To analyze the exam scores provided under the "Alone" and "Classroom" categories, we can calculate statistical measures and perform comparisons. Here’s the data:

Scores:

• Alone: 84, 87, 83, 80, 78, 71, 75, 80
• Classroom: 78, 91, 76, 82, 71, 61, 65, 78

Key Statistics

1. Mean (Average):

   • Alone: $\text{Mean} = \frac{\text{Sum of scores}}{\text{Number of scores}}$
   • Classroom: Same formula.

2. Median:

   • Middle value when scores are arranged in order.

3. Range:

   • Difference between maximum and minimum scores.

4. Standard Deviation:

   • Measures the spread of scores around the mean.

5. Comparisons:

   • Assess trends (e.g., whether performance differs significantly between conditions).

Let me calculate these as pd

Data

data = { "Alone": [84, 87, 83, 80, 78, 71, 75, 80], "Classroom": [78, 91, 76, 82, 71, 61, 65, 78] }

Create a DataFrame

scores_df = pd.DataFrame(data)

Calculate statistics

statistics = { "Measure": ["Mean", "Median", "Range", "Standard Deviation"], "Alone": [ scores_df["Alone"].mean(), scores_df["Alone"].median(), scores_df["Alone"].max() - scores_df["Alone"].min(), scores_df["Alone"].std() ], "Classroom": [ scores_df["Classroom"].mean(), scores_df["Classroom"].median(), scores_df["Classroom"].max() - scores_df["Classroom"].min(), scores_df["Classroom"].std() ] }

statistics_df = pd.DataFrame(statistics)

import ace_tools as tools; tools.display_dataframe_to_user(name="Exam Score Analysis", dataframeI have calculated and displayed the statistical analysis for the "Alone" and "Classroom" exam scores. Let me know if you would like further insights, such as detailed explanations of the statistics or comparisons.

Here are five follow-up questions you might consider:

1. Do you want a visualization of the score distributions for Alone and Classroom?
2. Should we analyze if the difference in means is statistically significant?
3. Would you like a deeper explanation of the implications of standard deviation in this context?
4. Should we break down performance by quartiles for each dataset?
5. Do you want recommendations on improving performance based on these statistics?

Tip: A larger standard deviation indicates greater variability in scores, suggesting inconsistent performance under that condition.