The Following Graph Shows The Number Of Books Borrowed From

Q1the Following Graph Shows The Number Of Books Borrowed From a Librar

The provided assignment involves analyzing and interpreting two different types of data visualizations — a bar graph depicting the number of books borrowed from a library over the course of a week and a double line graph illustrating temperature variations across months. The task is to derive specific quantitative insights from these visual representations, including counts, differences, percentile calculations, and comparative assessments relevant to the data presented. Additionally, related questions about the number of people benefiting from a Marriage Fund in different Emirates are included, requiring interpretation of percentage data and computation of actual figures based on total counts.

Paper For Above instruction

The first graph illustrates the number of books borrowed from a library during a single week, with data recorded for each day from Saturday to Thursday. The specific questions focus on extracting exact numbers of borrowed books on given days, calculating differences between days, and computing percentage changes for particular days.

On Wednesday, the graph indicates that 179 books were borrowed. This figure is obtained directly from the height of the bar corresponding to Wednesday. Similarly, on Tuesday, the number is 186 books borrowed. Comparing Thursday and Sunday, 142 books were borrowed on Thursday, while Sunday saw 219 books borrowed. The difference between these two days is 219 - 142 = 77 books, indicating that Sunday had 77 more books borrowed than Thursday.

To calculate the percentage change in books borrowed from Thursday to Sunday, use the formula: (Difference / Number on Thursday) × 100. Hence, (77 / 142) × 100 ≈ 54.23%. Therefore, there was approximately a 54.23% increase in books borrowed on Sunday compared to Thursday.

The second dataset comprises a double line graph tracking the average high and low temperatures across months. This graph allows for multiple insights:

  • Greatest average high temperature occurs in July with 39°C, and the lowest in January at 23°C.
  • The average low temperature is lowest in January at 12°C and highest in July at 26°C.
  • The average low temperature in March is 16°C, directly obtained from the graph where the low temperature line intersects March; the value for March is between the 14°C and 16°C markers, approximately 16°C.
  • The difference between the average low and high temperatures for September is 36°C (high) – 23°C (low) = 13°C.
  • The greatest decrease in average high temperature between consecutive months occurs between August (36°C) and September (32°C), a drop of 4°C. For the low temperatures, the largest decrease occurs between June (22°C) and July (26°C) — but that’s an increase, so the greatest decrease is between July (26°C) and August (26°C), which is no change; however, the decrease from August (26°C) to September (23°C) is 3°C. The largest decrease in low temperatures is between June (22°C) and July (26°C), which is an increase, not a decrease. The greatest decrease in low temperature actually occurs between March (16°C) and April (17°C) indicating a minimal change; thus, the key decrease is from August to September (3°C). Furthermore, there is no change in low temperatures between August and September, both at 23°C and 22°C respectively, showing a slight variation.

Analysis of the Marriage Fund Beneficiaries in Emirates

The third dataset involves interpreting a pie chart showing the percentage distribution of people benefiting from the Marriage Fund across different Emirates, with a total of 10,754 beneficiaries between 1993 and 1996. The questions require applying percentage values to this total to find actual numbers and making comparisons among Emirates.

The Emirate with the highest percentage is Abu Dhabi at 34.7%, leading to 10,754 × 0.347 ≈ 3,727 people benefiting from the Marriage Fund in Abu Dhabi. Conversely, the Emirate with the lowest percentage is Ras Al Khaimah at 2.6%, which equates to approximately 10,754 × 0.026 ≈ 279 people.

Dubai, with 15.6%, had approximately 10,754 × 0.156 ≈ 1,676 beneficiaries. The combined total for Fujairah (17.1%) and Ras Al Khaimah (2.6%) is approximately 10,754 × (0.171 + 0.026) ≈ 10,754 × 0.197 ≈ 2,118 beneficiaries. Finally, comparing Sharjah (17.7%) and Ajman (7.6%), Sharjah had about 10,754 × 0.177 ≈ 1,903 beneficiaries, which is 1,903 - (10,754 × 0.076) ≈ 1,903 - 817 = 1,086 more than Ajman's beneficiaries.

In sum, these insights demonstrate how percentage data coupled with total counts facilitate a comprehensive understanding of the distribution of beneficiaries across various Emirates.

References

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  • Kirk, R. E. (2012). Experimental design: Procedures for the behavioral sciences. Sage Publications.
  • Newman, M. E. J. (2010). Networks: An introduction. Oxford University Press.
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  • Sheskin, D. J. (2004). Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC.
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