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The problem involves Ms. Susan's test scores for a science class, and she is considering removing the score of 25 from the dataset before analyzing the data. The task is to determine how removing this score impacts the data by comparing different box plots.

The scores listed are:

\[
25, 60, 63, 65, 73, 77, 80, 85, 86, 88, 89, 90, 92, 95, 95
\]

### Step 1: Analyze the current dataset

- **Minimum**: 25
- **Maximum**: 95
- **Median (Middle Value)**: 85
- **Lower Quartile (Q1)**: The median of the lower half of the data, excluding the median, which is 65
- **Upper Quartile (Q3)**: The median of the upper half of the data, which is 90

### Step 2: What happens if the score of 25 is removed?

Removing 25 will likely affect the minimum value, shifting it from 25 to 60. Other measures, such as quartiles and the interquartile range (IQR), may also change, resulting in a boxplot that shows this shift in the dataset.

### Step 3: Identify the correct boxplot

You are asked to choose a boxplot that reflects the removal of the 25 score. This will mainly result in:
- A higher minimum (at 60)
- Likely shifts in Q1, Q3, and the median
- A reduction in the overall spread of the data

I recommend selecting the boxplot that shows:
- A minimum of 60 instead of 25
- Changes in the distribution as explained above.

Do you want to explore this further with specific calculations on the dataset?

---

Here are some questions to extend your understanding:

1. How is the interquartile range (IQR) affected when removing outliers from a dataset?
2. Why is it important to consider removing outliers when analyzing data distributions?
3. What does a boxplot tell us about the symmetry or skewness of a dataset?
4. How would removing an outlier like 25 affect the mean and median of this dataset?
5. How can quartiles help to understand the spread of data in a boxplot?

**Tip**: When removing an outlier from a dataset, observe how both the central tendency (like median) and spread (like range and IQR) are affected. This can provide insights into the nature of the data.

Ms. Susan is graphing the test scores for her science class. The scores are: [25, 60, 63, 65, 73, 77, 80, 85, 86, 88, 89, 90, 92, 95, 95]. After analyzing the data, she is considering removing the score of 25. The task is to determine how removing the score of 25 impacts the data by observing the changes in the boxplot.

Explore how removing the lowest score of 25 from Ms. Susan's science class test scores impacts the boxplot. Learn key statistical concepts like median, quartiles, and the interquartile range (IQR) while visualizing the changes in the data distribution.

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