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The Transfer Window
Data Analysis · Scatterplots & Correlation
The deadline is in 90 minutes. Coastal FC has one shot at signing a striker before the window slams shut, but the owner won't spend a cent without hard data.

You're the club's analyst. Six data puzzles stand between you and the deal of the season. Crack them all to get the transfer over the line. 📋
PROGRESS 0 / 6 complete
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1
Scout Report
Active
The head scout has been tracking 6 strikers. He measured the correlation (r) between each player's weekly training hours and their goals scored this season.
Luca (IT):r = 0.88
Bjorn (SE):r = −0.91
Rodrigo (BR):r = 0.95
Hamid (MA):r = 0.63
Sven (DE):r = −0.14
Omar (NG):r = 0.72
The owner only wants strikers whose data shows a strong positive linear relationship (r ≥ 0.75). How many players qualify?
Look at all 6 r values. Which ones are positive AND have a value of 0.75 or higher? Count those.
2
The Model
Locked
The scouts have zeroed in on two candidates: Luca and Rodrigo. For Rodrigo, the analyst builds a regression model based on this season's data.
Regression equation:Goals = 3x + 2
x =training hours/week
Rodrigo trains:8 hours/week
Using the regression equation, predict how many goals Rodrigo would score this season.
Substitute x = 8 into Goals = 3x + 2. Multiply first, then add.
3
Does He Deliver?
Locked
The owner wants to know if Rodrigo is the kind of player who over-delivers, or if he underperforms relative to what the data predicts.
Model predicted:26 goals
Rodrigo actually scored:29 goals
Residual =actual − predicted
Calculate Rodrigo's residual. A positive residual means he over-delivers the model, a negative means he underperforms it.
Residual = actual − predicted = 29 − 26 = ?
4
The Slope
Locked
The club's fitness director wants to understand the model better before they push Rodrigo's training schedule up after signing him.
Regression equation:Goals = 3x + 2
According to the model, how many additional goals per season does Rodrigo score for each extra hour of training per week?
In y = 3x + 2, the gradient (slope) is the number multiplied by x. It tells you how much y changes for each 1-unit increase in x.
5
The Target
Locked
The club needs to win the league this season. Based on historical data, their striker needs to hit 41 goals to give them a real shot at the title.
Regression equation:Goals = 3x + 2
Target goals:41
How many training hours per week does the model predict Rodrigo will need to score 41 goals? Round to the nearest whole number.
Set Goals = 41. So 41 = 3x + 2. Subtract 2 from both sides, then divide by 3. Round your answer.
6
The Rival
Locked
There's one final complication. A rival club is after the same striker. Their performance analyst has a different model for Rodrigo, built from older data. The two clubs are debating which model is more reliable.
Coastal FC model:Goals = 3x + 2
Rival club model:Goals = 2x + 8
At how many training hours per week do both models predict the same number of goals? (Solve simultaneously, set the two equations equal.)
Set them equal: 3x + 2 = 2x + 8. Subtract 2x from both sides, then subtract 2 from both sides.
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Transfer Complete!

Rodrigo signs for Coastal FC! Your data analysis convinced the owner, six challenges cracked, deadline beaten.

You've used scatterplots, Pearson's r, regression equations, residuals and simultaneous equations. That's a big chunk of the Data Analysis section done.

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