INTERPRETING TABLES AND GRAPHS

22°C

Between 9am and 12pm, the temperature rose from 14°C to 22°C — an increase of 8°C in 3 hours, the steepest rise shown.

Highest: 26°C (3pm). Lowest: 8°C (6am). Range $=26-8=18°C$.

75 m

The balloon descends from 75 m to 45 m between minute 30 and minute 50 — a decrease of 30 m over 20 minutes. Rate $=30\div20=1.5$ m per minute.

The graph is flat between Hour 1 and Hour 3, with the water level staying at 45 cm.

A likely explanation is that the water supply was paused or turned off during that period (e.g. for a scheduled break, a valve being closed, or a technical issue), before filling resumed afterward.

A line graph directly shows the TREND and rate of change between data points (via the slope of each segment), and can suggest values BETWEEN measured points — a bar graph only shows isolated values at each point, with no visual sense of a continuous trend connecting them.

A bar graph is more appropriate for comparing separate, unrelated CATEGORIES (like sales by different named products, or votes for different candidates) where there's no meaningful 'in-between' value connecting one category to the next — a line connecting them would be misleading.

$$82-65=17 \text{ kg}$$

Average loss per week $=17\div5=3.4$ kg (over 5 intervals from Week 1 to Week 6). Predicted Week 8 weight: $65 - 2\times3.4 = 58.2$ kg.

27°C at 3pm, which is also the maximum.

$$\frac{14+18+23+27+22+16}{6}=\frac{120}{6}=20°C$$

$$\frac{27-23}{3}\approx1.33°C\text{/hour}$$

296 mg.

$$\frac{209}{500}\times100=41.8\%$$

Day 0-1: $500-420=80$mg. Day 1-2: $420-353=67$mg. NOT equal — so the loss is not a constant fixed amount each day.

Day 0-1: $\frac{80}{500}\times100=16\%$. Day 1-2: $\frac{67}{420}\times100\approx16\%$. These percentages ARE approximately equal — revealing that radioactive decay loses a roughly CONSTANT PERCENTAGE of the remaining amount each day (proportional/exponential decay), rather than losing a fixed amount, explaining why the graph curves rather than forming a straight line.

52 km total (final value, since start was 0).

Second journey final value is also 52km, confirming the same TOTAL distance. However, the shapes represent very different journeys: the FIRST graph (steep-then-flat) suggests fast travel early, slowing down later (e.g. starting energetic, then tiring or hitting traffic) — while the SECOND (flat-then-steep) suggests a slow start followed by a fast finish (e.g. a warm-up period followed by a sprint). Same total distance, but very different speed patterns throughout the journey.

Without axis labels, a reader has NO way to know what quantity is being measured (sales in dollars? units sold? something else?), what time period each point represents, or what SCALE the numbers are on — a visually clear trend is meaningless without this context, since the same shape could represent wildly different real situations depending on the actual units and values involved.

1) A label for the horizontal (x) axis, including units if relevant. 2) A label for the vertical (y) axis, including units. 3) A title or caption stating what the graph represents overall (and ideally the data source/time period).

Precise version: 'Temperature rose by $27-14=13°C$ over the 9-hour period from 6am to 3pm, an average increase of approximately $13\div9\approx1.4°C$ per hour.' This version specifies the exact magnitude of change, the time period, and an average rate — far more informative than 'goes up a lot'.

$$600\div8=75 \text{ mAh/hour}$$

Using the roughly linear trend, estimated hours for 550 mAh: $550\div75\approx7.3$ hours. However, real EV batteries typically charge FASTER when nearly empty and SLOWER as they approach full capacity (to protect battery health) — this means a simple linear extrapolation likely UNDERESTIMATES the true time needed near the end of charging, since the last portion of charge often takes disproportionately longer than the earlier, faster-charging portion.

Highest: July (7.1%). Change Jan?Nov: $5.0-4.2=0.8$ percentage points increase.

The claim is partially supported by the data — the rate DID decrease from July (7.1%) through September (6.3%) to November (5.0%). However, evaluating whether this is 'under control' requires more context: is this a recurring SEASONAL pattern (e.g. summer tourism jobs boosting July unemployment temporarily, then a normal autumn recovery), or a genuine structural improvement? Data from PRIOR years (to check for a repeating seasonal pattern) and data BEYOND November (to see if the improvement continues, plateaus, or reverses) would both be needed to properly assess whether this represents lasting progress.