![]() OK, if we didn't have this outlier point, this regression line would dip down a little bit and would better fit the data that we have here in our data set. The last option here says there's an influential point that strongly affects the graph of the regression line. Well, most of the data here conforms to your straight line regression line, so that's obviously not true. The next answer option says the data has a pattern that is not a straight line. If the others don't pan out, then this one's obviously going to be the one that's right. ![]() Let's check out the other answer options. “There is no characteristics of the data that is ignored by the regression line.” Well, maybe, maybe not. Most of the data here fits this regression line pretty well. Well, if I go back to my scatter plot, there's definitely a trend in the data. The first one here says there's no trend in the data. The last part of the problem says, “Identify a characteristic of the data that is ignored by the regression line.” If we look at the different answer options here, let's examine them one by one. The coefficient for my x-variable is the same as the slope, so that's to three decimal places. And then it says round the coefficient to three decimal places. The constant is the intercept, so I'm going to round that to two decimal places. My instructions say to round the constant two decimal places as needed. You can take it from wherever you want it's the same number either way. So I'm just going to take the numbers here from the parameters estimate table. Notice these numbers here are the same numbers that we find up here in the regression line equation, and everything's laid out a little bit more here. I find there's a lot of information here at the top that's crammed together, and so in order to get the numbers right, I'm gonna look down here at the parameter estimates table. I just flip back here to the first page, and my regression equation is right here at the top. OK, the second part of our problem asks us for the regression line equation. So now we see which answer option is obviously the one we want to pick. And now I can select each axis independently and change the values here for maximum and minimum so that they match what I see in the problem statement. ![]() So I click on this little three line icon in the bottom left corner. I can change the axes here to match, and that'll make it much easier to see which answer option is the right one. Now in order to select the right answer option from the four that I'm selecting, notice how the axes on my scatter plot here in StatCrunch are different than the ones for the answer options in my problem. To get to the second page, I go down to this arrow button here in the bottom right corner, and lo and behold, there's my scatterplot already made for me! That means this is page 1 of 2 pages total. So I press Compute!, and here in my results window, notice how it says up here 1 of 2. And then I don't need a mess with any of these other settings they're all gonna be good for me. Here in the options window, I'm going to select my x- and my y-variables. So to start the regression analysis, I go to Stat –> Regression –> Simple Linear. So I'm just going to do that because it requires me to push less buttons. However, I know that I'm gonna have to make a regression line equation eventually anyway, and I get a scatter plot from the regression analysis. Inside StatCrunch, I could go up here to Graph and then select Scatter Plot. The first part of our problem asks us to create a scatterplot. ![]() Now I'm going to resize this window so we can see everything that's gonna go on here. More information is available in the help file through StatCrunch.OK, the first thing I want to do is bring up my data set in StatCrunch. Select the columns for the expected counts.Select the columns for the observed counts.Choose Stat > Goodness-of-fit > Chi-Square test.Enter the observed counts in the first column, and the expected counts in the second column.You'll need to calculate the expected counts based on the assumed distribution.Chi-Square Goodness-of-Fit Test Using StatCrunch Step 6 : No, there is clearly not enough evidence based on this sample to say that the distribution is different from what the company claims. Step 5 : Since the P-value is much larger than α, we do not reject the null hypothesis. of colors does not follow the company's claim Notice that all expected counts are at least 1, and none are less than 5. ![]()
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