Help Proving That $T_n \sim t_n$ with Z and Y Independent

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In summary: {2\pi}}e^{-\frac{z^2}{2}}\int_{0}^{\infty} \frac{1}{\sqrt{2\pi y}}e^{-\frac{y}{2}}e^{-\frac{n}{2y}z^2} dy \\&= \frac{1}{\sqrt{2\pi}}e^{-\frac{z^2}{2}} \cdot \frac{1}{\sqrt{2\pi}} \int_{0}^{\infty} e^{-\frac{y}{2}}e^{-\frac{n}{2y}z^2} dy \\&= \frac{1}{\sqrt{2\pi}}
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evinda
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Hello! (Wave)

Could you help me at the following exercise? (Thinking)

Let $Z \sim N(0,1) $ , $Y \sim \chi_n^2$ and $Z,Y$ independent .

Show that the random variable $T_n := \frac{Z}{ \sqrt{\frac{Y}{n}}} \sim t_n$.
 
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Hint: Use the fact that $\frac{Z}{ \sqrt{Y/n}}$ has the same distribution as $Z \sqrt{n/Y}$.We need to prove that $T_n \sim t_n$. Let's start by noting that $T_n = \frac{Z}{ \sqrt{\frac{Y}{n}}}$, and that $Z$ and $Y$ are independent. We can then use the fact that $\frac{Z}{ \sqrt{Y/n}}$ has the same distribution as $Z \sqrt{n/Y}$.Now, we need to show that $Z \sqrt{n/Y}$ follows a $t_n$ distribution. To do this, we will calculate the probability density function (PDF) of $Z \sqrt{n/Y}$ and show that it is equal to the PDF of a $t_n$ distribution, which is given by\begin{align*}f_{t_n}(z) = \frac{\Gamma(\frac{n+1}{2})}{\sqrt{n\pi}\Gamma(\frac{n}{2})} (1+\frac{z^2}{n})^{-\frac{n+1}{2}} \end{align*}The PDF of $Z \sqrt{n/Y}$ is given by\begin{align*}f_{Z \sqrt{n/Y}}(z) &= \int_{0}^{\infty} \frac{1}{\sqrt{2\pi}}e^{-\frac{z^2}{2}}\frac{1}{\sqrt{2\pi y}}e^{-\frac{y}{2}}\frac{1}{\sqrt{n/y}}e^{-\frac{n}{2y}}dy \\&= \frac{1}{\sqrt{2\pi}}e^{-\frac{z^2}{2}}\int_{0}^{\infty} \frac{1}{\sqrt{2\pi y}}e^{-\frac{y+nz^2}{2}} dy \\&= \frac{1}{\sqrt
 

FAQ: Help Proving That $T_n \sim t_n$ with Z and Y Independent

What is the significance of proving that $T_n \sim t_n$?

The proof of $T_n \sim t_n$ is important because it establishes the distributional equivalence of the two variables, which allows for the use of the t-distribution to approximate the sampling distribution of the mean of a population. This is a crucial aspect in many statistical analyses and hypothesis testing.

Can you explain the concept of independence between Z and Y?

Independence between Z and Y means that the values of one variable do not affect or influence the values of the other variable. In other words, the occurrence of one event does not affect the probability of the other event happening. In this context, it means that the two variables are not related and their values are not dependent on each other.

What is the relationship between Tn and tn?

Tn and tn are related through their respective distributions - the t-distribution and the standard normal distribution. Tn is a random variable that follows a t-distribution with n-1 degrees of freedom, while tn is a random variable that follows a standard normal distribution. The proof of $T_n \sim t_n$ shows that the two distributions are equivalent.

How can Z and Y be proven to be independent?

Z and Y can be proven to be independent by showing that the joint probability distribution of the two variables can be expressed as the product of their individual probability distributions. In other words, the probability of both events occurring together is equal to the probability of each event occurring separately multiplied together.

What are the implications of $T_n \sim t_n$ for statistical inference?

The implications of $T_n \sim t_n$ are significant for statistical inference as it allows for the use of the t-distribution to approximate the sampling distribution of the mean of a population. This enables the use of t-tests and confidence intervals, which are commonly used in statistical analyses and hypothesis testing. It also provides a more accurate and robust method of estimating the mean of a population compared to using the standard normal distribution.

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