Analyzing the Influence of Program Type on Scores in LWCM302

Apr 5, 2024, 11:55 PM

generate a research question and an hypothesis based on the uploaded result

First, let's load and inspect the provided data to understand its structure and content. This will help us in formulating a research question and hypothesis.
Based on the data preview, we have scores for a course (LWCM302) across different students in various law programs. A potential research question could be:
Research Question: Does the program type (e.g., LLB Civil Law vs. LLB With Specialization) influence the scores in LWCM302?
Hypothesis: Students enrolled in LLB With Specialization programs score higher on average in LWCM302 than those in the LLB Civil Law program.

analysis the the data to answer the research question and test the hypothesis

First, we'll prepare the data by grouping it based on the Programme and calculating the average scores for each program. This will help us analyze if there's a significant difference in scores between different program types.

Analysis Summary

  • Research Question: Does the program type influence scores in LWCM302?
  • Hypothesis: Students in LLB With Specialization programs score higher on average than those in LLB Civil Law.

Findings

  • LLB Civil Law Average Score: 12.09
  • LLB With Specialization Average Score: 11.18

Conclusion

  • The data does not support the hypothesis. On average, students in the LLB Civil Law program scored higher than those in LLB With Specialization programs.

Explore factors influencing scores further

To explore factors influencing scores further, we'll conduct a more detailed analysis. This includes examining the distribution of scores across all programs and potentially identifying other variables that might impact scores, such as the distribution of scores within each program. Let's start by visualizing the score distributions.

Further Exploration Summary

  • Visualization: The box plot of LWCM302 scores by program shows the distribution of scores within each program.
  • Observations:
    • The score distribution varies between programs, indicating that program type may indeed influence scores.
    • However, the distribution also suggests variability within programs, pointing to other potential factors influencing scores.

Next Steps

  • To understand the factors influencing scores more comprehensively, further statistical analysis or the inclusion of additional variables (e.g., study habits, attendance) could be beneficial.

Investigate correlation between attendance and scores

To investigate the correlation between attendance and scores, we would need attendance data for each student alongside their scores. Since the current dataset only includes scores, we'll need additional data on attendance to proceed with this analysis.

Request attendance data for correlation analysis

Could you provide the attendance data for each student? This data is essential for investigating the correlation between attendance and scores.
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