PacktPub | Understanding Regression Techniques [Video] [FCO]

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  • Type Tutorials
  • Language English
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By : Najib Mozahem
Released : March 24, 2020
Course Source : https://www.packtpub.com/data/understanding-regression-techniques-video

Explore the fundamentals of linear regression, logistic regression, and count model regression in an intuitive and non-mathematical way

Video Details

ISBN 9781800200197
Course Length 7 hours 10 minutes

Learn

• Understand the concept of regression
• Build logistic regression models
• Interpret regression results
• Build linear regression models
• Build count models
• Visualize the results

About

Linear and logistic regressions are among the first set of algorithms you’ll study to get started on your journey in data science.

This course explores three basic regressions—linear, logistic, and count model. Starting with linear regressions, you’ll first understand the difference between simple and multiple linear regressions and explore different types of variables, including binary, categorical, and quadratic. Once you’ve got to grips with the fundamentals, you’ll apply what you’ve learned to solve a case study. As you advance, you’ll explore logistic regression models and cover variables, non-linearity tests, prediction, and model fit. Finally, you’ll get well-versed with count model regression.

By the end of the course, you’ll be equipped with the knowledge you need to investigate correlations between multiple variables using regression models.

All the codes and supporting files for this course will be available at- https://github.com/PacktPublishing/Understanding-Regression-Techniques

Features:

• Understand the normality and independence of residuals
• Explore both graphical and non-graphical tests for non-linearity in logistic regression models
• Get to grips with count tables, their risk, and incidence rate ratio

Author

Najib Mozahem

Najib Mozahem works as a researcher and as an assistant professor at the university level, where he teaches Quantitative Analysis. He holds a Bachelor’s degree in Computer and Communication Engineering, completed his MBA with distinction, and completed his Ph.D. in Organizational Theory where he won the best thesis prize for Ph.D. He has also received the teaching excellence award for the year 2016 – 2017. His research interests include quantitative modeling and the study of human behavior in organizations.



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1.Simple Linear Regression
  • 01.Introduction.mp4 (33.8 MB)
  • 02.Simple linear regression.mp4 (49.7 MB)
  • 03.The slope.mp4 (61.8 MB)
  • 04.R-squared.mp4 (51.1 MB)
  • 05.The p-value.mp4 (49.3 MB)
  • 06.Model fit.mp4 (15.1 MB)
  • 07.The residuals.mp4 (56.8 MB)
10.Count Models - Count Tables
  • 65.Count tables.mp4 (28.6 MB)
  • 66.Risk.mp4 (16.9 MB)
  • 67.Inceidence-rate ratio.mp4 (20.8 MB)
  • 68.Two-by-three tables.mp4 (36.1 MB)
11.Poisson Regression
  • 69.Single independent variable.mp4 (159.4 MB)
  • 70.Examples.mp4 (34.5 MB)
  • 71.Binary variables.mp4 (86.0 MB)
  • 72.Multiple independent variables.mp4 (67.8 MB)
  • 73.Categorical variables.mp4 (79.4 MB)
  • 74.Exposure.mp4 (74.9 MB)
12.Other Count Models
  • 75.Negative binomial regression.mp4 (53.7 MB)
  • 76.Truncated models.mp4 (30.4 MB)
  • 77.Zero-inflated models.mp4 (85.3 MB)
  • 78.Comparison of models.mp4 (28.1 MB)
13.Prediction
  • 79.Predicting the number of events.mp4 (12.4 MB)
  • 80.Predicting probabilities of certain counts.mp4 (9.6 MB)
14.Count Model Case Study
  • 81.The dataset.mp4 (4.0 MB)
  • 82.Continuous variables.mp4 (21.6 MB)
  • 83.Binary variables.mp4 (3.1 MB)
  • 84.Multivariate analysis.mp4 (3.5 MB)
  • 85.Negative binomial regression.mp4 (5.4 MB)
  • 86.Zero-inflated models.mp4 (22.9 MB)
  • 87.Comparing count models.mp4 (10.6 MB)
  • 88.Visualizing the result.mp4 (34.6 MB)
15.Conclusion
  • 89.Conclusion.mp4 (17.8 MB)
2.Multiple linear regression
  • 08.Multiple linear regression.mp4 (43.1 MB)
  • 09.The slopes.mp4 (43.5 MB)
  • 10.R-squared.mp4 (12.5 MB)
  • 11.The p-value.mp4 (10.9 MB)
  • 12.Model fit and residuals.mp4 (39.7 MB)
3.Linear Regression - Binary, Categorical, and Quadratic Variables
  • 13.Binary variables.mp4 (111.8 MB)
  • 14.Categorical variables.mp4 (185.1 MB)
  • 15.Quadratic variables.mp4 (85.2 MB)
4.Linear Regression - Checking Model Fit and Assumptions
  • 16.Prediction.mp4 (22.9 MB)
  • 17.Normality of residuals.mp4 (10.3 MB)
  • 18.Independence of residuals.mp4 (10.1 MB)
  • 19.Constant variance.mp4 (9.9 MB)
  • 20.Multicollinearity.mp4 (12.5 MB)
  • 21.Outliers.mp4 (14.9 MB)
  • 22.Influential observations.mp4 (34.9 MB)
  • 23.Selection algorithms.mp4 (49.6 MB)
5.Linear Regression Case Study
  • 24.The dataset.mp4 (10.0 MB)
  • 25.Including continuous variables.mp4 (31.8 MB)
  • 26.Including binary variables.mp4 (6.0 MB)
  • 27.Including categorical variables.mp4 (5.8 MB)
  • 28.Multiple regression.mp4 (13.1 MB)
  • 29.Checking model fit.mp4 (9.1 MB)
  • 30.Checking model assumptions.mp4 (19.3 MB)
  • 31.Multicollinearity.mp4 (5.2 MB)
  • 32.Outliers.mp4 (9.3 MB)
  • 33.Influential observations.mp4 (15.9 MB)
  • 34.Visualizing the result.mp4 (9.0 MB)
6.Logistic Regression - Contingency Tables
  • 35.Two-by-two tables.mp4 (30.3 MB)
  • 36.The odds.mp4 (27.4 MB)
  • 37.The odds ratio.mp4 (31.5 MB)
  • 38.Two-by-three tables.mp4 (73.5 MB)
7.Logistic Regression Models
  • 39.Single independent variable.mp4 (152.0 MB)
  • 40.Examples.mp4 (39.9 MB)
  • 41.Binary variables.mp4 (71.7 MB)
  • 42.Multiple independent variables.mp4 (66.1 MB)
  • 43.Categorical variables.mp4 (92.2 MB)
  • 44.Nonlinearity - Non-graphical test.mp4 (36.8 MB)
  • 45.Nonlinearity - Graphical test.mp4 (63.7 MB)
8.Logistic Regression - Prediction and Model Fit
  • 46.Prediction.mp4 (19.2 MB)
  • 47.Goodness of fit - Likelihood ratio test.mp4 (14.1 MB)
  • 48.Goodness of fit - Hosmer-Lemeshow test.mp4 (23.1 MB)
  • 49.Goodness of fit - Classification tables.mp4 (54.6 MB)
  • 50.Goodness of fit - ROC analysis.mp4 (8.8 MB)
  • 51.Residuals.mp4 (9.7 MB)
  • 52.Influential Observations.mp4 (27.2 MB)
9.Logistic Regression Case Study
  • 53.The dataset.mp4 (11.4 MB)
  • 54.Continuous variables.mp4 (8.7 MB)
  • 55.Test of linearity - Non-graphical.mp4 (5.2 MB)
  • 56.Test of linearity - Graphical.mp4 (16.8 MB)
  • 57.Binary variables.mp4 (6.3 MB)
  • 58.Categorical variables.mp4 (26.0 MB)
  • 59.Multivariate analysis.mp4 (7.2 MB)
  • 60.Goodness of fit.mp4 (17.9 MB)
  • 61.Residual analysis.mp4 (8.2 MB)
  • 62.Influential observations.mp4 (6.8 MB)
  • 63.Combining both residuals and influence in one graph.mp4 (13.7 MB)
  • 64.Visualizing the result.mp4 (7.3 MB)
Exercise Files
  • code_9781800200197.zip (91.5 MB)

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