![]() Troubleshooting Problem: When you choose Zoomstat you get an error.īelieve it or not, the most common cause of this is having your calculator in your backpack and having it accidentally type all kinds of stuff in the y-list. If you want to see which points are which, you can press and use the arrows to jump from point to point. Here, it seems that taller players generally weigh more, but since the points are not very close together, the association between height and weight isn’t very strong. It doesn’t have any labels or anything, but you can use this to see if perhaps linear regression is appropriate or just to see what kind of pattern is present. Once plot 1 is on you can press at the top of the calculator and choose 9:ZoomStat to see the scatterplot.Īfter pressing or typing 9, the plot should come up! ![]() You can do this by highlighting it and pressing. Once in this menu, highlight and press to turn the plot on and then make sure that the little graph that looks like a scatterplot is selected for TYPE. Press or 1:Plot 1… to get into the next menu. To get to the statplot menu, press and (at the top of the calculator). Stept 2: Set up the scatterplot in STAT PLOTS. Notice that we keep the data in the SAME ORDER as the original table. Just type the number and press enter to go to the next space. Now enter the values for x (the predictor) into L1 and the values for y (the response) into L2. To enter data in your calculator, press and then choose 1.Edit. If not, be sure to check out the article on reading scatterplots to better understand how to choose these variables. Often a textbook will tell you which to treat as x (predictor) and y (response). We will use the height as the independent variable (the predictor) and the weight as the dependent variable (the response). The table below shows the heights (inches) and weights (pounds) of seven randomly selected players on the Chicago Cubs active roster. Let’s use an example data set to walk through the process. ![]() On the TI83 or 84 series of graphing calculators, getting a scatterplot is pretty easy. The overall pattern would help us determine what kind of association time spent studying has with final exam grades. For example, can you say in general that studying more will result in higher grades? We could investigate this by collecting data on how long students studied and perhaps their grade on a final exam and then creating a scatterplot. Scatterplots are used to visualize the relationship or association between two variables. ![]()
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