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We are given the regression equation:

\[
y = -630 + 5200x
\]

where \(y\) represents the number of runs scored, and \(x\) represents the team's batting average.

The batting average provided is \(x = 0.235\). We need to substitute this value into the equation to find the expected number of runs scored.

\[
y = -630 + 5200 \times 0.235
\]

Let's calculate it step by step:

1. Multiply \(5200\) by \(0.235\):
   \[
   5200 \times 0.235 = 1222
   \]

2. Add this result to \(-630\):
   \[
   y = -630 + 1222 = 592
   \]

So, the expected number of runs scored is **592**.

Would you like more details or have any questions?

Here are five additional questions you could explore:

1. How would the prediction change if the batting average increased to 0.250?
2. What is the meaning of the slope \(5200\) in this context?
3. If a team's batting average is very low, say 0.150, how many runs would they be expected to score?
4. How does the regression line help in predicting future performance?
5. What is the intercept \(-630\) representing in this model?

**Tip**: Always round carefully when interpreting regression models, especially for predictions with real-world implications!

Suppose the equation for the regression line for the number of runs scored in a season, y, is given by y = -630 + 5200x, where x is the team's batting average. For a team with a batting average of 0.235, find the expected number of runs scored in a season. Round your answer to the nearest whole number.

Learn how to predict the expected number of runs scored by a baseball team using a linear regression equation. For a team with a batting average of 0.235, the expected runs are calculated to be 592.

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