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PacktPub | Understanding Regression Techniques [Video] [FCO]

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File	Size
0. Websites you may like/0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url	377.00 B
0. Websites you may like/1. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url	286.00 B
0. Websites you may like/2. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, & more.etc.url	163.00 B
0. Websites you may like/3. (FTUApps.com) Download Cracked Developers Applications For Free.url	239.00 B
0. Websites you may like/How you can help our Group!.txt	208.00 B
1.Simple Linear Regression/01.Introduction.mp4	33.82 MB
1.Simple Linear Regression/02.Simple linear regression.mp4	49.67 MB
1.Simple Linear Regression/03.The slope.mp4	61.80 MB
1.Simple Linear Regression/04.R-squared.mp4	51.14 MB
1.Simple Linear Regression/05.The p-value.mp4	49.25 MB
1.Simple Linear Regression/06.Model fit.mp4	15.12 MB
1.Simple Linear Regression/07.The residuals.mp4	56.81 MB
10.Count Models - Count Tables/65.Count tables.mp4	28.62 MB
10.Count Models - Count Tables/66.Risk.mp4	16.93 MB
10.Count Models - Count Tables/67.Inceidence-rate ratio.mp4	20.81 MB
10.Count Models - Count Tables/68.Two-by-three tables.mp4	36.09 MB
11.Poisson Regression/69.Single independent variable.mp4	159.42 MB
11.Poisson Regression/70.Examples.mp4	34.46 MB
11.Poisson Regression/71.Binary variables.mp4	86.02 MB
11.Poisson Regression/72.Multiple independent variables.mp4	67.77 MB
11.Poisson Regression/73.Categorical variables.mp4	79.37 MB
11.Poisson Regression/74.Exposure.mp4	74.94 MB
12.Other Count Models/75.Negative binomial regression.mp4	53.70 MB
12.Other Count Models/76.Truncated models.mp4	30.39 MB
12.Other Count Models/77.Zero-inflated models.mp4	85.27 MB
12.Other Count Models/78.Comparison of models.mp4	28.09 MB
13.Prediction/79.Predicting the number of events.mp4	12.43 MB
13.Prediction/80.Predicting probabilities of certain counts.mp4	9.63 MB
14.Count Model Case Study/81.The dataset.mp4	4.05 MB
14.Count Model Case Study/82.Continuous variables.mp4	21.57 MB
14.Count Model Case Study/83.Binary variables.mp4	3.07 MB
14.Count Model Case Study/84.Multivariate analysis.mp4	3.45 MB
14.Count Model Case Study/85.Negative binomial regression.mp4	5.44 MB
14.Count Model Case Study/86.Zero-inflated models.mp4	22.90 MB
14.Count Model Case Study/87.Comparing count models.mp4	10.56 MB
14.Count Model Case Study/88.Visualizing the result.mp4	34.62 MB
15.Conclusion/89.Conclusion.mp4	17.76 MB
2.Multiple linear regression/08.Multiple linear regression.mp4	43.10 MB
2.Multiple linear regression/09.The slopes.mp4	43.48 MB
2.Multiple linear regression/10.R-squared.mp4	12.51 MB
2.Multiple linear regression/11.The p-value.mp4	10.94 MB
2.Multiple linear regression/12.Model fit and residuals.mp4	39.72 MB
3.Linear Regression - Binary, Categorical, and Quadratic Variables/13.Binary variables.mp4	111.79 MB
3.Linear Regression - Binary, Categorical, and Quadratic Variables/14.Categorical variables.mp4	185.14 MB
3.Linear Regression - Binary, Categorical, and Quadratic Variables/15.Quadratic variables.mp4	85.24 MB
4.Linear Regression - Checking Model Fit and Assumptions/16.Prediction.mp4	22.92 MB
4.Linear Regression - Checking Model Fit and Assumptions/17.Normality of residuals.mp4	10.26 MB
4.Linear Regression - Checking Model Fit and Assumptions/18.Independence of residuals.mp4	10.07 MB
4.Linear Regression - Checking Model Fit and Assumptions/19.Constant variance.mp4	9.94 MB
4.Linear Regression - Checking Model Fit and Assumptions/20.Multicollinearity.mp4	12.46 MB
4.Linear Regression - Checking Model Fit and Assumptions/21.Outliers.mp4	14.88 MB
4.Linear Regression - Checking Model Fit and Assumptions/22.Influential observations.mp4	34.87 MB
4.Linear Regression - Checking Model Fit and Assumptions/23.Selection algorithms.mp4	49.56 MB
5.Linear Regression Case Study/24.The dataset.mp4	9.99 MB
5.Linear Regression Case Study/25.Including continuous variables.mp4	31.81 MB
5.Linear Regression Case Study/26.Including binary variables.mp4	5.96 MB
5.Linear Regression Case Study/27.Including categorical variables.mp4	5.76 MB
5.Linear Regression Case Study/28.Multiple regression.mp4	13.10 MB
5.Linear Regression Case Study/29.Checking model fit.mp4	9.12 MB
5.Linear Regression Case Study/30.Checking model assumptions.mp4	19.27 MB
5.Linear Regression Case Study/31.Multicollinearity.mp4	5.19 MB
5.Linear Regression Case Study/32.Outliers.mp4	9.29 MB
5.Linear Regression Case Study/33.Influential observations.mp4	15.93 MB
5.Linear Regression Case Study/34.Visualizing the result.mp4	8.96 MB
6.Logistic Regression - Contingency Tables/35.Two-by-two tables.mp4	30.29 MB
6.Logistic Regression - Contingency Tables/36.The odds.mp4	27.36 MB
6.Logistic Regression - Contingency Tables/37.The odds ratio.mp4	31.54 MB
6.Logistic Regression - Contingency Tables/38.Two-by-three tables.mp4	73.52 MB
7.Logistic Regression Models/39.Single independent variable.mp4	152.02 MB
7.Logistic Regression Models/40.Examples.mp4	39.90 MB
7.Logistic Regression Models/41.Binary variables.mp4	71.67 MB
7.Logistic Regression Models/42.Multiple independent variables.mp4	66.07 MB
7.Logistic Regression Models/43.Categorical variables.mp4	92.15 MB
7.Logistic Regression Models/44.Nonlinearity - Non-graphical test.mp4	36.78 MB
7.Logistic Regression Models/45.Nonlinearity - Graphical test.mp4	63.65 MB
8.Logistic Regression - Prediction and Model Fit/46.Prediction.mp4	19.23 MB
8.Logistic Regression - Prediction and Model Fit/47.Goodness of fit - Likelihood ratio test.mp4	14.08 MB
8.Logistic Regression - Prediction and Model Fit/48.Goodness of fit - Hosmer-Lemeshow test.mp4	23.06 MB
8.Logistic Regression - Prediction and Model Fit/49.Goodness of fit - Classification tables.mp4	54.64 MB
8.Logistic Regression - Prediction and Model Fit/50.Goodness of fit - ROC analysis.mp4	8.76 MB
8.Logistic Regression - Prediction and Model Fit/51.Residuals.mp4	9.71 MB
8.Logistic Regression - Prediction and Model Fit/52.Influential Observations.mp4	27.16 MB
9.Logistic Regression Case Study/53.The dataset.mp4	11.36 MB
9.Logistic Regression Case Study/54.Continuous variables.mp4	8.67 MB
9.Logistic Regression Case Study/55.Test of linearity - Non-graphical.mp4	5.17 MB
9.Logistic Regression Case Study/56.Test of linearity - Graphical.mp4	16.80 MB
9.Logistic Regression Case Study/57.Binary variables.mp4	6.32 MB
9.Logistic Regression Case Study/58.Categorical variables.mp4	26.03 MB
9.Logistic Regression Case Study/59.Multivariate analysis.mp4	7.22 MB
9.Logistic Regression Case Study/60.Goodness of fit.mp4	17.87 MB
9.Logistic Regression Case Study/61.Residual analysis.mp4	8.16 MB
9.Logistic Regression Case Study/62.Influential observations.mp4	6.77 MB
9.Logistic Regression Case Study/63.Combining both residuals and influence in one graph.mp4	13.73 MB
9.Logistic Regression Case Study/64.Visualizing the result.mp4	7.30 MB
Exercise Files/code_9781800200197.zip	91.52 MB

Description

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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.