examining significance/relevance of A, B variables for regression model (equation) goodness of fit

Predicting Y-variable upon varying values of A, B variables

Understanding Multi-Collinearity and its disadvantages

Implementation on sample datasets using SPSS and output simulation in MS Excel

Course Curriculum

Chapter 1: Introduction

Lecture 1: Understanding Logistic Regression Concepts

Lecture 2: Working on IBM SPSS Statistics Data Editor

Lecture 3: SPSS Statistics Data Editor Continues

Lecture 4: IBM SPSS Viewer

Chapter 2: Implementation using MS Excel – Example

Lecture 1: Variable in the Equation

Lecture 2: Implementation Using MS Excel

Lecture 3: Smoke Preferences

Lecture 4: Heart Pulse Study

Lecture 5: Heart Pulse Study Continues

Lecture 6: Variables in the Equation

Lecture 7: Smoking Gender Equation

Lecture 8: Generating Output and Observations

Lecture 9: Generating Output and Observations Continues

Lecture 10: Interpretation of Output Example

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

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4 stars: 3 votes

5 stars: 12 votes

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