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.
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.
Enroll now: Logistic Regression & Supervised Learning using SPSS
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:
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n- no. of fixed trials on a taken dataset.
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With two outcomes trial.
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The outcome of the probability should be independent of each other.
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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:
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Interpretation of regression attributes such as R-Squared (correlation coefficient), t and p values
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m (slope) and c (intercept),
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Dependent variables (Y), independent (A1, A2, A3……) variables, and Binary/Dummy B1, B2, B3 …..) variables
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Examining significance/relevance of A, B variables for regression model (equation) goodness of fit
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Predicting Y-variable upon varying values of A, B variables
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Understanding Multi-Collinearity and its disadvantages
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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
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EDUCBA Bridging the Gap
Learn real world skills online
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 1 votes
- 3 stars: 1 votes
- 4 stars: 3 votes
- 5 stars: 12 votes
Frequently Asked Questions
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