This is part of probstat.

In this section, we shall discuss linear regression. We shall focus on one-variable linear regression.

เนื้อหา

1 Model
2 The least squares estimators
3 Distribution of regression parameters
4 Statistical tests on regression parameters

Model

We consider two variables and $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

Y

where is a function of

X

. We refer to as independent or input variable, and as a dependent variable. We consider linear relationship between independent variable and dependent variable. We assume that there exist hidden variables and such that

where $e$ is a random error. We further assume that the error is unbiased, i.e., and is independent of .

Input: As an input to the regression process, we are given a set of data points: generated from the previous equation.

Goal: We want to estimate $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

\alpha

and

\beta

.

The least squares estimators

Denote our estimate for $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

\alpha

as

A

and for

\beta

as

Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } } Error Too many requests (f061ab2) If you report this error to the Wikimedia System Administrators, please include the details below. Request served via cp5021 cp5021, Varnish XID 605429087 Upstream caches: cp5021 int Error: 429, Too many requests (f061ab2) at Sat, 01 Aug 2026 01:36:26 GMT Sensitive client information IP address: 158.108.32.49

. Using both variables as estimator, the error at data point

(x_{i},y_{i})

, the error is

$y_{i}-(A+Bx_{i})=y_{i}-A-Bx_{i}$ .

We focus more on the sum of squared errors, i.e.,

$SS=\sum _{i=1}^{n}(y_{i}-A-Bx_{i})^{2}$ .

The method of least squares use the parameters that minimize the squared errors as an estimator. Therefore, we want to find $A$ and $B$ that minimize $SS$ . To do so, we partially differentiate $SS$ with respect to $A$ and $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

B

:

${\frac {\partial }{\partial A}}SS=-2\sum _{i=1}^{n}(y_{i}-A-Bx_{i})$ (Eq1)

${\frac {\partial }{\partial B}}SS=-2\sum _{i=1}^{n}x_{i}(y_{i}-A-Bx_{i})$ (Eq2)

We set these two equations to zero to find the maximum and obtain these two equations we have to solve.

$\sum _{i=1}^{n}y_{i}=nA+B\sum _{i=1}^{n}x_{i}$

$\sum _{i=1}^{n}x_{i}y_{i}=A\sum _{i=1}^{n}x_{i}+B\sum _{i=1}^{n}x_{i}^{2}$

Before solving these two equations, let's define

${\bar {y}}=\sum _{i=1}^{n}y_{i}/n,\ \ \ \ {\bar {x}}=\sum _{i=1}^{n}x_{i}/n.$

We start by rewriting the first equation (Eq1) as

$A={\bar {y}}-B{\bar {x}},$ (Eq3)

and put it in (Eq2) to get

$\sum _{i=1}^{n}x_{i}y_{i}=({\bar {y}}-B{\bar {x}})\sum _{i=1}^{n}x_{i}+B\sum _{i=1}^{n}x_{i}^{2}$ .

With some calculation, we get

.

To find $A$ , we can just use equation (Eq3).

Estimated regression parameters. Using the least squares method, we obtain the following estimates

$Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

B={\frac {\sum _{i=1}^{n}x_{i}y_{i}-n{\bar {x}}{\bar {y}}}{\sum _{i=1}^{n}x_{i}^{2}-n{\bar {x}}^{2}}}

,

and

$A={\bar {y}}-B{\bar {x}}$ .

Distribution of regression parameters

Although the estimators $A$ and $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

B

are least-squares estimators, we are not sure if they are good estimators. In this section, we shall discuss various properties of these parameters.

We make an assumption on the error, that is is normally distributed with variance $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

\sigma ^{2}

. Therefore,

$y_{i}\sim Normal(\alpha +\beta x_{i},\sigma ^{2})$ .

We shall start with $B$ . First, note that $x_{i}$ 's are inputs and are not random. Therefore, if we look at the formula for $B$ , we see that $B$ is actually a sum of independent normal random variables. This implies that $B$ is a normal random variable. If we can find its mean and its variance, we have a complete information about the distribution of $B$ .

We can calculate the expectation and variance of $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

B

as follows.

$Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } } Error Too many requests (f061ab2) If you report this error to the Wikimedia System Administrators, please include the details below. Request served via cp5021 cp5021, Varnish XID 635011189 Upstream caches: cp5021 int Error: 429, Too many requests (f061ab2) at Sat, 01 Aug 2026 01:36:08 GMT Sensitive client information IP address: 158.108.32.49$ .

$Var(B)={\frac {\sigma ^{2}}{\sum _{i=1}^{n}x_{i}^{2}-n{\bar {x}}^{2}}}$ .

We can also calculate the expectation and variance of $A$ .

$E[A]=\alpha$ .

$Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } } Error Too many requests (f061ab2) If you report this error to the Wikimedia System Administrators, please include the details below. Request served via cp5021 cp5021, Varnish XID 634781933 Upstream caches: cp5021 int Error: 429, Too many requests (f061ab2) at Sat, 01 Aug 2026 01:36:09 GMT Sensitive client information IP address: 158.108.32.49$ .

Statistical tests on regression parameters

We focus on how to test the null hypothesis:

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Error

Too many requests (f061ab2)

H_{0}:\ \ \ \beta =0.

Since $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

B

is normal with mean

\beta

and variance

\sigma ^{2}/(\sum _{i=1}^{n}x_{i}^{2}-n{\bar {x}}^{2})

, we know that the statistic

$Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

{\frac {B-\beta }{\sigma /{\sqrt {(\sum _{i=1}^{n}x_{i}^{2}-n{\bar {x}}^{2})}}}}

is unit normal and it is possible to perform various statistical tests on $\beta$ based on the estimated value $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

B

if we know parameter

\sigma ^{2}

. However, usually, we do not.

We end up with a situation similar to when we perform sampling on populations with unknown variances. Another key quantity in this case is the sum of squares of the residuals:

$Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

SS_{R}=\sum _{i=1}^{n}(y_{i}-A-Bx_{i})^{2}

Note that if we substitute $A$ with and $B$ with $\beta$ the term $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

y_{i}-\alpha -\beta x_{i}

is exactly the errors (which is normally distributed with mean 0 and variance

\sigma ^{2}

). This motivate the fact that

SS_{R}

can be used to estimate

\sigma ^{2}

:

$E\left[{\frac {SS_{R}}{n-2}}\right]=\sigma ^{2}$ .

More over, it can be shown that

,

and $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

SS_{R}

is independent of

Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } } Error Too many requests (f061ab2) If you report this error to the Wikimedia System Administrators, please include the details below. Request served via cp5021 cp5021, Varnish XID 575948569 Upstream caches: cp5021 int Error: 429, Too many requests (f061ab2) at Sat, 01 Aug 2026 01:36:14 GMT Sensitive client information IP address: 158.108.32.49

. These two facts implies that

$Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

{\frac {\left({\frac {B-\beta }{\sigma /{\sqrt {(\sum _{i=1}^{n}x_{i}^{2}-n{\bar {x}}^{2})}}}}\right)}{\sqrt {\frac {SS_{R}}{\sigma ^{2}(n-2)}}}}={\frac {\left({\frac {B-\beta }{1/{\sqrt {(\sum _{i=1}^{n}x_{i}^{2}-n{\bar {x}}^{2})}}}}\right)}{\sqrt {\frac {SS_{R}}{(n-2)}}}}={\frac {B-\beta }{\sqrt {\frac {SS_{R}}{(n-2)S_{xx}}}}}\sim t_{n-2},

where $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

S_{xx}=\sum _{i=1}^{n}x_{i}^{2}-n{\bar {x}}^{2}

.

Therefore, if we want to perform a hypothesis testing if $\beta =0$ , we can check if $Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } }$

Error

Too many requests (f061ab2)

{\frac {B-\beta }{\sqrt {\frac {SS_{R}}{(n-2)S_{xx}}}}}

deviate far enough from the t-distribution with

Wikimedia Error * { margin: 0; padding: 0; } body { background: #fff; font: 15px/1.6 sans-serif; color: #333; } .content { margin: 7% auto 0; padding: 2em 1em 1em; max-width: 640px; display: flex; flex-direction: row; flex-wrap: wrap; } .footer { clear: both; margin-top: 14%; border-top: 1px solid #e5e5e5; background: #f9f9f9; padding: 2em 0; font-size: 0.8em; text-align: center; } img { margin: 0 2em 2em 0; } a img { border: 0; } h1 { margin-top: 1em; font-size: 1.2em; } .content-text { flex: 1; } p { margin: 0.7em 0 1em 0; } a { color: #0645ad; text-decoration: none; } a:hover { text-decoration: underline; } code { font-family: sans-serif; } summary { font-weight: bold; cursor: pointer; } details[open] { background: #970302; color: #dfdedd; } .text-muted { color: #777; } @media (prefers-color-scheme: dark) { a { color: #9e9eff; } body { background: transparent; color: #ddd; } .footer { border-top: 1px solid #444; background: #060606; } #logo { filter: invert(1) hue-rotate(180deg); } .text-muted { color: #888; } } Error Too many requests (f061ab2) If you report this error to the Wikimedia System Administrators, please include the details below. Request served via cp5021 cp5021, Varnish XID 575127455 Upstream caches: cp5021 int Error: 429, Too many requests (f061ab2) at Sat, 01 Aug 2026 01:36:57 GMT Sensitive client information IP address: 158.108.32.49

degrees of freedom.

Notes: This is fairly similar to the use of the t-distribution for the sample mean when the variance of the population is unknown, where the quantity $SS_{R}$ acts as $S^{2}$ in that case.

Probstat/notes/regression

เนื้อหา

Model

Error

Error

The least squares estimators

Error

Error

Error

Distribution of regression parameters

Error

Error

Error

Statistical tests on regression parameters

Error

Error

Error

Error

Error

Error

Error

Error

Error

Error

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