Conduct a [i]Two Variable Regression Analysi[/i]s of the given data and display the statistical values in a table.

[table][tr][td]1.[/td][td][icon]https://www.geogebra.org/images/ggb/toolbar/mode_move.png[/icon][/td][td]Use the [i]Move [/i]tool to highlight all cells of columns [i]A[/i] and [i]B[/i] that contain numbers.[/td][/tr][tr][td]2.[/td][td][icon]https://www.geogebra.org/images/ggb/toolbar/mode_twovarstats.png[/icon][/td][td]Select the [i]Two Variable Regression Analysis[/i] tool to show the diagram in the [i]Data Analysis Window.[/i][/td][/tr][tr][td]3.[/td][td][icon]https://www.geogebra.org/wiki/uploads/thumb/8/8a/Stylingbar_variable_analysis_statistics.svg/20px-Stylingbar_variable_analysis_statistics.svg.png[/icon][/td][td]Select [i]Show Statistics[/i] in the [i]Data Analysis Window [/i]to see the [i]statistic parameters [/i]of the data.[/td][/tr][tr][td]4.[/td][td][/td][td]Below the [i]Data Analysis Window [/i]you can choose from a variety of regression models[i] (e.g. [/i]Linear or Polynom).[br][/td][/tr][/table]

[table][tr][td][/td][td][b]Description[/b][/td][td][b]Formula[/b][/td][/tr][tr][td][b]MeanX[/b][/td][td]Calculates the [i]arithmetic mean[/i] of the elements in the first column.[/td][td][math]\frac{\sum x_i}{n}[/math][/td][/tr][tr][td][b]MeanY[/b][/td][td]Calculates the [i]arithmetic mean[/i] of the elements in the second column.[br][/td][td][math]\frac{\sum y_i}{n}[/math][/td][/tr][tr][td][b]Sx[/b][/td][td]Calculates the [i]standard deviation[/i] of the numbers in the first column.[/td][td][math]\sqrt{\frac{1}{n-1}\sum\left(x_i-MeanX\right)^2}[/math][/td][/tr][tr][td][b]Sy[/b][/td][td]Calculates the [i]standard deviation[/i] of the numbers in the second column.[/td][td][math]\sqrt{\frac{1}{n-1}\sum\left(y_i-MeanY\right)^2}[/math][/td][/tr][tr][td][b]r[/b][/td][td]Calculates the [i]correlation coefficient[/i].[/td][td][math]\frac{Sxy}{\sqrt{Sxx\cdot Syy}}[/math][/td][/tr][tr][td][math]\rho[/math][/td][td]Calculates the [url=https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient]Spearman's rank correlation coefficient[/url].[/td][td][/td][/tr][tr][td][b]Sxx[/b][/td][td]Calculates the statistic value:[/td][td][math]\sum x_i^2-\frac{\left(\sum x_i\right)^2}{n}=\sum\left(x_i-MeanX\right)^2[/math][/td][/tr][tr][td][b]Syy[/b][/td][td]Calculates the statistic value:[/td][td][math]\sum y_i^2-\frac{\left(\sum y_i\right)^2}{n}=\sum\left(y_i-MeanY\right)^2[/math][/td][/tr][tr][td][b]Sxy[/b][/td][td]Calculates the statistic value:[/td][td][math]\sum x_iy_i-\frac{\sum x_i\cdot\sum y_i}{n}[/math][math]=\sum\left(x_i-MeanX\right)\cdot\left(y_i-MeanY\right)[/math][/td][/tr][tr][td][math]R^2[/math][/td][td]Calculates the coefficient of determination.[br][b]Note:[/b] If the Regression Model is linear, then [math]R^2=r^2[/math][/td][td][math]1-\frac{SSE}{Syy}[/math][/td][/tr][tr][td][b]SSE[/b][/td][td]Calculates the Sum of squared errors between the y-values of the list and the function values of the x-values. [/td][td]f...Regression curve[br][math]\sum\left(y_i-f\left(x_i\right)\right)^2[/math][br][/td][/tr][/table]