Little Bits of Artificial Intelligence

1. Arithmetic Operations

• Addition, subtraction, multiplication, division
• Why: Needed for calculating sums like ∑x , ∑y , and so on

2. Averages (Mean)

• Formula: xˉ=∑x / n
• Why: Helps to understand “center” of the data and later used in interpretation

3. Squares and Square Roots

• Understanding x² (e.g., 3² = 9)
• Why: Used to calculate ∑x², also in measuring errors (like MSE)

4. Basic Algebra

• Understanding variables (x, y), constants, and solving equations
• Why: Regression line is an equation: y = mx + c

5. Summation (Σ Notation)

• Notation like ∑x, ∑xy
• Why: Helps compactly represent data operations (used in slope/intercept formulas)

6. Slope and Intercept

• Concept of slope = “rise over run” in a line
  Intercept = where the line crosses y-axis
• Why: Key to understanding what the regression line really means

7. Correlation Intuition

• Not mandatory, but helpful to grasp: do two variables increase/decrease together?
• Why: Regression builds on this idea — fitting a line where such a pattern exists