How to Calculate the Sum of Squared Residuals (SSR) Without a Math PhD - Math Calculator Tutorial | SumCalculator

TL;DR Scroll to the step-by-step example if you just spilled coffee on your laptop and need the formula now. Everything else is context, nuance, and the occasional snarky side-comment.

Why You Should Care (Even If You Slept Through Stats Class) #

The sum of squared residuals (SSR), sometimes called the residual sum of squares (RSS), measures how far your predicted values stray from reality. In plain English: it’s your model’s total error bill. A small SSR means your line of best fit is hugging the data like a clingy koala. A large SSR? That line is more like a bored cat that wandered off.

If you do anything with forecasts—sales, weather, fantasy football—you eventually need to measure how wrong you are. SSR turns that vague “hunch” into a hard number you can optimize.

Need a quick refresher on basic sums before we get fancy? Pop over to our Sum Calculator. For deeper dives into dispersion, bookmark the variance calculator and our weekly rants on the blog.

What Exactly Is a Residual? #

A residual is the difference between an observed value (y_i) and its corresponding predicted value (ŷ_i).

Residual_i = y_i - ŷ_i

Why square them? Because positives and negatives would cancel out if we just summed raw residuals. Squaring also punishes big mistakes more severely—think of it as a disciplinary fine for way-off predictions.

Formula Cheat-Sheet #

SSR = Σ(y_i - ŷ_i)² for i = 1 to n

If you already have residuals listed in a column (e_i):

SSR = Σ(e_i²) for i = 1 to n

Pretty simple—until typos, hidden rows, or rogue text strings derail your spreadsheet.

Before you panic-search yet another tutorial, you can always sanity-check totals with our online sum tool—handy when Excel decides to crash right before the deadline.

Step-by-Step Example #

Imagine you’ve built a linear model to predict monthly website visits based on ad spend. Here’s a tiny dataset (because you don’t need to see all 10,000 rows—I’m kind, not cruel).

Month	Actual Visits	Predicted Visits
Jan	2,100	2,000
Feb	2,500	2,400
Mar	1,800	1,950
Apr	2,300	2,150
May	2,900	3,050

1. Compute residuals
e_i = y_i - ŷ_i

Month	Actual	Predicted	Residual
Jan	2,100	2,000	100
Feb	2,500	2,400	100
Mar	1,800	1,950	−150
Apr	2,300	2,150	150
May	2,900	3,050	−150

2. Square each residual

100² = 10,000
(-150)² = 22,500

Month	Residual	Squared
Jan	100	10,000
Feb	100	10,000
Mar	−150	22,500
Apr	150	22,500
May	−150	22,500

3. Sum the squares

10,000 + 10,000 + 22,500 + 22,500 + 22,500 = 87,500

SSR = 87,500. That’s your error budget—file it away for model comparison later.

Doing It in Excel #

Residual column:
```
=B2 - C2
```
Squared residuals:
```
=D2^2
```
Total:
```
=SUM(E2:E6)
```

Pro tips

Absolute references: If your predicted values live elsewhere, lock them with $ to avoid reference drift.
Filters messing up totals? Use SUBTOTAL(109,E2:E1000) so hidden rows don’t sabotage your sum.
Dynamic arrays (Excel 365): Wrap residual calculations in LET for cleaner one-cell formulas:
```
=LET(
    resid, B2:B6 - C2:C6,
    SUMSQ(resid)
)
```
Less scrolling, more coffee.

Need a refresher on plain old addition in Excel first? Check our tutorial How to Total Numbers in Excel—written for people who are “under deadline and losing it.”

Lightning-Fast Python Snippet #

If you’re crunching data in Jupyter:

import numpy as np

actual = np.array([2100, 2500, 1800, 2300, 2900])
pred = np.array([2000, 2400, 1950, 2150, 3050])

ssr = np.sum((actual - pred) ** 2)
print(ssr)  # 87500

That’s literally two lines of code and one sarcastic comment away from production.

How to Interpret SSR #

Absolute number, not percentage. Compare SSR only against alternative models built on the same scale.
Lower is better all else equal. But beware of overfitting—adding zillions of predictors can artificially shrink SSR without improving real-world accuracy.
R-squared tie-in. SSR is the numerator in the formula R² = 1 - (SSR/SST). If SSR plummets while total variance (SST) stays fixed, your R² climbs—and the executive dashboard lights up green.

Want a gentle intro to variance before you jump to R²? The variance calculator has your back (and no annoying sign-ups).

Common Pitfalls and Debugging Tips #

Pain Point	Symptom	Fix
Mixed data types	SSR returns `#VALUE!` in Excel	Convert text to numbers (`Data → Text to Columns` Finish)
Hidden rows	“Why is my SSR wrong?”	Swap `SUM` for `SUBTOTAL(109, …)`
Outliers	SSR skyrockets	Investigate or winsorize extreme values; report SSR with and without them
Over-fitted model	SSR near zero in sample, huge errors on new data	Use cross-validation, hold-out sets, or penalized regression
Scale differences	Comparing SSR across units (e.g., dollars vs. percentages)	Standardize variables or use metrics like RMSE

Still stuck? Skim our troubleshooting checklist on the about page where we document every spreadsheet mishap we’ve had since Y2K (it’s therapeutic).

Metric	Formula	Best For
SSR	`Σ(y_i - ŷ_i)²`	Raw model error
MSE / RMSE	`SSR/n` (or square root)	Scale-adjusted error; easier to interpret
MAE	`(1/n)Σ	y_i - ŷ_i
MAPE	`(100/n)Σ	(y_i-ŷ_i)/y_i

SSR is the granddaddy—simple, brutal, and a prerequisite for everything else. Learn it once, reap the benefits forever.

Wrap-Up #

Understanding how to calculate the sum of squared residuals is like learning to balance your checkbook before dabbling in cryptocurrency. Once you can tally model error with confidence, you unlock slews of fancy diagnostics—R², F-tests, cross-validation—you name it.

Bookmark this guide, share it with that colleague who still copies formulas cell-by-cell, and remember: if Excel melts down five minutes before the big meeting, our online tools are always open, well-behaved, and 100% virus-free.

Happy forecasting—and may your SSR be ever shrinking.