Find the relation between 2 variables

In summary, finding the relation between two variables involves analyzing how changes in one variable impact the other. This can be achieved through various statistical methods, such as correlation and regression analysis, which help identify patterns, strengths, and types of relationships, whether linear or non-linear. Understanding these relationships is crucial for making predictions and informed decisions based on data.
  • #1
Debdut
19
2
Homework Statement
Find the relation between Vin and Vout
Relevant Equations
V1 = (-gm1 * Vin + s* C1 * Vout) / (gmc + s * C1)
gmc * V1 + s * C2 * Vout = Vx * (s * rb * C2 + 1) / rb
s * C1 * (V1 - Vout) + s * C2 * (Vx - Vout) = gm2 * Vx + Vout / ro2
Here is the equation I obtain after simplification, I don't know if it is correct:
gmc * V1 + s * C2 * Vout = [{s * (C1 + C2) * ro2 + 1} * Vout - s * C1 * ro2 * V1] * (s * rb * C2 + 1) / {ro2 * rb * (s * C2 - gm2)}

I need to eliminate V1 to find the relation between Vin and Vout.
 
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  • #2
Can you post the complete problem statement ?
And please understand that telepathy isn't everyone's forte, so tell us what this is about.

##\ ##
 
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  • #3
BvU said:
Can you post the complete problem statement ?
And please understand that telepathy isn't everyone's forte, so tell us what this is about.

##\ ##
I don't know if they rewrote, but he explained in the line at the bottom. Though , OP , please use Latex to write your question.
 
  • #4
Debdut said:
V1 = (-gm1 * Vin + s* C1 * Vout) / (gmc + s * C1)
gmc * V1 + s * C2 * Vout = Vx * (s * rb * C2 + 1) / rb
s * C1 * (V1 - Vout) + s * C2 * (Vx - Vout) = gm2 * Vx + Vout / ro2
For clarity's sake, is the following an accurate statement of the three equations?

##\qquad \textrm{Eqn 1: } V_1 = \dfrac{-g_{m_1} V_{in} + sC_1V_{out}}{g_{m_c} + sC_1}##

##\qquad \textrm{Eqn 2: } g_{m_c} V_1 + s C_2 V_{out} = V_x \dfrac{s r_b C_2 + 1}{r_b}##

##\qquad \textrm{Eqn 3: } s C_1 \left(V_1 - V_{out}\right) + s C_2 \left(V_x - V_{out}\right) = g_{m_2} V_x + \dfrac{V_{out}}{r_{o_2}}##

Thank you!
 
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  • #5
e_jane said:
For clarity's sake, is the following an accurate statement of the three equations?

##\qquad \textrm{Eqn 1: } V_1 = \dfrac{-g_{m_1} V_{in} + sC_1V_{out}}{g_{m_c} + sC_1}##

##\qquad \textrm{Eqn 2: } g_{m_c} V_1 + s C_2 V_{out} = V_x \dfrac{s r_b C_2 + 1}{r_b}##

##\qquad \textrm{Eqn 3: } s C_1 \left(V_1 - V_{out}\right) + s C_2 \left(V_x - V_{out}\right) = g_{m_2} V_x + \dfrac{V_{out}}{r_{o_2}}##

Thank you!
Yes, these are the equations. Thank you very much.
 
  • #6
ckt.png


I am sorry for not elaborating. The equations are obtained by KCL of the above image.
Here ##V_1##, ##V_x##, ##V_{in}## and ##V_{out}## are variables and all else are constants. I need to find the relation between ##V_{in}## and ##V_{out}##.
 
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  • #7
Hi, I found the solution using the method of determinants. It was not difficult. Thanks.
 
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  • #8
Debdut said:
Hi, I found the solution using the method of determinants. It was not difficult. Thanks.
If not overly long, why not write it here so others can benefit from it?
 
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FAQ: Find the relation between 2 variables

What is the purpose of finding the relationship between two variables?

Finding the relationship between two variables helps in understanding how one variable influences or is associated with another. This can be crucial for making predictions, identifying trends, and establishing causality in various fields such as economics, biology, engineering, and social sciences.

What are the common methods to determine the relationship between two variables?

Common methods include correlation analysis, regression analysis, scatter plots, and contingency tables. Correlation analysis measures the strength and direction of the relationship, while regression analysis models the relationship to make predictions. Scatter plots provide a visual representation, and contingency tables are used for categorical data.

What is the difference between correlation and causation?

Correlation refers to a statistical association between two variables, meaning they tend to move together in some way. Causation implies that one variable directly affects the other. While correlation can suggest a relationship, it does not prove that changes in one variable cause changes in the other.

How can you interpret the correlation coefficient?

The correlation coefficient, typically denoted as 'r', ranges from -1 to 1. A value close to 1 indicates a strong positive relationship, meaning as one variable increases, the other also increases. A value close to -1 indicates a strong negative relationship, meaning as one variable increases, the other decreases. A value around 0 suggests no linear relationship.

What are some potential pitfalls when analyzing the relationship between two variables?

Potential pitfalls include assuming causation from correlation, ignoring outliers that can skew results, failing to consider confounding variables that may influence the relationship, and overfitting models to the data. It's important to use proper statistical techniques and consider the context of the data to avoid these issues.

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