(a) For increase of 1 SQFT in size, predicted change in List
Price = $1305.827.
(b) For x = 3182, predicted list price = -765663 + (1305.827 *
3182) = $3,389,479.
residual = $(3,695,000 - 3,389,479) = $305,521.
For 3182 SQFT, the predicted list price of the property is
$3,389,479 and considering the actual list price, we can conclude
that we have under-predicted (since the predicted value is less
than the actual value) by $305,521.
(c) The intercept here is the predicted list price of the property
if the SQFT value becomes 0. Its value here is
-$765,663. In most cases, the intercept has no
practical meaning.
(d) Percent of fluctuation = R-square * 100% = 78.1%.
(e) Standard deviation = Root mean square error = 3167283.
LA Real Estate Data. On a particular day in the spring, there were several properties for...
I think I am reading into the question to much. Would I just do x=10 and plug it into the least square equation? Bivariate Fit of ls2 By pyr2 1.0 0.9 0.8 Is2 0.7 0.6 0.5 0.4 30 40 50 10 0 20 60 70 pyr2 Linear Fit Linear Fit Is2 - 0.8044313 - 0.0016504 pyr2 Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.031661 0.006831 0.137003 0.759024 Lack Of Fit...
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A study was conducted in which participants looked at photographs of various people and guessed how old each phot regression analy ographed person was. Then the amount of error in each guess was calculated, and this was used as a response variable in Here are the names of the variables used: these will be referenced in the questions below: 2. Difference between guessed age and true age (Positive errors are overestimates, i.e. guessing an age greater than true age; negative...
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