Open the desmos link below to go along with this lesson. I suggest you split your computer screen with the desmos and this GeoGebra.[br][br][u]desmos link[/u]: https://www.desmos.com/calculator/idcdlmuqic[br][br]Watch the oranges video below and record the weight for the number of oranges in the box into your desmos table. [br][br][b]here is the url in case the video just below won't work:[/b] [br][u]oranges video[/u]: https://player.vimeo.com/video/304121358[br][br][u]Desmos directions[/u]:[br]1. Type values in the table to plot points in the plane. Make sure you have the coordinates in the right order, (x, y).[br]2. Click on the wrench to add labels and change the window settings, especially necessary when the x- and y-axes have different scales.[br]3. The green line at y = 0.5 can be changed by dragging the points at (-2, 0.5) and (2, 0.5).[br]4. Adjust the moveable points to fit the line to the data set as closely as possible (make the line go in the same direction as all the points and through the middle of the points).

[u]Desmos directions[/u]:[br]1. Type values in the table to plot points in the plane. Make sure you have the coordinates in the right order,[br] (x, y).[br]2. Click on the wrench to add labels and change the window settings, especially necessary when the x- and y-axes have different scales.[br]3. The green line at y = 0.5 can be changed by dragging the points at (-2, 0.5) and (2, 0.5).[br]4. Adjust the moveable points to fit the line to the data set as closely as possible.

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[/img][br]We'll keep this data and scatter plot for a future lesson.