Difference between revisions of "Aufgaben:Exercise 4.4Z: Contour Lines of the "2D-PDF""

From LNTwww
 
(5 intermediate revisions by the same user not shown)
Line 3: Line 3:
 
}}
 
}}
  
[[File:P_ID297__Sto_Z_4_4.png|right|frame|Gaussian 2D PDF:   contour lines]]
+
[[File:EN Sto Z 4 4.png|right|frame|Gaussian 2D–PDF: Contour lines]]
Given a two-dimensional Gaussian random variable  $(x, y)$  with mean  $(0, 0)$  and the 2D PDF.
+
Given a two-dimensional Gaussian random variable  $(x, y)$  with mean  $(0, 0)$  and the 2D–PDF
:$$f_{xy}(x, y) = C\cdot{\rm e}^{-(x^{\rm 2} + y^{\rm 2} +\sqrt{\rm 2}\hspace{0.05cm}\cdot \hspace{0.05cm} x \hspace{0.05cm}\cdot \hspace{0.05cm} y)}.$$
+
:$$f_{xy}(x,\ y) = C\cdot{\rm e}^{-(x^{\rm 2} + y^{\rm 2} +\sqrt{\rm 2}\hspace{0.05cm}\cdot \hspace{0.05cm} x \hspace{0.05cm}\cdot \hspace{0.05cm} y)}.$$
  
It is further known that the two standard deviations  $\sigma_x$  and  $\sigma_y$  are respectively equal  $1$ .
+
It is further known that the standard deviations are  $\sigma_x=\sigma_y=1.$ 
  
 
Entered in the sketch are:
 
Entered in the sketch are:
* a height line of this PDF for  $f_{xy}(x,y) =0.2$,
+
* a contour line of this PDF for  $f_{xy}(x,y) =0.2$,
* the (dark blue) ellipse major axis  $\rm (EA)$, and
+
* the  (dark blue)  ellipse major axis  $\rm (EA)$,  and
* the (red) correlation line  $y=K(x)$.
+
* the  (red)  "correlation line"  or  "regression line" $\rm (RL)$.
  
  
Line 22: Line 22:
 
Hints:
 
Hints:
 
*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables|Two-dimensional Gaussian Random Variables]].
 
*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables|Two-dimensional Gaussian Random Variables]].
 
+
*More information on this topic is provided in the  (German language) learning video  [[Gaußsche_2D-Zufallsgrößen_(Lernvideo)|"Gaußsche 2D-Zufallsgrößen"]]:
*More information on this topic is provided in the learning video  [[Gaußsche_2D-Zufallsgrößen_(Lernvideo)|Gaussian 2D random variables]]:
 
 
::Part 1:   Gaussian random variables without statistical bindings,   
 
::Part 1:   Gaussian random variables without statistical bindings,   
::Part 2:   Gaussian random variables with statistical bindings.  
+
::Part 2:   Gaussian random variables with statistical bindings.
 
 
 
 
  
  
Line 33: Line 30:
  
 
<quiz display=simple>
 
<quiz display=simple>
{How large is the correlation coefficient $\rho_{xy}$?
+
{What is the correlation coefficient&nbsp; $\rho_{xy}$?
 
|type="{}"}
 
|type="{}"}
 
$\rho_{xy} \ = \ $ { -0.727--0.687 }
 
$\rho_{xy} \ = \ $ { -0.727--0.687 }
  
  
{What is the maximum value&nbsp; $C = f_{xy}(0, 0)$&nbsp; of the PDF?
+
{What is the maximum PDF value&nbsp; $C = f_{xy}(0, 0)$?
 
|type="{}"}
 
|type="{}"}
 
$C \ = \ $ { 0.225 3% }
 
$C \ = \ $ { 0.225 3% }
Line 48: Line 45:
  
  
{At what values&nbsp; $x_0$&nbsp; and&nbsp; $y_0$&nbsp; respectively, does the hemline&nbsp; $f_{xy}(x,y) = 0.2$&nbsp; intersect the ellipse major axis?&nbsp; What is the relationship between&nbsp; $x_0$&nbsp; and&nbsp; $y_0$?
+
{At what values&nbsp; $x_0$&nbsp; and&nbsp; $y_0$&nbsp; does the contour line&nbsp; $f_{xy}(x,y) = 0.2$&nbsp; intersect the ellipse major axis?&nbsp; What is the relationship between&nbsp; $x_0$&nbsp; and&nbsp; $y_0$?
 
|type="{}"}
 
|type="{}"}
 
$x_0/y_0 \ = \ $ { -1.03--0.97 }
 
$x_0/y_0 \ = \ $ { -1.03--0.97 }
  
  
{Which statements are true regarding the correlation line&nbsp; $K(x)$&nbsp;?
+
{Which statements are true regarding the correlation line&nbsp; $y=K\cdot x$&nbsp;?
 
|type="[]"}
 
|type="[]"}
 
- The correlation line is steeper than the ellipse major axis.
 
- The correlation line is steeper than the ellipse major axis.
+ The angle of&nbsp; $K(x)$&nbsp; with respect to&nbsp; $x$&ndash;axis is about&nbsp; $-35^\circ$.
+
+ The angle ofthe correlation line with respect to the&nbsp; $x$&ndash;axis is about&nbsp; $-35^\circ$.
+ The correlation line intersects all hemlines where a vertical tangent can be applied to the ellipse.
+
+ The correlation line intersects all contour line where a vertical tangent can be applied to the ellipse.
  
  
Line 64: Line 61:
 
===Solution===
 
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Even without specifying&nbsp; $\sigma_x = \sigma_y = 1$&nbsp; one could see that the standard deviations&nbsp; $\sigma_x$&nbsp; and&nbsp; $\sigma_y$&nbsp; are equal,  
+
'''(1)'''&nbsp; Even without specifying&nbsp; $\sigma_x = \sigma_y = 1$&nbsp; one could see that the standard deviations&nbsp; $\sigma_x$&nbsp; and&nbsp; $\sigma_y$&nbsp; are equal,&nbsp; <br>since in the exponent of $f_{xy}(x, y)$&nbsp; the coefficients at&nbsp; $x^2$&nbsp; and&nbsp; $y^2$&nbsp; are equal.  
*since in the exponent of $f_{xy}(x, y)$&nbsp; the coefficients at&nbsp; $x^2$&nbsp; and&nbsp; $y^2$&nbsp; are equal.  
+
*By comparing coefficients,&nbsp; we thus obtain:
*By comparing coefficients, we thus obtain:
 
 
:$$\frac{- 2 \rho_{xy}}{\sigma_x\cdot\sigma_y} = \sqrt{2}\hspace{0.3cm}\Rightarrow\hspace{0.3cm}
 
:$$\frac{- 2 \rho_{xy}}{\sigma_x\cdot\sigma_y} = \sqrt{2}\hspace{0.3cm}\Rightarrow\hspace{0.3cm}
 
\rho_{xy}=\frac{-1}{\sqrt{2}} \hspace{0.15cm}\underline{\approx -0.707}.$$
 
\rho_{xy}=\frac{-1}{\sqrt{2}} \hspace{0.15cm}\underline{\approx -0.707}.$$
  
  
'''(2)'''&nbsp; Using the numerical values calculated in point&nbsp; '''(1)'''&nbsp; we also obtain:
+
'''(2)'''&nbsp; Using the numerical values calculated in point&nbsp; '''(1)''',&nbsp; we obtain:
 
:$$C=\frac{\rm 1}{\rm 2\it\pi\cdot\sigma_x\cdot\sigma_y\cdot\sqrt{\rm 1 - \rho_{xy}^{\rm 2}}}
 
:$$C=\frac{\rm 1}{\rm 2\it\pi\cdot\sigma_x\cdot\sigma_y\cdot\sqrt{\rm 1 - \rho_{xy}^{\rm 2}}}
 
=\frac{\rm 1}{\rm 2\pi\cdot\rm 1\cdot 1\cdot\sqrt{0.5}}=\frac{\rm 1}{\sqrt{\rm 2}\cdot \pi}\hspace{0.15cm}\underline{\approx \rm 0.225}.$$
 
=\frac{\rm 1}{\rm 2\pi\cdot\rm 1\cdot 1\cdot\sqrt{0.5}}=\frac{\rm 1}{\sqrt{\rm 2}\cdot \pi}\hspace{0.15cm}\underline{\approx \rm 0.225}.$$
Line 77: Line 73:
  
 
'''(3)'''&nbsp; The general equation is:  
 
'''(3)'''&nbsp; The general equation is:  
:$$\alpha = {\rm 1}/{\rm 2}\cdot \rm arctan \ (\rm 2 \cdot\it \rho_{xy}\cdot \frac{\sigma_x\cdot\sigma_y}{\sigma_x^{\rm 2} - \sigma_y^{\rm 2}}{\rm ).}.$$
+
:$$\alpha = {\rm 1}/{\rm 2}\cdot \rm arctan \ (\rm 2 \cdot\it \rho_{xy}\cdot \frac{\sigma_x\cdot\sigma_y}{\sigma_x^{\rm 2} - \sigma_y^{\rm 2}}{\rm ).}$$
  
*Applies&nbsp; $\sigma_x = \sigma_y$&nbsp; and&nbsp; $\rho_{xy} \ne 0$,&nbsp; then the angle is always&nbsp; $\alpha = \pm 45^\circ$, where the sign is equal to the sign of&nbsp; $\rho_{xy}$&nbsp; .  
+
*Applies&nbsp; $\sigma_x = \sigma_y$&nbsp; and&nbsp; $\rho_{xy} \ne 0$,&nbsp; then the angle is always&nbsp; $\alpha = \pm 45^\circ$,&nbsp; where the sign is equal to the sign of&nbsp; $\rho_{xy}$.  
 
*In the present case&nbsp; $\alpha\hspace{0.15cm}\underline{ = -45^\circ}$ holds.
 
*In the present case&nbsp; $\alpha\hspace{0.15cm}\underline{ = -45^\circ}$ holds.
  
Line 86: Line 82:
 
'''(4)'''&nbsp; For the plotted contour line holds:
 
'''(4)'''&nbsp; For the plotted contour line holds:
 
:$$f_{xy}(x, y)=\frac{1}{\sqrt{2}\cdot \pi}\cdot {\rm e}^{(x^{2} + y^{2} + \sqrt{2}\hspace{0.05cm}\cdot \hspace{0.05cm} x \hspace{0.05cm}\cdot \hspace{0.05cm}y)}=0.2\hspace{0.3cm}
 
:$$f_{xy}(x, y)=\frac{1}{\sqrt{2}\cdot \pi}\cdot {\rm e}^{(x^{2} + y^{2} + \sqrt{2}\hspace{0.05cm}\cdot \hspace{0.05cm} x \hspace{0.05cm}\cdot \hspace{0.05cm}y)}=0.2\hspace{0.3cm}
\rightarrow \hspace{0.3cm}{\rm e}^{-(x^{2} + y^{2} + \sqrt{2}\hspace{0.05cm} \cdot \hspace{0.05cm} x \hspace{0.05cm} \cdot \hspace{0.05cm}y)} = 0.8885
+
\Rightarrow \hspace{0.3cm}{\rm e}^{-(x^{2} + y^{2} + \sqrt{2}\hspace{0.05cm} \cdot \hspace{0.05cm} x \hspace{0.05cm} \cdot \hspace{0.05cm}y)} = 0.8885
 
\hspace{0.5cm}\Rightarrow \hspace{0.5cm} x^{\rm 2} + y^{\rm 2} + \sqrt{\rm 2}\cdot\hspace{0.05cm} x \hspace{0.05cm} \cdot \hspace{0.05cm}y = -{\rm ln(0.8885)} \approx\rm 0.118.$$
 
\hspace{0.5cm}\Rightarrow \hspace{0.5cm} x^{\rm 2} + y^{\rm 2} + \sqrt{\rm 2}\cdot\hspace{0.05cm} x \hspace{0.05cm} \cdot \hspace{0.05cm}y = -{\rm ln(0.8885)} \approx\rm 0.118.$$
  
*The angle of the ellipse major axis is&nbsp; $\alpha = -45^\circ$.&nbsp; Therefore&nbsp; $y_0 = - x_0$&nbsp; must hold. It further follows:
+
*The angle of the ellipse major axis is&nbsp; $\alpha = -45^\circ$.&nbsp; Therefore&nbsp; $y_0 = - x_0$&nbsp; must hold.&nbsp; It further follows:
 
:$$x_{\rm 0}^{\rm 2} + (-x_{\rm 0})^{\rm 2} + \sqrt{\rm 2}\cdot x_{\rm 0}(-x_{\rm 0}) = 0.118$$
 
:$$x_{\rm 0}^{\rm 2} + (-x_{\rm 0})^{\rm 2} + \sqrt{\rm 2}\cdot x_{\rm 0}(-x_{\rm 0}) = 0.118$$
 
:$$\Rightarrow \hspace{0.3cm}(\rm 2 - \sqrt{\rm 2})\cdot \it x_{\rm 0}^{\rm 2} = {\rm 0.118}  
 
:$$\Rightarrow \hspace{0.3cm}(\rm 2 - \sqrt{\rm 2})\cdot \it x_{\rm 0}^{\rm 2} = {\rm 0.118}  
Line 100: Line 96:
  
  
'''(5)'''&nbsp; Correct are <u>the proposed solutions 2 and 3</u>:  
+
'''(5)'''&nbsp; Correct are&nbsp; <u>the proposed solutions 2 and 3</u>:  
 
*With&nbsp; $\sigma_x = \sigma_y$&nbsp; and the result of the subtask&nbsp; '''(1)'''&nbsp; holds for the angle of the correlation line:
 
*With&nbsp; $\sigma_x = \sigma_y$&nbsp; and the result of the subtask&nbsp; '''(1)'''&nbsp; holds for the angle of the correlation line:
 
:$$\theta_{y\hspace{0.05cm}\rightarrow \hspace{0.05cm}x} = \arctan (\rho_{\it xy})=\arctan(-{\rm 1}/{\sqrt{\rm 2}})\approx -\rm 35.3^{\circ}.$$
 
:$$\theta_{y\hspace{0.05cm}\rightarrow \hspace{0.05cm}x} = \arctan (\rho_{\it xy})=\arctan(-{\rm 1}/{\sqrt{\rm 2}})\approx -\rm 35.3^{\circ}.$$
Line 106: Line 102:
  
  
The following is the <u>proof of the correctness of the last statement</u>:  
+
The following is the&nbsp; <u>proof of the correctness of the last statement</u>:  
*Solving the elliptic equation&nbsp; $($with&nbsp; $z = 0. 118)$,&nbsp; so&nbsp; $x^{\rm 2}+ y^{\rm 2} +\sqrt{\rm 2}\cdot \it x\cdot \it y - \it z = \rm 0$,&nbsp; to&nbsp; $y$&nbsp; we get after solving a quadratic equation:
+
*Solving the elliptic equation&nbsp; $($with&nbsp; $z = 0. 118)$,&nbsp; so&nbsp;  
 +
:$$x^{\rm 2}+ y^{\rm 2} +\sqrt{\rm 2}\cdot \it x\cdot \it y - \it z = \rm 0.$$
 +
 
 +
After solving the quadratic equation:
 
:$$y_{\rm 1, \ 2}={\sqrt{\rm 2}}/ {\rm 2} \cdot x\pm\sqrt{{x^{\rm 2}}/{\rm 2}-x^{\rm 2}+{\it z}}
 
:$$y_{\rm 1, \ 2}={\sqrt{\rm 2}}/ {\rm 2} \cdot x\pm\sqrt{{x^{\rm 2}}/{\rm 2}-x^{\rm 2}+{\it z}}
\hspace{0.5cm}\rightarrow \hspace{0.5cm} y_{\rm 1, \ 2}={\it x}/{\sqrt{\rm 2}}\pm \sqrt{z-{x^{\rm 2}}/{\rm 2}}.$$
+
\hspace{0.5cm}\Rightarrow \hspace{0.5cm} y_{\rm 1, \ 2}={\it x}/{\sqrt{\rm 2}}\pm \sqrt{z-{x^{\rm 2}}/{\rm 2}}.$$
*The vertical tangent results for the case that the two solutions&nbsp; $y_{\rm 1, \rm 2}$&nbsp; are identical.&nbsp; That is: &nbsp; The root expression must result in zero.  
+
*The vertical tangent results for the case that the two solutions&nbsp; $y_{\rm 1,\ \rm 2}$&nbsp; are identical.&nbsp; That is: &nbsp; The root expression must result in zero.  
 
*The solution for positive&nbsp; $x$&nbsp; is then: &nbsp; $x_{\rm T}=\sqrt{\rm 2\cdot \it z}=\rm \rm 0.485.$
 
*The solution for positive&nbsp; $x$&nbsp; is then: &nbsp; $x_{\rm T}=\sqrt{\rm 2\cdot \it z}=\rm \rm 0.485.$
*Inserted into the ellipse equation one obtains f&uuml;r the&nbsp; $y$&ndash;value of the tangent point:  
+
*Inserted into the ellipse equation one obtains for the&nbsp; $y$&ndash;value of the tangent point:  
 
:$$x_{\rm T}^{\rm 2} + y_{\rm T}^{\rm 2} + \sqrt{2} \cdot x_{\rm T} \cdot y_{\rm T} - z = 0
 
:$$x_{\rm T}^{\rm 2} + y_{\rm T}^{\rm 2} + \sqrt{2} \cdot x_{\rm T} \cdot y_{\rm T} - z = 0
 
\hspace{0.5cm}\Rightarrow \hspace{0.5cm} 2 z + y_{\rm T}^{\rm 2} + 2\sqrt{ z}\cdot y_{\rm T} - z = 0$$
 
\hspace{0.5cm}\Rightarrow \hspace{0.5cm} 2 z + y_{\rm T}^{\rm 2} + 2\sqrt{ z}\cdot y_{\rm T} - z = 0$$

Latest revision as of 16:43, 10 April 2022

Gaussian 2D–PDF: Contour lines

Given a two-dimensional Gaussian random variable  $(x, y)$  with mean  $(0, 0)$  and the 2D–PDF

$$f_{xy}(x,\ y) = C\cdot{\rm e}^{-(x^{\rm 2} + y^{\rm 2} +\sqrt{\rm 2}\hspace{0.05cm}\cdot \hspace{0.05cm} x \hspace{0.05cm}\cdot \hspace{0.05cm} y)}.$$

It is further known that the standard deviations are  $\sigma_x=\sigma_y=1.$ 

Entered in the sketch are:

  • a contour line of this PDF for  $f_{xy}(x,y) =0.2$,
  • the  (dark blue)  ellipse major axis  $\rm (EA)$,  and
  • the  (red)  "correlation line"  or  "regression line" $\rm (RL)$.




Hints:

Part 1:   Gaussian random variables without statistical bindings,
Part 2:   Gaussian random variables with statistical bindings.


Questions

1

What is the correlation coefficient  $\rho_{xy}$?

$\rho_{xy} \ = \ $

2

What is the maximum PDF value  $C = f_{xy}(0, 0)$?

$C \ = \ $

3

What is the angle  $\alpha$  between ellipse major axis  $\rm (EA)$  and  $x$–axis?

$\alpha\ = \ $

$ \ \rm degrees$

4

At what values  $x_0$  and  $y_0$  does the contour line  $f_{xy}(x,y) = 0.2$  intersect the ellipse major axis?  What is the relationship between  $x_0$  and  $y_0$?

$x_0/y_0 \ = \ $

5

Which statements are true regarding the correlation line  $y=K\cdot x$ ?

The correlation line is steeper than the ellipse major axis.
The angle ofthe correlation line with respect to the  $x$–axis is about  $-35^\circ$.
The correlation line intersects all contour line where a vertical tangent can be applied to the ellipse.


Solution

(1)  Even without specifying  $\sigma_x = \sigma_y = 1$  one could see that the standard deviations  $\sigma_x$  and  $\sigma_y$  are equal, 
since in the exponent of $f_{xy}(x, y)$  the coefficients at  $x^2$  and  $y^2$  are equal.

  • By comparing coefficients,  we thus obtain:
$$\frac{- 2 \rho_{xy}}{\sigma_x\cdot\sigma_y} = \sqrt{2}\hspace{0.3cm}\Rightarrow\hspace{0.3cm} \rho_{xy}=\frac{-1}{\sqrt{2}} \hspace{0.15cm}\underline{\approx -0.707}.$$


(2)  Using the numerical values calculated in point  (1),  we obtain:

$$C=\frac{\rm 1}{\rm 2\it\pi\cdot\sigma_x\cdot\sigma_y\cdot\sqrt{\rm 1 - \rho_{xy}^{\rm 2}}} =\frac{\rm 1}{\rm 2\pi\cdot\rm 1\cdot 1\cdot\sqrt{0.5}}=\frac{\rm 1}{\sqrt{\rm 2}\cdot \pi}\hspace{0.15cm}\underline{\approx \rm 0.225}.$$


(3)  The general equation is:

$$\alpha = {\rm 1}/{\rm 2}\cdot \rm arctan \ (\rm 2 \cdot\it \rho_{xy}\cdot \frac{\sigma_x\cdot\sigma_y}{\sigma_x^{\rm 2} - \sigma_y^{\rm 2}}{\rm ).}$$
  • Applies  $\sigma_x = \sigma_y$  and  $\rho_{xy} \ne 0$,  then the angle is always  $\alpha = \pm 45^\circ$,  where the sign is equal to the sign of  $\rho_{xy}$.
  • In the present case  $\alpha\hspace{0.15cm}\underline{ = -45^\circ}$ holds.


(4)  For the plotted contour line holds:

$$f_{xy}(x, y)=\frac{1}{\sqrt{2}\cdot \pi}\cdot {\rm e}^{(x^{2} + y^{2} + \sqrt{2}\hspace{0.05cm}\cdot \hspace{0.05cm} x \hspace{0.05cm}\cdot \hspace{0.05cm}y)}=0.2\hspace{0.3cm} \Rightarrow \hspace{0.3cm}{\rm e}^{-(x^{2} + y^{2} + \sqrt{2}\hspace{0.05cm} \cdot \hspace{0.05cm} x \hspace{0.05cm} \cdot \hspace{0.05cm}y)} = 0.8885 \hspace{0.5cm}\Rightarrow \hspace{0.5cm} x^{\rm 2} + y^{\rm 2} + \sqrt{\rm 2}\cdot\hspace{0.05cm} x \hspace{0.05cm} \cdot \hspace{0.05cm}y = -{\rm ln(0.8885)} \approx\rm 0.118.$$
  • The angle of the ellipse major axis is  $\alpha = -45^\circ$.  Therefore  $y_0 = - x_0$  must hold.  It further follows:
$$x_{\rm 0}^{\rm 2} + (-x_{\rm 0})^{\rm 2} + \sqrt{\rm 2}\cdot x_{\rm 0}(-x_{\rm 0}) = 0.118$$
$$\Rightarrow \hspace{0.3cm}(\rm 2 - \sqrt{\rm 2})\cdot \it x_{\rm 0}^{\rm 2} = {\rm 0.118} \hspace{0.5cm}\rightarrow \hspace{0.5cm} x_{\rm 0}^{\rm 2} \approx \frac{\rm0.118}{\rm0.585}\approx\rm 0.202; \hspace{0.5cm} {\it x}_{\rm 0}\approx\pm\rm 0.450.$$
  • The two intersections of the plotted contour lines with the ellipse major axis are thus at  $(+0.45, -0.45)$  and  $(-0.45, +0.45)$.
  • The quotient in both cases is  $x_0/y_0 \hspace{0.15cm}\underline{ = -1}$.



(5)  Correct are  the proposed solutions 2 and 3:

  • With  $\sigma_x = \sigma_y$  and the result of the subtask  (1)  holds for the angle of the correlation line:
$$\theta_{y\hspace{0.05cm}\rightarrow \hspace{0.05cm}x} = \arctan (\rho_{\it xy})=\arctan(-{\rm 1}/{\sqrt{\rm 2}})\approx -\rm 35.3^{\circ}.$$
  • This means:   The first statement is false and the second is true.


The following is the  proof of the correctness of the last statement:

  • Solving the elliptic equation  $($with  $z = 0. 118)$,  so 
$$x^{\rm 2}+ y^{\rm 2} +\sqrt{\rm 2}\cdot \it x\cdot \it y - \it z = \rm 0.$$

After solving the quadratic equation:

$$y_{\rm 1, \ 2}={\sqrt{\rm 2}}/ {\rm 2} \cdot x\pm\sqrt{{x^{\rm 2}}/{\rm 2}-x^{\rm 2}+{\it z}} \hspace{0.5cm}\Rightarrow \hspace{0.5cm} y_{\rm 1, \ 2}={\it x}/{\sqrt{\rm 2}}\pm \sqrt{z-{x^{\rm 2}}/{\rm 2}}.$$
  • The vertical tangent results for the case that the two solutions  $y_{\rm 1,\ \rm 2}$  are identical.  That is:   The root expression must result in zero.
  • The solution for positive  $x$  is then:   $x_{\rm T}=\sqrt{\rm 2\cdot \it z}=\rm \rm 0.485.$
  • Inserted into the ellipse equation one obtains for the  $y$–value of the tangent point:
$$x_{\rm T}^{\rm 2} + y_{\rm T}^{\rm 2} + \sqrt{2} \cdot x_{\rm T} \cdot y_{\rm T} - z = 0 \hspace{0.5cm}\Rightarrow \hspace{0.5cm} 2 z + y_{\rm T}^{\rm 2} + 2\sqrt{ z}\cdot y_{\rm T} - z = 0$$
$$\Rightarrow \hspace{0.3cm}y_{\rm T}^{\rm 2} + 2\sqrt{ z}\cdot y_{\rm T} + z = 0 \hspace{0.5cm}\Rightarrow \hspace{0.5cm} (y_{\rm T} + \sqrt{ z}) = 0\hspace{0.5cm}\Rightarrow \hspace{0.5cm} y_{\rm T} = -\sqrt{ z} = -0.343.$$
  • This gives  $y_{\rm T}=-{x_{\rm T}}/{\sqrt{\rm 2}}. $  But this also means:   The tangent point  $(x_{\rm T}, y_{\rm T})$  lies exactly on the correlation line  $y=K(x)=-{ x}/{\sqrt{\rm 2}}.$