Question

Use the data to construct a scatter plot in the
space at the right, comparing the time Romy
practices on her own to the number of baskets
she makes in team practice the following day.
Part B
What association is there between the data,
if any?

Answer 1

I don't see the data in your message. Please paste the list/table of Romy's practice times and the next-day baskets (or a picture of it). Once you do, I can (a) give you the exact ordered pairs to plot, (b) draw or generate the scatter plot, and (c) describe the association.

While you paste the data, here are clear steps and tips so you can make the scatter plot and determine the association yourself:

1. Prepare the ordered pairs
- Let x = minutes Romy practices on her own.
- Let y = number of baskets she makes in team practice the following day.
- Each observation is an ordered pair (x, y). Example: if she practiced 12 minutes and made 8 baskets, that point is (12, 8).

2. Make the scatter plot by hand
- Draw horizontal axis (x, minutes practiced) and vertical axis (y, baskets made).
- Choose scales that cover the range of your x and y values with even tick marks.
- Plot each (x, y) point.

3. Make the scatter plot with tools
- Excel/Google Sheets: put x values in one column and y values in next column → Insert → Scatter chart.
- Python (quick example):
```
import matplotlib.pyplot as plt

x = [/* paste practice minutes */]
y = [/* paste baskets next day */]

plt.scatter(x, y)
plt.xlabel("Minutes Romy practices on her own")
plt.ylabel("Baskets made in team practice next day")
plt.title("Romy: practice time vs. baskets made")
plt.grid(True)
plt.show()
```

4. Describe the association (what to look for)
- Direction:
- Positive association: points trend upward (more practice → more baskets).
- Negative association: points trend downward (more practice → fewer baskets).
- No association: no apparent trend.
- Form: roughly linear or curved.
- Strength:
- Strong: points lie close to a clear line/curve.
- Moderate: some scatter around a trend.
- Weak: points widely scattered.
- Outliers: any points that do not follow the general pattern.

5. Quantify it (optional)
- Compute the correlation coefficient r (Excel: =CORREL(range_x, range_y); Python: numpy.corrcoef(x,y)[0,1]). Interpretation:
- |r| near 1 → strong linear relationship
- |r| about 0.3–0.7 → moderate
- |r| < 0.3 → weak
- Sign of r indicates direction (positive/negative).

If you paste the data now, I will (1) list the ordered pairs, (2) draw or generate the scatter plot for you, and (3) state the association (direction, form, strength, and any outliers) with an estimated correlation.