📐 Linear Regression — Manual Math Breakdown

Let’s calculate everything manually to understand how PySpark computes slope (w) and intercept (b).

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🍋 Use Case: Lemonade Stand Sales

Temperature (x °C)	Sales (y units)
20	30
25	50
30	70
35	90

📘 Step-by-Step Explanation of the Core Formulas

We are trying to fit the equation:

y = w * x + b

Where:

w is the slope
b is the intercept

To compute this manually, here are the steps:

🧮 Step 1: Calculate the Mean of x-values

To find the average (mean) of your input values:

x̄ = (x₁ + x₂ + x₃ + x₄) / 4

This gives you the center point of all the x-values (like average temperature).

📊 Step 2: Calculate the Mean of y-values

Same idea for the target values:

ȳ = (y₁ + y₂ + y₃ + y₄) / 4

This gives the average output (like average sales).

📐 Step 3: Compute the Slope (w)

This formula tells you how much y changes for every 1-unit increase in x:

w = Σ(xᵢ − x̄)(yᵢ − ȳ) / Σ(xᵢ − x̄)²

Breakdown:
-Subtract the mean from each x and y value to get how far each point is from the average.
-Multiply the differences (xᵢ − x̄) and (yᵢ − ȳ) for each row.
-Sum those products (this gives the numerator).
-Then, square each (xᵢ − x̄), and sum them (this gives the denominator).
-Divide numerator by denominator to get w (slope).

📏 Step 4: Compute the Intercept (b)

Once you have the slope, plug into this formula:

b = ȳ − w * x̄

Meaning:
The intercept is the predicted value of y when x = 0. It "shifts" the line up or down to fit the data.

✅ Final Output

Put it all together:

y = w * x + b

Now you have a model that can predict future values of y (like sales) from new values of x (like temperature).

2️⃣ Step-by-Step Calculation

Step 1: Calculate Means

x values	y values
20, 25, 30, 35	30, 50, 70, 90

x̄ = 27.5
ȳ = 60

Step 2: Build Table

x	y	x - x̄	y - ȳ	(x−x̄)(y−ȳ)	(x−x̄)²
20	30	-7.5	-30	225	56.25
25	50	-2.5	-10	25	6.25
30	70	2.5	10	25	6.25
35	90	7.5	30	225	56.25
Totals				500	125

Step 3: Calculate Coefficient & Intercept

w = 500 / 125 = 4.0
b = 60 - (4.0 × 27.5) = -50

✅ Final model: y = 4.0x - 50

3️⃣ Test the Manual Model

x (°C)	Predicted y = 4x - 50	Actual y
20	30	30 ✅
25	50	50 ✅
30	70	70 ✅
35	90	90 ✅

🎯 Perfect fit!

🔁 PySpark Comparison

Here’s how the exact same model looks using PySpark:

from pyspark.ml.regression import LinearRegression
from pyspark.ml.feature import VectorAssembler

data = [(20, 30), (25, 50), (30, 70), (35, 90)]
df = spark.createDataFrame(data, ["temperature", "sales"])

assembler = VectorAssembler(inputCols=["temperature"], outputCol="features")
df_features = assembler.transform(df).select("features", "sales")

lr = LinearRegression(featuresCol="features", labelCol="sales")
model = lr.fit(df_features)

print("Coefficient:", model.coefficients)
print("Intercept:", model.intercept)

Result

Coefficient: [4.0]
Intercept: -50.0

✅ Matches the manual result exactly.

🎓 Why Learn This?

Reason	Benefit
Build intuition	Understand what slope and intercept really mean
Debugging skills	Check if your ML models are making sense
ML foundation	You'll understand more complex models better later

🔑 1-Minute Summary — Manual Linear Regression (Lemonade Sales Example)

Step	What You Did
📊 Raw Data	Temperature and sales from a lemonade stall
🧮 Goal	Fit a line y = w*x + b to predict sales from temperature
📌 Formulas Used	Slope: w = Σ(x_i - x̄)(y_i - ȳ) / Σ(x_i - x̄)² Intercept: b = ȳ - w*x̄
📈 Mean Values	x̄ = 27.5, ȳ = 60
✍️ Computed Table	Calculated (x - x̄)(y - ȳ) and (x - x̄)² for all data points
➕ Sum of Products	Numerator = 500, Denominator = 125
📐 Slope (w)	w = 500 / 125 = 4.0
🧾 Intercept (b)	b = 60 - (4.0 * 27.5) = -50
✅ Final Equation	y = 4.0x - 50
🔮 Manual Predictions	All predicted values match actual ones perfectly
🔁 Compared with PySpark	PySpark model gave same result: Coefficient = 4.0, Intercept = -50.0
🧠 Why This Matters	Builds intuition, helps interpret model meaning, and validates ML results

Next Topic is Predicting House Price from Size Using Linear Regression (PySpark)