data:image/png;base64,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:image/png;base64,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Updated by Jose Torres 1/15/2020 1920 U3L4p3 Improving Your Graphing App (starter file) Week 1B: Data Unit 3 Lab 4: Building Data Visualization Tools Improving Your Graphing App1.0 Load your grapher from before; it should have been saved as "U3L4-Building a Graphing App."2.0 Look in the Variables palette for this dataset on a classroom of 27 sixth-graders: Gr6-age-height-weight-gender-stats. Each data point in this set represents a person & contains four items: list (137.4)(146.52)(40.52)(F) age in months / height in centimeters / weight in kilograms / gender Your grapher was written to expect only two numbers in each datapoint. Will it even work with this dataset? Try it. What does it do?3.0 Create a selector age which extracts the age of an input person.4.0 Similarly, create selectors height, weight, gender which extract the height, weight and gender of an input person.5.0 Now use the above selectors to create custom blocks that report a list containing only the 27: ages, heights, & weights.6.0 Create a block that takes a list of numbers as input and reports the average value.7.0 Create a block, female data, using keep, that reports a list containing all the datapoints of only the girls, and another block, male data, that does the same for the boys.8.0 You now have everything you need to create a graph of height vs. weight, showing the boys and girls in different colors that also allows you to examine individual datapoints to see their approximate values, as shown in the animation above. Build it.9.0 You will need to set graph scale for each of these graphs. Searching through the data to find the lowest and highest values can be a nuisance. Create tools to do that for you. min of list ()max of list ()... create a graph of height vs. weight, showing the boys and girls in different colors that also allows you to examine individual datapoints to see their approximate values, as shown in the animation above. Build it.10.0 Use the tools you have built to create a few separate graphs: age against height or weight, height against weight, separated or not separated by gender—and also mark the averages in each case. What can you say about this particular class of sixth-graders based on the data?

LABEL will stamp text on the stage at the given font size. The direction of the text is the direction the sprite is facing, and color will match the pen color.

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data lists

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132.32160.217.21664275466284327.4958.5511.59047005795235