The document Arizona Residential Property Valuation System, published by the Arizona Department of Revenue, describes how county assessors use compute

Given: ${\displaystyle n=\text{Sample size}=44}$ a) Lot Size is on the horizontal axis and value is on the vertical axis. b) It is reasonable to find a regression lien for the data if there is no strong curvature present in the scatterplot. We note that there is no strong curvature in the scatterplot of part (a) and thus it is reasonable to find a regression line for the data. c) Let us first determine the necessary sums: ${\displaystyle \sum x_i=101.93}$
${\displaystyle \sum x_i^2=241.5221}$
${\displaystyle \sum y_i=19689}$
${\displaystyle \sum x_i{y}_i=45973.23}$ Next, we can determine ${\displaystyle S_{xx}}$ and ${\displaystyle S_{xy}}$
${\displaystyle S_{xx}=\sum x_i^2-\frac{{(\sum x_i)}^2}{n}=241.5221-\frac{{101.93}^2}{44}=5.3920}$
${\displaystyle S_{xy}=\sum x_i{y}_i-\frac{(\sum x_i)(\sum y_i)}{n}=45973.23-\frac{101.93\cdot 19689}{44}=361.8716}$ The estimate b of the slope ${\displaystyle \beta }$ is the ratio of ${\displaystyle S_{xy}}$ and ${\displaystyle S_{xx}}$: ${\displaystyle b=\frac{S_{xy}}{S_{xx}}=\frac{361.8716}{5.3920}=67.1128}$ The mean is the sum of all values divided by the number of values: ${\displaystyle \bar{x}=\frac{\sum x_i}{n}=\frac{101.93}{44}=2.3166}$
${\displaystyle \bar{y}=\frac{\sum y_i}{n}=\frac{19689}{44}=447.4773}$ The estimate a of the intercept ${\displaystyle \alpha }$ is the average of y decreased by the product of the estimate of the slope and the average of x. ${\displaystyle a=\bar{y}-b\bar{x}=447.4773-67.1128\cdot 2.3166=292.0043}$ General least-squares equation: ${\displaystyle \hat{y}=\alpha +\beta x}$. Replace ${\displaystyle \alpha }$ by ${\displaystyle a=292.0043}$ and ${\displaystyle \beta }$ by ${\displaystyle b=67.1128}$ in the general least-squares equation: ${\displaystyle y=a+bx=292.0043+67.1128x}$ d) There appear to be no outliers, because the right most point lies much futher to the right than the other points in the graph. The outlier also appears to be a potential outlier, because it is possible that this point pulls the regression line down. e) Let us first determine the necessary sums: ${\displaystyle \sum x_i=98.31}$
${\displaystyle \sum x_i^2=228.4177}$
${\displaystyle \sum y_i=19314}$
${\displaystyle \sum x_i{y}_i=44615.73}$ Next, we can determine ${\displaystyle S_{\times }}$ and

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