Udemy - Linear Regression using Stata

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size2.1 GB
  • Uploaded Byfreecoursewb
  • Downloads20
  • Last checkedJul. 03rd '26
  • Date uploadedJul. 02nd '26
  • Seeders 0
  • Leechers5

Infohash : BEB08B8050DE6321A694D5B2E7A136509CC6B4D1

Linear Regression using Stata

https://WebToolTip.com

Last updated 4/2020
Created by Najib Mozahem
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 46 Lectures ( 3h 22m ) | Size: 2.2 GB

Theory and Application

What you'll learn
⚡ Understand what is linear regression
⚡ Identify when linear regression is used
⚡ Understand the difference between simple linear regression and multiple linear regression
⚡ Include the correct variables in the model
⚡ Understand the output produced by linear regression
⚡ Include categorical variables
⚡ Include quadratic variables
⚡ Predict values
⚡ Calculate the residuals
⚡ Test model assumptions
⚡ Apply linear regression using Stata
⚡ Understand the output generated by Stata
⚡ Produce graphs that summarize the results

Requirements
❗ Basix high school algebra
❗ Have a basic understand of Stata (only for the second part of the course)

Files:

[ WebToolTip.com ] Udemy - Linear Regression using Stata
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Simple Linear Regression
    • 1. Book.pdf (630.4 KB)
    • 1. Introduction.en_US.srt (4.0 KB)
    • 1. Introduction.mp4 (63.0 MB)
    • 1. Slides.pdf (5.8 MB)
    • 2. Simple linear regression.en_US.srt (5.7 KB)
    • 2. Simple linear regression.mp4 (46.3 MB)
    • 2. linear1.url (0.1 KB)
    • 3. The slope.en_US.srt (6.6 KB)
    • 3. The slope.mp4 (66.7 MB)
    • 4. R-squared.en_US.srt (6.8 KB)
    • 4. R-squared.mp4 (61.0 MB)
    • 5. The p-value.en_US.srt (9.5 KB)
    • 5. The p-value.mp4 (119.0 MB)
    • 6. Model fit.en_US.srt (3.4 KB)
    • 6. Model fit.mp4 (33.5 MB)
    • 7. The residuals.en_US.srt (6.8 KB)
    • 7. The residuals.mp4 (52.0 MB)
    2 - Multiple Linear Regression
    • 10. R-squared.en_US.srt (2.1 KB)
    • 10. R-squared.mp4 (22.9 MB)
    • 11. The p-value.en_US.srt (1.6 KB)
    • 11. The p-value.mp4 (19.7 MB)
    • 12. Model fit and residuals.en_US.srt (2.8 KB)
    • 12. Model fit and residuals.mp4 (28.7 MB)
    • 8. Multiple linear regression.en_US.srt (4.1 KB)
    • 8. Multiple linear regression.mp4 (31.6 MB)
    • 9. The slopes.en_US.srt (6.1 KB)
    • 9. The slopes.mp4 (64.4 MB)
    3 - Binary, Categorical, and Quadratic Variables
    • 13. Binary variables.en_US.srt (10.7 KB)
    • 13. Binary variables.mp4 (118.8 MB)
    • 14. Categrical variables.en_US.srt (14.7 KB)
    • 14. Categrical variables.mp4 (138.7 MB)
    • 15. Quadratic variables.en_US.srt (9.4 KB)
    • 15. Quadratic variables.mp4 (87.2 MB)
    4 - Checking Model Fit and Assumptions
    • 16. Prediction.en_US.srt (5.6 KB)
    • 16. Prediction.mp4 (40.8 MB)
    • 17. Normality of residuals.en_US.srt (3.0 KB)
    • 17. Normality of residuals.mp4 (19.4 MB)
    • 18. Independence of residuals.en_US.srt (3.1 KB)
    • 18. Independence of residuals.mp4 (9.6 MB)
    • 19. Constant variance.en_US.srt (2.5 KB)
    • 19. Constant variance.mp4 (9.3 MB)
    • 20. Multicolinearity.en_US.srt (3.7 KB)
    • 20. Multicolinearity.mp4 (35.9 MB)
    • 21. Outliers.en_US.srt (5.8 KB)
    • 21. Outliers.mp4 (26.1 MB)
    • 21. linear2.url (0.1 KB)
    • 22. Influencial observations.en_US.srt (6.2 KB)
    • 22. Influencial observations.mp4 (61.1 MB)
    • 23. Selection algorithms.en_US.srt (11.9 KB)
    • 23. Selection algorithms.mp4 (154.7 MB)
    5 - Application Fitting the Model
    • 24. Introduction.en_US.srt (2.5 KB)
    • 24. Introduction.mp4 (39.3 MB)
    • 24. linear1.url (0.1 KB)
    • 25. Simple linear regression.en_US.srt (4.6 KB)
    • 25. Simple linear regression.mp4 (20.4 MB)
    • 26. Model fit.en_US.srt (9.1 KB)
    • 26. Model fit.mp4 (44.6 MB)
    • 27. Multiple linear regression.en_US.srt (3.5 KB)
    • 27. Multiple linear regression.mp4 (21.3 MB)
    • 28. Model fit.en_US.srt (7.5 KB)
    • 28. Model fit.mp4 (41.5 MB)
    • 29. Binary variables.en_US.srt (9.0 KB)
    • 29. Binary variables.mp4 (60.0 MB)
    • 30. Categrical variables.en_US.srt (5.8 KB)
    • 30. Categrical variables.mp4 (38.6 MB)
    • 31. Quadratic variables.en_US.srt (10.6 KB)
    • 31. Quadratic variables.mp4 (68.4 MB)
    6 - Application Checking Model Fit and Assumptions
    • 32. Prediction.en_US.srt (3.3 KB)
    • 32. Prediction.mp4 (23.5 MB)
    • 33. Normality of residuals.en_US.srt (4.0 KB)
    • 33. Normality of residuals.mp4 (27.5 MB)
    • 34. Independence of residuals and Constant variance.en_US.srt (8.3 KB)
    • 34. Independence of residuals and Constant variance.mp4 (54.6 MB)
    • 35. Multicolinearity.en_US.srt (1.6 KB)
    • 35. Multicolinearity.mp4 (11.4 MB)
    • 36. Outliers.en_US.srt (8.3 KB)
    • 36. Outliers.mp4 (49.9 MB)
    • 36. linear2.url (0.1 KB)
    • 37. Influencial observations.en_US.srt (6.7 KB)
    • 37. Influencial observations.mp4 (51.2 MB)
    • 38. Selection algorithms.en_US.srt (5.7 KB)
    • 38. Selection algorithms.mp4 (44.9 MB)
    7 - Application Visualizing the Model
    • 39. Introduction.en_US.srt (1.8 KB)
    • 39. Introduction.mp4 (29.8 MB)
    • 40. One independent variable.en_US.srt (5.6 KB)
    • 40. One independent variable.mp4 (43.6 MB)
    • 41. Two independent variables.en_US.srt (4.0 KB)
    • 41. Two independent variables.mp4 (30.1 MB)
    • 42. Three independent variables.en_US.srt (5.5 KB)
    • 42. Three independent variables.mp4 (42.0 MB)
    • 43. Quadratic variables.en_US.srt (8.3 KB)
    • 43. Quadratic variables.mp4 (61.8 MB)
    8 - Conclusion The Project
    • 44. Introduction.en_US.srt (2.2 KB)
    • 44. Introduction.mp4 (34.3 MB)
    • 44. Project dataset.url (0.1 KB)
    • 45. The project.en_US.srt (3.3 KB)
    • 45. The project.mp4 (17.1 MB)
    • 45. solution.pdf (2.3 MB)
    • 46. Conclusion.en_US.srt (0.8 KB)
    • 46. Conclusion.mp4 (11.0 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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