# View My Data

Learn How: Create Your Own Live GeoGebra Web Pages

OzDASL: Australian Data and Story Library

TI SensorTag: Real World Data in the Palm of your Hand

Quickly and easily view your datasets using this simple GeoGebra utility...

 Tap the image to see how to use the web tools on this page... ⇓ Tap the image to see how to use the GeoGebra Data Analysis tools ⇓

Enter one or two data sets into the fields below, and then select one of the Chart My Data buttons.

For individual (univariate) data sets (DataSet1 and/or DataSet2), each value will be numbered sequentially.

For paired (bivariate) data sets, DataSet1 becomes the horizontal (independent) value set and DataSet2 the vertical (dependent) set.

Note: To access GeoGebra's in-built Data Analysis tools, select list data from the spreadsheet (simplest way is to drag across the column headers to select those columns), and then select the Analysis menu as shown - it is the first of the three spreadsheet menus.

Explore the three menus available, and the various options available within these. They offer a couple of more options for you to view and analyse your data sets.

## Sample 1: Bar of soap

Do you use up the same amount of the soap in the shower each morning, or does it depend on the size of the bar of soap?

This data was collected by Rex Boggs in Rockhampton, Queensland.

"I had a hypothesis that the daily weight of my bar of soap in my shower wasn't a linear function, the reason being that the tiny little bar of soap at the end of its life seemed to hang around for just about ever. I wanted to throw it out, but I felt I shouldn't do so until it became unusable. And that seemed to take weeks."

Day = {0,1,4,5,6,7,8,9,11,12,17,19,20,21,22}

Weight = {124,121,103,96,90,84,78,71,58,50,27,16,12,8,6}

## Sample 2: Kiama Blowhole: Time Between Eruptions

 My family lived for many years in the beautiful seaside town of Kiama, on the South Coast of New South Wales. One of the best known features of the town is the Kiama Blowhole, which blows regularly - or at least, semi-regularly. This data set shows the time (in seconds) between eruptions over an extended period. Study the data using different representations. Any predictions you could make? Eruptions = {83,51,87,60,28,95,8,27,15, 10,18,16,29,54,91,8,17,55,10,35, 47,77,36,17,21,36,18,40,10,7,34, 27,28,56,8,25,68,146,89,18, 73,69,9,37,10,82,29,8,60,61,61,18, 169,25,8,26,11,83,11,42,17,14,9,12} Source: OzDASL

## Sample 3: Road Trip Sensor Data

 TI SensorTag data: Canberra to Sydney (June 18 2016: 9:17am - 11:57 am - Collected using DataWorks app and iPhone 6) Data points were collected at approximately 1 minute intervals from 9:20 am (outside Canberra) until just before midday (Sydney).

• Can you find the time (approximately how many minutes into the trip) where I pulled over and removed the red jacket and plastic casing from the SensorTag? (Hint: check IR Temperature Data)

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• Can you find the time where I moved the SensorTag from the dashboard of the vehicle to the passenger seat? (Hint: check Light Data)

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• Can you find the time where the altitude drops most quickly between Canberra and Sydney? (Hint: check Barometer Data)

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• Can you explain how altitude and barometric pressure are related? (FYI: a formula which can be used is

$altitude = 44330*(1-({P_{barometer} \over P_{sealevel}}) ^ {1 \over 5.255})$

where Psealevel represents the barometric pressure at sea level - 1013.25 hPa standard).

Note that here we set both longitude and altitude data sets - what does this show us?

What do you think you will see if we plot barometric pressure against altitude? Explain your response.

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• What was the weather like for this trip? (Be as detailed as possible using a range of data to support your response).

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• Study the latitude and longitude data from this trip. What does this reveal?