INTERPRETING TABLES AND GRAPHS

78 mm

$$85+62+78+95+110=430 \text{ mm}$$

Wettest: May (110 mm). Driest: Feb (62 mm). Difference $=110-62=48$ mm.

30 students

$$\frac{13}{100}\times100=13\%$$

Grade 9: $\frac{18}{50}\times100=36\%$. Grade 10: $\frac{15}{50}\times100=30\%$. Grade 9 is higher, by $36-30=6$ percentage points.

2019?2020: $\frac{195-210}{210}\times100=-7.14\%$ (a decrease). 2022?2023: $\frac{310-265}{265}\times100=16.98\%$ (an increase).

2020?2021: $\frac{230-195}{195}\times100=17.9\%$. 2021?2022: $\frac{265-230}{230}\times100=15.2\%$. After the initial dip, the company shows strong, fairly consistent double-digit percentage growth each year from 2020 onward.

Grade 9 has more students walking (8) compared to Grade 10 (5).

Find the row labelled with the category you want (e.g. 'Grade 10'), then move along that row until you reach the column labelled with the second category (e.g. 'Walk') — the value at that exact row-column intersection is the answer. Reading carelessly or mixing up rows and columns is a common source of errors.

$$950+420+180+250+200=2000$$

Yes, they currently spend \$2000 of \$2200 income (\$200 left over). To increase Savings from \$250 to \$400 (a \$150 increase) while keeping total spending at or under \$2200, they could use their current \$200 surplus — no other category needs cutting, since $2000+150=2150 \le 2200$.

Cairo (9.4 hours) has the most. Excess over Reykjavik (3.0, the least): $9.4-3.0=6.4$ hours.

$$\frac{5.8+3.4+9.4+6.9+3.9+3.0}{6}=\frac{32.4}{6}=5.40 \text{ hours}$$

Tokyo (5.8) and Cairo (9.4) and Nairobi (6.9) qualify — the three cities with values above 5.5.

$$\frac{680}{820}\times100\approx82.9\%$$

Under 18: $\frac{420}{600}\times100=70\%$. 18-40: $\approx82.9\%$ (from part a). Over 40: $\frac{510}{580}\times100\approx87.9\%$. The Under 18 group has the LOWEST rate, at 70%.

2018?19: $\frac{68-45}{45}\times100\approx51.1\%$. 2019?20: $\frac{102-68}{68}\times100\approx50.0\%$. 2020?21: $\frac{178-102}{102}\times100\approx74.5\%$. 2021?22: $\frac{295-178}{178}\times100\approx65.7\%$. 2022?23: $\frac{412-295}{295}\times100\approx39.7\%$.

The rates don't show a simple consistent trend — they rise (51.1%?50.0%?74.5%) then fall (74.5%?65.7%?39.7%), suggesting the growth RATE itself peaked around 2020-2021 and has since been slowing, even though sales in absolute terms are still increasing every year.

No — the most RECENT data (2022?2023 growth of only 39.7%, down from a peak of 74.5%) suggests the growth rate is DECELERATING, not sustaining indefinitely. This is a common real-world pattern for emerging technology adoption (rapid initial growth eventually slows as the market matures), so extrapolating the early high growth rates far into the future would likely overestimate future sales.

$$\frac{1610}{2000}\times100=80.5\%$$

A policy-maker seeing only 80.5% might assume vaccination coverage is fairly uniform, missing that the UNDER-18 group is significantly BEHIND (70%) compared to older groups (up to 87.9%) — this detail matters because it identifies a SPECIFIC group that may need targeted outreach or a different vaccination strategy, information that's completely hidden by a single averaged summary statistic.

28 is drastically lower than both neighbouring months (340 and 355) — a sudden 92%+ drop followed by an immediate, complete recovery to normal levels the very next month is highly unusual for typical sales patterns. A far more likely explanation is a DATA ENTRY ERROR (e.g. a missing digit — perhaps '280' was mistyped as '28', or a decimal/typo issue), rather than a genuine one-month sales collapse with no lasting cause.

Region A: $\frac{4.2\text{ million}}{850}\approx4941$ people/km². Region B: $\frac{2.8\text{ million}}{3200}=875$ people/km². Region C: $\frac{6.5\text{ million}}{1450}\approx4483$ people/km². Region A is most densely populated.

While Region C does have the highest RAW population (6.5 million), Region A actually has the highest DENSITY (?4941 people/km², compared to Region C's ?4483). Density is often more relevant for infrastructure planning because it reflects how CROWDED an area actually is — a region with a large population spread over a huge area may have plenty of room to expand housing/transport, while a smaller-population but highly dense region may face more acute overcrowding pressures per square kilometre, even with fewer total residents.