Description
Analyzing Student Exam Scores
Task Description:
In this project, you will use Python to analyze a dataset containing student exam scores. You will perform simple statistical calculations, create basic visualizations, and draw insights from the data.
Task Requirements:
1. Dataset (2 points):
· using provided dataset with a CSV file containing student information, including their names, ages, study hours, and exam scores. This dataset will serve as your data source.
2. Data Loading (2 points):
· Use Python to load the dataset from the CSV file into your program.
3. Statistical Analysis (4 points):
· Calculate the average (mean) exam score of the students.
· Calculate the highest and lowest exam scores.
· Calculate the standard deviation of exam scores to measure the spread.
· Median Score: Calculate and report the median exam score for the students in the dataset.
· Quartiles: Calculate and report the first quartile (Q1) and the third quartile (Q3) of the exam scores. Explain the insights of it.
· Interquartile Range: and report the Interquartile Range (IQR).
· Correlation Analysis: Calculate the correlation coefficient between study hours and exam scores. The correlation coefficient measures the strength and direction of the linear relationship between two variables. Interpret the correlation result.
· Hypothesis Testing: Formulate a hypothesis related to the dataset. For example, you could hypothesize that students who study more hours achieve higher exam scores. Conduct a hypothesis test (e.g., t-test) to determine if there is a statistically significant difference in exam scores between groups (e.g., high study hours vs. low study hours). Explain the hypothesis test, report the results, and draw conclusions.
4. Data Visualization (3 points):
· Create a bar chart or histogram to visualize the distribution of exam scores.
· Create a scatterplot to visualize the relationship between study hours and exam scores.
· Box Plot: Create a box plot (box-and-whisker plot) to visualize the distribution of exam scores. The box plot displays the quartiles, median, and any potential outliers.
· Frequency Distribution: Create a frequency distribution of exam scores, categorizing scores into intervals (e.g., 0-10, 11-20, 21-30) and counting the number of students in each interval. Display the frequency distribution as a table or histogram.
5. Interpretation (3 points):
· In your report, explain the dataset and its variables (e.g., age, study hours, exam scores).
· Present the results of your statistical calculations and visualizations.
· Interpret what these results tell you about the students’ performance and study habits.
6. Report Structure (0 points):
· For each task, follow this outline to construct your report:
· 1.1. Dataset and Method of Calculation: Describe the dataset and explain how you loaded it into your program.
· 1.2. Code (or pseudocode): Share the Python code you used for calculations and visualizations.
· 1.3. Results & Plot: Present the results of your calculations and display the plots.
· 1.4. Conclusion (interpretation of results): Provide your interpretation of the findings and what they imply.
