Using StatCrunch to find a regression line equation

Intro

Howdy!  I am Professor Curtis of Aspire Mountain Academy here with more statistics homework help.  Today we're going to learn how to use StatCrunch to find a regression line equation.  Here's our problem statement: Use the given data to find the equation of the regression line. Examine the scatter plot and identify a characteristic of the data that is ignored by the regression line.

Part 1

OK, the first thing I want to do is bring up my data set in StatCrunch.  Now I'm going to resize this window so we can see everything that's gonna go on here.  OK, we're set with that now.

The first part of our problem asks us to create a scatterplot.  Inside StatCrunch, I could go up here to Graph and then select Scatter Plot.  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.  So I'm just going to do that because it requires me to push less buttons.

So to start the regression analysis, I go to Stat –> Regression –> Simple Linear.  Here in the options window, I'm going to select my x- and my y-variables.  And then I don't need a mess with any of these other settings; they're all gonna be good for me.  So I press Compute!, and here in my results window, notice how it says up here 1 of 2.  That means this is page 1 of 2 pages total.  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!

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.  I can change the axes here to match, and that'll make it much easier to see which answer option is the right one.  So I click on this little three line icon in the bottom left corner.  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 just did it for the X.  I do the same thing here for the Y.  And there we go.  So now we see which answer option is obviously the one we want to pick.  It's going to be this one here.  Nice work!

Part 2

OK, the second part of our problem asks us for the regression line equation.  That's really easy to do.  We've already done the analysis here.  I just flip back here to the first page, and my regression equation is right here at the top.  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.  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.  So I'm just going to take the numbers here from the parameters estimate table.  You can take it from wherever you want; it's the same number either way.

My instructions say to round the constant two decimal places as needed.  The constant is the intercept, so I'm going to round that to two decimal places.  And then it says round the coefficient to three decimal places.  The coefficient for my x-variable is the same as the slope, so that's to three decimal places.  Good job!

Part 3

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 first one here says there's no trend in the data.  Well, if I go back to my scatter plot, there's definitely a trend in the data.  Most of the data here fits this regression line pretty well.

“There is no characteristics of the data that is ignored by the regression line.”  Well, maybe, maybe not.  Let's check out the other answer options.  If the others don't pan out, then this one's obviously going to be the one that's right.

The next answer option says the data has a pattern that is not a straight line.  Well, most of the data here conforms to your straight line regression line, so that's obviously not true.  The last option here says there's an influential point that strongly affects the graph of the regression line.  That's definitely true.  Look at this outlier point right here.  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.  So this is the answer we're going to want to select.  Well done!

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