## Travel to School I

Purpose

In this unit students investigate methods of travel to school, using a spreadsheet to produce data displays and investigate distributions.

Specific Learning Outcomes
• plan an investigation;
• use spreadsheets to display and analyse data
• discuss features of data display
• compare features of data distributions;
Description of Mathematics

In this unit students will use Excel to enter discrete numeric data from the results of the last three New Zealand censuses into spreadsheets and generate bar graphs and pie charts. Using Excel to generate the graphs removes the complications of working with large numbers. Students will then discuss features of their graphs. The unit also gives the students the opportunity to plan and conduct an investigation. The Spreadsheet Tutorial is intended to support teachers who are unfamiliar with the use of spreadsheets.

Required Resource Materials
Spreadsheet Tutorial (see Related Resources if needed)

www.censusatschool.org.nz

Computer(s) with Excel Spreadsheet and Internet access

Tally sheet

Key Vocabulary

census, discrete numeric data, continuous numeric data, category data, collate, valid, validity, bar graphs, compare, investigations, percentages, pie graphs, spreadsheets, surveys, tally charts

Activity

#### Session 1: Getting our travel data

1. Discuss what kind of data the class could collect about their daily travel to school.
2. Identify variables that can be (i) counted (e.g.method of travel; number of passengers per vehicle) or (ii) measured (time to travel to school; distance travelled).
3. Students help to select data to collect as a class. Collect countable data for the morning’s travel to school. Students use the tally sheet copymaster in groups to collect the data on how they travelled to school this morning.Discuss methods of collection of measured data (e.g.use of the car or bus odometer; watch to measure elapsed time, etc).
4. Students collect measured data for use in the second unit.

#### Session 2: Organising our travel data

1. Work on the method of travel.
2. Discuss what is the best way to represent this data. Explain that we are going to use the computer to help us.
3. With the students work through the Pie Graph worksheet contained in the Spreadsheet Tutorial.
4. In the same way as the worksheet get small groups of students to draw the pie graph for the Travel to School data. Obviously the management of this is dependent on the numbers of computers that you have available for your class.

#### Session 3: How do we compare with other New Zealanders?

1. Students from around New Zealand have also been asked how they travel to school. This data has been collected and is available on the CensusAtSchool site.
2. Choose "Explore the data". Under the "Databases" heading, choose a year, and click on the "Table Maker" link. Choose "age" or "year" as variable 1 and "travel" as variable 2.
3. Once the data has been collected from the site get the students to discuss how they might make a comparison with their own data.
4. Once the students have decided on their approach they can be encouraged to type the data into Excel and use Excel to draw the graph(s) they need. They will need to consider the format they will enter the data in, whether they will need headings, etc. While the pie graph is the easiest to make the comparison with they may come up with other methods. One way is to convert the class numbers into percentages using Excel and then to draw bar graphs.

#### Session 4: How do we compare with the UK?

1. Other students from around the world have also been asked how they travel to school. This data has been collected and is available on the CensusAtSchool International site.
2. Get the students to find the Travel to School data for the students in the UK by using the DataTool (requires Flash). After you have made each selection, click the next button at the top right of the tool.
1. Choose UK, and a year to compare with.
2. Choose age and travel to school as the variables.
3. Choose to only use part of the database.
4. Double click on Age, and choose an appropriate age to compare with your class (you can choose a range of just one age).  You need to click save this option before continuing.
5. Drag the two variables onto the axes of the Tabulated data box (in the middle) and click the small play button. This will give you a sample randomly taken from the database of results.

3. Once the data has been collected from the site get the students to discuss how they might make a comparison with their own data. Consider questions such as:
Which of the columns of data should we take?
4. Once the students have decided on their approach they can be encouraged to type the data into Excel and use Excel to draw the graph(s) they need. They will need to consider the format they will enter the data in, whether they will need headings, etc. While the pie graph is the easiest to make the comparison with they may come up with other methods. One way is to convert the class numbers into percentages using Excel and then to draw bar graphs.
5. Conclude with a reporting time where the students are given the chance to show their graphs and to discuss what they have found out. They will probably have compared each mode of travel between the three lots of data. Can they give reasons for any differences they have found?
6. Get the students to ask their parents what was the most common form of travel to school when they were the same age ready for the next session.

#### Session 5: Has the method of travel to school changed?

1. Get the students to collate their parents’ data as before.
2. Students use Excel to enter the data and then compare it with the three sets used before.
3. What conclusions have they reached? What factors mean that this may not be a totally valid comparison? (eg the differing ages of the parents, the fact that parents may not have lived in this area when they were young, etc).
4. If you are going to go on and consider the second unit on travel immediately then you could raise questions at this point on whether the data on distance and time can be treated the same way. This leads to questions of the difference between category and continuous numeric data.
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