Teaching sequence

Work through this resource material in the following sequence:

10 minutes

Tune In: Renewable energy targets

15 minutes

Find Out: Calculating mean, median, mode and range

30 minutes

Take Action: Analysing data

5 minutes

Reflection

Learning intentions and success criteria

Students will:

evaluate numerical data sets through an investigation of renewable energy projects.

Students can:

calculate the mean, median, mode and range for numerical data
evaluate the influence of outliers on measures of centre
make conclusions using statistical values in a real-world context.

Tune In: Renewable energy targets

Step 1

Display the following graph for your class using the attached Visual Explainer, which shows the proportion of energy generated by renewable resources as a proportion of total energy generation in each state of Australia in 2022 and 2023 (note: 2023 data is orange).

Source: Clean Energy Australia 2024, Clean Energy Council, p26

Ask students what they can tell about Australia's renewable energy generation from this graph.

What value represents the Australian average?
What states are performing below average? Above average?
What can be deduced about Australia's progress from the data?

Step 2

Display or read the following quote to students from the Clean Energy Australia 2024 Report:

‘Queensland Energy and Jobs Plan, released Sept 2022, targets 70% renewable energy by 2032 and 80% by 2035. Announced in 2023, it will be enshrined into law, along with the state’s renewable energy targets.’

Step 3

Pose the following questions to students and ask them to participate in a ‘Think, Pair, Share’ activity:

Question	Suggested Responses
Why is it important that we increase our renewable energy usage?	• reduce the greenhouse gas emissions into the atmosphere • reduce the impact of human activity on climate change • reduce the cost of energy prices.
How can we predict if Queensland will meet these renewable energy targets?	• measure Queensland’s current renewable energy usage • investigate future renewable energy projects that are being planned or built • investigate if Queensland’s renewable energy usage is increasing or decreasing each year

Extension opportunity: Check out page 26 of the Clean Energy Australia Report 2024, What was the percentage increase in energy generation from renewable resources in Queensland from 2022 to 2023? If Queensland continues at the same levels of progress, will it reach the 2032 and 2035 targets? Also check out page 12 of the Clean Energy Australia Report 2024. How much investment in renewable projects does Queensland have, and how does this compare to other states?

Find Out: Calculating mean, median, mode and range

Step 1

After discussing possible ways to predict if Queensland will meet its renewable energy targets, explain to students that a set of data can be summarised using key statistical values that measure the data’s centre and spread, which are important tools in making predictions. Being able to summarise large sets of data like this can help identify patterns and trends, such as whether renewable energy generation is increasing or decreasing over time. The statistical values that measure the centre and spread are the mean, median, mode and range.

Step 2

Distribute 1 copy of the Student Worksheet to each student and conduct the following as worked examples, calculating the mean, median, mode and range for the cost of each farm. Encourage students to write down the working out for each. You might like to complete the first example as a class and get your students to complete the rest individually.

The details for 5 large-scale Queensland wind farms are shown below:

Renewable energy type	Name	Energy capacity (megawatts)	Project cost (millions $AUD)
Onshore wind	MacIntyre Wind Farm	1026	1960
Onshore wind	Clarke Creek Wind Farm Stage 1	450	1000
Onshore wind	Coopers Gap Wind Farm	453	850
Onshore wind	Boulder Creek Wind Farm	372	750
Onshore wind	Mount Hopeful Wind Farm	330	750

Data source: Clean Energy Council

Note: Some of these wind farms are in the planning stage, whilst others are operational.

For the cost of these 5 wind farms, calculate the:

a) Mean

b) Median

c) Mode

d) Range

What is a megawatt? Drawing a comparison between a standard 60-watt light globe may be useful here - a 60-megawatt power station would be able to power one million of these light globes.

Step 3

Post the following questions to students and discuss:

Do you think the mean or the median is a better representation of the cost for wind farms in Queensland?
Is there a value that may be affecting the mean value?

Suggested responses:

Identifying the difference between the mean and median
Identifying the MacIntyre Wind Farm as an outlier, as its cost is close to double that of the next largest
Discussing the impact that the outlier has on both the mean and the median.

Step 4

Explain to the students the significance of outliers and their effects on how measures of centre are interpreted. For large data sets, the median is often the preferred choice as it isn’t as affected by outliers.

Links to common statistical values such as median house prices or median salaries may promote understanding. For example, the median salary of an Amazon worker is approximately $30,000 per year, whereas the CEO Jeff Bezos, makes approximately $1,681,840 per year. The CEO’s salary would be considered an outlier, which would likely affect the mean salary of Amazon’s employees. Therefore, the mean salary does not truly reflect how much Amazon employees are paid. Hence, the median salary is chosen as the appropriate measure of centre.

Take Action: Analysing data

Step 1

Introduce students to the Australian Renewable Energy Projects F actsheet by either providing the link or printed copies for students to share. Draw out students’ observations of the data with the following questions:

What information does this table give us?
What does the ‘Capacity (megawatts)’ column mean?
Do you recognise any of the project names?

Step 2

Explain to students that their goal is to use this data to answer the research question: ‘Is Australia heading in the right direction to ensure that renewable energy targets will be met?’ This will be done by completing the questions below which can be found on the Student Worksheet.

a) Calculate the statistical values for the energy capacity (in megawatts) for the Australian renewable energy projects completed in 2021 and 2023, and fill in the table below.

Energy capacity (megawatts) of completed Australian renewable energy projects

	2021	2023
Mean
Median
Mode
Range
Possible outliers

b) Create a histogram for the 2 data sets by hand, Excel, or another form of digital tool.

You might like students to use the following website (https://www.socscistatistics.com/descriptive/histograms/default.aspx) to create their histograms or alternatively watch this video that shows you to create a histogram in excel.

Suggested responses:

c) On the histograms, locate where the centre of the data is. Compare this to calculated values for the mean and median, and explain which of the 2 best represents the centre of the data.

d) Based on the statistical values calculated, is Australia heading in the right direction to ensure that renewable energy targets will be met? Use your data to justify the response.

Extension: Use the last 2 pages on the Australian Renewable Energy Projects factsheet to explore Queensland's large-scale renewable energy projects to investigate the following research question:

‘What is the link between energy Capacity and the cost for Queensland’s renewable energy projects?’

Note: Encourage students to use a scatterplot to analyse this data set and uncover patterns and trends.

Step 3

Students will need access to a laptop or device to view the data and to use software to create their histogram. Encourage students to use the worked examples to help them with their calculations.

Step 4

Bring the class back together to discuss their findings. Re-address the research question: ‘Is Australia heading in the right direction to ensure that renewable energy targets will be met?’ Pose the following questions to guide students through their findings.

What effect did outliers have on the data?
Did you learn anything new about the data after creating the histogram?
What were some key differences between the 2021 and 2023 data?
What was your answer to the research question?
Did you try the extension? What did you find?

Step 5 (Optional)

Explain to students that data analysis is an incredibly important aspect of many different roles and careers, especially in the Clean Energy sector. Ask students: ‘Why would collecting and analysing data such as the data featured in the lesson be an important part of this industry?’

Suggested responses:

Collecting and analysing data on clean energy projects helps track progress toward goals like Queensland’s 2032 renewable energy targets. It shows trends, helps predict future results, and guides better decision-making, making sure resources are used wisely. Data also reveals the environmental, economic, and social impacts of projects and identifies any unusual results that need further investigation. By understanding this information, governments and planners can adjust their strategies to ensure a successful switch to cleaner energy, reduce greenhouse gas emissions, and manage costs and benefits effectively.

Invite students to explore the Future Energy Jobs Guide to find what roles and careers rely on the use of data to make decisions in their work. Encourage students to see the importance of collecting and analysing data in all types of roles, from corporate, scientists, engineers and technicians.

Reflection

Revisit the quote from the Clean Energy Australia 2024 Report:

‘Queensland Energy and Jobs Plan, released Sept 2022, targets 70% renewable energy by 2032 and 80% by 2035. Announced in 2023, it will be enshrined into law, along with the state’s renewable energy targets.”

Based on the data investigated in this lesson and the statistical values that were calculated, do you think these are realistic targets?

Statistical Insights into Renewable Energy

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