How Do You Solve for Alpha and Beta in Exponential Equations?

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In summary: So, in summary, to solve the given system of equations for $\alpha_n$ and $\beta_n$, you can use elimination by multiplying the second equation by a suitable term and then adding it to the first equation. This will result in a simplified expression for $\alpha_n$, which can then be used to find the solution for $\beta_n$ by substitution.
  • #1
Dustinsfl
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\begin{align}
\alpha_nb^n +\beta_nb^{-n}=A_n\\
\alpha_na^n +\beta_na^{-n}=C_n
\end{align}

How does one go from that to
$$
\alpha_n = \frac{A_n/a_n - C_n/b^n}{(b/a)^n-(a/b)^n}
$$
and
$$
\beta_n = \frac{a^nC_n - b^nA_n}{(b/a)^n-(a/b)^n}
$$
 
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  • #2
dwsmith said:
\begin{align}
\alpha_nb^n +\beta_nb^{-n}=A_n\\
\alpha_na^n +\beta_na^{-n}=C_n
\end{align}

How does one go from that to
$$
\alpha_n = \frac{A_n/a_n - C_n/b^n}{(b/a)^n-(a/b)^n}
$$
and
$$
\beta_n = \frac{a^nC_n - b^nA_n}{(b/a)^n-(a/b)^n}
$$

That's a [linear...] system of two equation in the unknown variables $\alpha_{n}$ and $\beta_{n}$...

Kind regards

$\chi$ $\sigma$
 
  • #3
chisigma said:
That's a [linear...] system of two equation in the unknown variables $\alpha_{n}$ and $\beta_{n}$...

Kind regards

$\chi$ $\sigma$

I do know that, but every time I solve it, I don't get the correct answer. I even put it into full version Mathematica and Wolfram online and it just returns an error.

For $\beta$, I keep getting
$$
\beta_n = \frac{b^nC_n - a^nA_n}{\text{the correct denominator}}
$$
 
  • #4
I would use elimination, for example, multiplying the second equation by:

$\displaystyle -\left(\frac{a}{b} \right)^n$

gives us:

$\displaystyle -\alpha_na^{2n}b^{-n}-\beta_nb^{-n}=-C_na^nb^{-n}$

Adding this to the first equation, we find:

$\displaystyle \alpha_n(b^n-a^{2n}b^{-n})=A_n-C_na^nb^{-n}$

$\displaystyle \alpha_n=\frac{A_n-C_na^nb^{-n}}{b^n-a^{2n}b^{-n}}=\frac{\frac{A_n}{a^n}-\frac{C_n}{b^{n}}}{\left(\frac{b}{a} \right)^n-\left(\frac{a}{b} \right)^n}$

The solution for $\displaystyle \beta_n$ can now be found by substitution.
 
  • #5


To solve for alpha and beta in this system of equations, we can use the method of elimination. We can eliminate the variable beta by multiplying the first equation by b^n and the second equation by a^n, resulting in:

\begin{align}
\alpha_nb^{2n}+\beta_n&=A_nb^n\\
\alpha_na^{2n}+\beta_n&=C_na^n
\end{align}

Subtracting these equations, we can eliminate beta and solve for alpha:

\begin{align}
\alpha_na^{2n}-\alpha_nb^{2n}&=C_na^n-A_nb^n\\
\alpha_n(a^{2n}-b^{2n})&=C_na^n-A_nb^n\\
\alpha_n&=\frac{C_na^n-A_nb^n}{a^{2n}-b^{2n}}
\end{align}

We can then substitute this value back into one of the original equations to solve for beta. Let's substitute it into the first equation:

\begin{align}
\frac{C_na^n-A_nb^n}{a^{2n}-b^{2n}}b^n +\beta_nb^{-n}&=A_n\\
\frac{C_na^n-A_nb^n}{a^{n}-b^{n}} +\beta_nb^{-n}&=A_n\\
\beta_nb^{-n}&=A_n-\frac{C_na^n-A_nb^n}{a^{n}-b^{n}}\\
\beta_n&=\frac{a^nA_n-a^{2n}C_n+a^{2n}A_n-b^{2n}A_n}{a^{2n}-b^{2n}}\\
\beta_n&=\frac{a^nC_n-a^{2n}C_n+a^{2n}A_n-b^{2n}A_n}{a^{2n}-b^{2n}}\\
\beta_n&=\frac{a^nC_n-b^nA_n}{a^{2n}-b^{2n}}\\
\end{align}

This can be simplified further by factoring out a^n from the numerator and denominator:

\begin{align}
\beta_n&=\frac{a^n(C_n-b^nA_n)}{a^n(a^n-b^n)}\\
\beta_n&=\frac{C_n-b^nA_n}{a^n-b^n}
\
 

FAQ: How Do You Solve for Alpha and Beta in Exponential Equations?

What is the purpose of solving for alpha and beta?

Solving for alpha and beta is a statistical method used to analyze the relationship between two variables. It helps to determine the strength and direction of the relationship, and can also be used to make predictions about future values of one variable based on the other.

How do you calculate alpha and beta?

Alpha (α) and beta (β) are calculated using linear regression analysis. This involves plotting the data points on a scatter plot and finding the line of best fit. Alpha represents the intercept of the line, while beta represents the slope.

What does a high/low value of alpha and beta indicate?

A high value of alpha indicates that the line of best fit intercepts the y-axis at a higher point, while a low value indicates a lower intercept point. A high value of beta indicates a steeper slope, while a low value indicates a flatter slope.

How can alpha and beta be used in hypothesis testing?

In hypothesis testing, alpha and beta are used to determine the significance of the relationship between two variables. The p-value associated with alpha can indicate if the relationship is statistically significant, while the beta value can indicate the strength of the relationship.

Can alpha and beta be used for non-linear relationships?

No, alpha and beta are only applicable for linear relationships. For non-linear relationships, other statistical methods such as polynomial regression or curve fitting may be used to determine the relationship between variables.

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