This is a bubble chart analysis question - these multi-variable data interpretation problems can be tricky because you need to track what changes versus what stays fixed.
Let me walk you through the logical approach here.
Understanding What Each Variable Represents:First, let's identify what we're dealing with:
-
C = percent comedy in each preview (determined by the preview's content)
-
F = percent of audience who found it funny (depends on audience reaction)
-
L = likelihood to see the movie (shown by bubble size, also audience-dependent)
Question 1: What Could Differ with Different Audiences?Here's the key insight you need to see: if we show the
same previews to different test audiences, what's fixed versus what could change?
Think about it this way - the previews themselves don't change. So the percent of comedy in each preview (C) stays exactly the same. But different audiences might have different reactions to the same content.
Therefore:
-
C stays the same (preview content is fixed)
-
F could change (new audience might find different things funny)
-
L could change (new audience might have different likelihood to see the movie)
The answer is
only F and L could differ.
Question 2: Correlation Between Drama and "Finding Funny"Now let's tackle the relationship between D (percent drama) and F (percent finding it funny).
Since the previews are only comedy and drama: \(C + D = 100\)
This means \(D = 100 - C\)
Looking at the chart, notice how as C increases (moving right), F also increases (moving up). This shows a positive relationship between comedy and funny ratings.
But here's what you need to connect: if C and F move in the same direction (both increase together), and D is the opposite of C, then D and F must move in opposite directions.
When D increases, C decreases, which means F decreases too.
Therefore, there is
a negative correlation between D and F.
The complete systematic framework for tackling these variable relationship questions - including how to quickly identify fixed vs. variable components and master correlation analysis across different question types - is available in the
detailed solution on Neuron. You can also practice with comprehensive explanations for
similar official Data Insights questions to build consistency in your approach to these multi-variable analysis problems.