In Python with pandas, linear regression for the sample data takes a few lines, shown below. It returns 6.42.
Slope = Σ (x − x̄)(y − ȳ) ÷ Σ (x − x̄)²; Intercept = ȳ − slope × x̄
Step by step
- Install the libraries (pip install pandas numpy) and save daily.csv next to your script.
- Run the script and compare the printed value with the expected result.
The code
import numpy as np
import pandas as pd
df = pd.read_csv("daily.csv")
slope, intercept = np.polyfit(df["ad_spend"], df["website_orders"], 1)
orders_per_1000 = slope * 1000
print(round(orders_per_1000, 3), round(intercept, 2))
Expected result on the sample data: 6.42.
What the result means
The slope here is multiplied by 1,000, so it reads as extra website orders per extra ₹1,000 of daily ad spend. It describes the past association, not what more spend will cause.
Notes for Python
- pandas methods such as .std(), .var() and .quantile() match Excel's sample versions (STDEV.S, VAR.S, PERCENTILE.INC). NumPy's np.std() and np.var() default to the population version instead.
- pandas skips NaN values in these calculations by default. Count how many rows were used if data might be missing.
Where linear regression misleads
- Regression on observational data is still correlation; it does not prove the spend caused the orders.
- Do not predict far outside the range of x you have. A line fitted between ₹3,000 and ₹7,200 says nothing about ₹20,000.
- Check the residuals: a pattern in them means a straight line is the wrong shape.
The plain-language explanation is on the Linear regression (slope) page.
The data
| day | ad_spend | website_orders |
|---|---|---|
| 1 | 3,800 | 24 |
| 2 | 3,000 | 27 |
| 3 | 3,900 | 22 |
| 4 | 3,200 | 30 |
| 5 | 4,000 | 28 |
| 6 | 4,100 | 25 |
| 7 | 3,100 | 31 |
| 8 | 3,600 | 29 |
| 9 | 4,100 | 26 |
| 10 | 3,500 | 33 |
| 11 | 4,300 | 30 |
| 12 | 4,500 | 28 |
| 13 | 3,900 | 35 |
| 14 | 3,900 | 32 |
| 15 | 4,300 | 29 |
| 16 | 3,700 | 34 |
| 17 | 7,200 | 61 |
| 18 | 4,800 | 31 |
| 19 | 3,800 | 36 |
| 20 | 4,500 | 33 |
| 21 | 4,500 | 30 |
| 22 | 4,000 | 37 |
| 23 | 4,200 | 35 |
| 24 | 4,800 | 32 |
| 25 | 4,300 | 38 |
| 26 | 5,000 | 36 |
| 27 | 3,900 | 34 |
| 28 | 5,000 | 40 |
| 29 | 5,200 | 37 |
| 30 | 4,700 | 39 |
Same method, other tools
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- Python and SQL for data analysis, side by side — 3 min read
- Data analysis methods and techniques, and when to use each — 3 min read
About the data on this page
The figures come from a small made-up dataset, printed above in full so you can check every number by hand. Download it as daily.csv. The results are computed from those rows when the page is built, and the Excel formulas, SQL and Python on these pages are run against the same rows before publishing, so the code and the answers agree.
To get these numbers from your own data without writing formulas, try AnalyzeIt: ask in plain words and it shows the working behind every figure.
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