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Data Trends Analysis

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Data Trends Analysis

Finn, Kahu and Arjun are reviewing records from a New Zealand weather station. Use the data below to describe patterns, identify unusual values and make cautious inferences. Remember: an association does not by itself prove cause.

A. Monthly rainfall

Rainfall was recorded at the station over one year. Values are monthly totals in millimetres (mm).

Month

Rainfall (mm)

Jan

Feb

Mar

Apr

108

May

126

Jun

143

Jul

137

Aug

119

Sep

Oct

Nov

Dec

Read the pattern

1.Which description best fits the rainfall pattern across the year?

It rises overall from summer to winter, then falls towards summer.
It increases steadily every month.
It falls overall from summer to winter, then rises towards summer.
There is no seasonal pattern.

2.Find the range of the monthly rainfall totals. Show how you calculated it.

3.Compare June and December rainfall. How many millimetres greater was the June total, and what does this suggest about the seasonal pattern?

4.Would a straight-line trend be a good summary of all 12 monthly totals? Explain your choice using evidence from the data.

B. Wind and temperature

On six afternoons, the team recorded wind speed and air temperature. Use the paired measurements to explore their relationship.

Observation

Wind speed (km/h)

Temperature (°C)

5.Describe the association between wind speed and temperature in these observations. Give two data points as evidence.

6.Sketch a scatter plot of the six wind-speed and temperature pairs. Label both axes with units, plot each point, and draw a sensible line of best fit.

Wind speed (km/h) on the horizontal axis; temperature (°C) on the vertical axis.

7.The afternoon measurements show an association. Can the team conclude that wind causes the temperature to drop? Explain one reason for your answer.

8.Finn wants to use the rainfall pattern to predict next January's rainfall. Give one reasonable informal inference and one limitation of that prediction.

9.Arjun checks the station log and discovers that the June rain gauge may have overflowed during a storm. How should this information affect the analysis?

Ignore the note; all recorded values must be correct.
Treat June's value as potentially incomplete, check or replace it if possible, and avoid drawing strong conclusions from it alone.
Change June's value to zero.
Delete every month from the dataset.