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KJ4UTS
 one year ago
The following table shows total forest and timberland in the United States in millions of acres in the indicated year. Calculate the SSE for the quadratic regression function (with coefficients rounded to three decimal places) from the previous question. Round your answer to one decimal place.
KJ4UTS
 one year ago
The following table shows total forest and timberland in the United States in millions of acres in the indicated year. Calculate the SSE for the quadratic regression function (with coefficients rounded to three decimal places) from the previous question. Round your answer to one decimal place.

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KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2I am not sure how to calculate the SSE (sum of the squares error)? Is it the R^2 0.8823... or 0.9 rounded to on decimal place?

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2This is the quadratic equation y=0.036x^21.976x+761.454

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0Honestly I've never done this before. A google search gave this formula \[\sum_{i=1}^{n}(x_i\bar x)^2\] The best I can figure is that you're supposed to use the number in the table for \(x_i\) and the corresponding point from your regression function for \(\bar x\).

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2This is an example I found in my textbook. I guess instead of y=x^2 I would use y=0.036x^21.976x+761.454 but its still a little confusing.

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0ok. the numbers in the timber land row of 1st table are the actual values. You have to use your function to get the other value. What year did your function start at?

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.21962 but I think you represent that with 0 1970 = 8 1977 = 15 1987 = 25 1992= 30

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0ok great now find the corresponding y values from your regression function.

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0It really is easier to set this up in a table like they have it

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0ok. those are your actual values. You have to plug in the year after 1962 into your regression formula to get the 3rd columndw:1442969120793:dw

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0y=0.036x^21.976x+761.454 So for x = 0 you get y = 761.454 For x = 8 you get y = 747.95 and so on

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2yeah I figured out how to plug it in on my calculator

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0something's not right with the third column. They shouldn't be that far off from the actuals.

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2im not sure what happened?

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0this is what I gotdw:1442970171548:dw

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2oh ok I must have made a mistake somewhere

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0oh ok. once you get the right values, sum the L5 column and that's the SSE.

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2so add everything up on L5. I wonder why my calculator got the first two right and not the rest on the same column it was the same equation.

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2because with what I got now it would be 1557.127

anonymous
 one year ago
Best ResponseYou've already chosen the best response.0Yeah, that's huge. I think the smaller the number the better the regression curve is

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2oh ok thank you for your time and help :)

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2@jim_thompson5910 I was wondering if you might know where I went wrong?

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2oh thank you I have just been confused with this SSE

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0what data did you type in to get y=0.036x^21.976x+761.454

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2the x an y's on the chart I checked and I got this formula y=0.036x^21.976x+761.454 as the right answer but now I just have to figure out the SSE

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0did you use 1962? or just 62?

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0if possible, show me a screenshot of the L1 & L2 lists you typed in

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0if you plugged x = 0 into your regression equation y=0.036x^21.976x+761.454, what is y?

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2@jim_thompson5910 761.454

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0yes, and I see how that is at the top of L3. Good

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0L4 looks like yyhat and L5 looks like (yyhat)^2

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0the last thing you do is add up the values in L5

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2I did add them and got 1557.127 but I though that looked to big I though it was supposed to be close to 1

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0you're thinking of the correlation coefficient r maybe? there is no restriction on the SSE. The larger the SSE, the more error we have so to speak

KJ4UTS
 one year ago
Best ResponseYou've already chosen the best response.2oh so the answer would be 1557.1 rounded to one decimal place then. Thank you for taking the time to check everything over :)

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0on page 4 of this pdf, they computed the SSE to be approx 96 thousand. So it's definitely possible to get a large SSE http://www.public.iastate.edu/~alicia/stat328/Regression%20inferencepart3.pdf

jim_thompson5910
 one year ago
Best ResponseYou've already chosen the best response.0`oh so the answer would be 1557.1 rounded to one decimal place then` I agree
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