Machine Learning A-Z™: Hands-On Python & R In Data
  • Introduction
  • Introduction
    • Introduction
  • Section 1: Welcome to the course!
    • 1. Applications of Machine Learning
    • 2. Why Machine Learning is the Future
    • 3. Important notes, tips & tricks for this course
    • 4. Installing Python and Anaconda (Mac, Linux & Windows)
    • 5. Update: Recommended Anaconda Version
    • 6. Installing R and R Studio (Mac, Linux & Windows)
    • 7. BONUS: Meet your instructors
  • Section 2: Part 1 Data Preprocessing
    • 8. Welcome to Part 1 - Data Preprocessing
    • 9. Get the dataset
    • 10. Importing the Libraries
    • 11. Importing the Dataset
    • 12. For Python learners, summary of Object-oriented programming: classes & objects
    • 13. Missing Data
    • 14. Categorical Data
    • 15. WARNING - Update
    • 16. Splitting the Dataset into the Training set and Test set
    • 17. Feature Scaling
    • 18. And here is our Data Preprocessing Template!
    • Quiz 1: Data Preprocessing
  • Section 3: Part 2 Regression
    • 19. Welcome to Part 2 - Regression
  • Section 4: Simple Linear Regression
    • 20. How to get the dataset
    • 21. Dataset + Business Problem Description
    • 22. Simple Linear Regression Intuition - Step 1
    • 23. Simple Linear Regression Intuition - Step 2
Powered by GitBook
On this page
  • SIMPLE LINEAR REGRESSION 1
  • Regressions
  • Simple Linear Regression
  1. Section 4: Simple Linear Regression

22. Simple Linear Regression Intuition - Step 1

SIMPLE LINEAR REGRESSION 1

Regressions

  • Simple Linear Regression -> y = b0 + b1 * x1

  • y is the Dependent variable (DV)

  • Something you are trying to understand how it depends on something else

  • x1 is the Independent variable (IV)

  • so x1 might have an implied association between Independent variable and Dependent variable

  • b1 is the coefficient for the dependent variable

  • It kind of says how the effect or how a change in x1, a unit change in x1

  • Unit changes in x1 how that affects a unit change in Y.

  • So it is also the proportion to which that change in inflated or deflated

Simple Linear Regression

  • Experience is going to be our horizontal axis (X)

  • Salary ($) is going to be our Vertical axis (Y)

  • And what that essentially means is just putting a line through your chart that best fits this data

  • When the experience is equals to 0, salary will equals to b0 which will be $30,000

  • So someone with no experience, assuming fresh from university will most likely have a salary about $30,000

  • b1 is the slope of the line

  • The steeper the line, the more you get more salary, based on the experience

Previous21. Dataset + Business Problem DescriptionNext23. Simple Linear Regression Intuition - Step 2

Last updated 6 years ago