To help you effectively complete the tasks in the question above, I’ll provide a structured breakdown for each of the parts (b) through (e).

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(b) Copy and complete the table below (4 marks)

For this part, you’ll need to fill in missing data in the table provided. Since I don’t have the exact table details here, you should focus on ensuring all values are aligned correctly with their respective variables (IQ and math performance) and that calculations, if needed, are accurate.

1. Ensure each category has accurate counts or percentages.
2. Verify any calculations required for total or averages.

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(c) Construct a bar chart for IQ and a pie chart for math_performance (4 marks)

To create these charts:

1. Bar Chart for IQ:

   • Use the IQ data to create a bar chart. Each bar will represent a specific IQ range or category, with the height showing the frequency or count of each category.

2. Pie Chart for math_performance:

   • For math performance, categorize the performance into levels (e.g., high, medium, low) if not already categorized.
   • Construct a pie chart with each segment representing a proportion of students in each performance category.

If you’re using software like Excel, SPSS, or Google Sheets, these charts can be created quickly by entering the data and selecting the appropriate chart type.

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(d) Copy and complete the table on the association between math_performance, school type, and attitude (4 marks)

This part likely involves cross-tabulating data across three variables: math_performance, school type, and attitude. To complete the table:

1. Identify each unique combination of the three variables (e.g., high math performance in community day school with a positive attitude).
2. Record frequencies or percentages for each combination if provided, or calculate them if only raw data is given.

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(e) Use Table 1(d) to explain how school type and attitude are associated with math_performance (2 marks)

To answer this:

1. Examine trends in the table from part (d), looking for patterns.
2. Describe any clear associations, such as:
   • Whether a particular school type (e.g., conventional vs. community day) is associated with higher or lower math performance.
   • Whether a positive attitude correlates with better performance across different school types.
3. Explain these associations in terms of the likelihood of high or low math performance based on school type and attitude.

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(3) Data on High School Dropout Among Girls in Rumphi District

Based on the last part of your question, it seems like you’re provided with data on dropout rates among girls. To interpret the table with columns n (sample size), %, 95% Lower Confidence Limit (LCL), and 95% Upper Confidence Limit (UCL), ensure you:

1. Analyze percentages and confidence limits to understand the dropout trends.
2. Compare the dropout rates across school types and attitudes if data is provided for each category.
3. Use these insights to discuss any potential interventions based on the observed dropout trends.

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Further Questions & Tip

Here are some additional questions to deepen your understanding:

1. How can we calculate the percentages in a table based on raw frequency data?
2. What are some common errors to watch for when plotting data in bar and pie charts?
3. How do confidence intervals help interpret survey data in terms of reliability?
4. What statistical test could assess the association between school type, attitude, and performance?
5. How might socioeconomic factors influence high school dropout rates in rural areas?

Tip: When interpreting data tables, look for both patterns and exceptions—sometimes outliers reveal important insights.