notes: use math mode to write "t-test"

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Michele Guerini Rocco 2020-05-28 20:49:08 +02:00
parent d9af353135
commit 59176ab4cf
3 changed files with 8 additions and 8 deletions

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@ -181,7 +181,7 @@ $$
$$ $$
In order to compare the values $m_e$ and $m_0$, the following compatibility In order to compare the values $m_e$ and $m_0$, the following compatibility
t-test was applied: $t$-test was applied:
$$ $$
p = 1 - \text{erf}\left(\frac{t}{\sqrt{2}}\right)\ \with p = 1 - \text{erf}\left(\frac{t}{\sqrt{2}}\right)\ \with
t = \frac{|m_e - m_o|}{\sqrt{\sigma_e^2 + \sigma_o^2}} t = \frac{|m_e - m_o|}{\sqrt{\sigma_e^2 + \sigma_o^2}}
@ -267,7 +267,7 @@ $$
As stated above, the median is less sensitive to extreme values with respect to As stated above, the median is less sensitive to extreme values with respect to
the mode: this lead the result to be much more precise. Applying again the the mode: this lead the result to be much more precise. Applying again the
aforementioned t-test to this statistic: aforementioned $t$-test to this statistic:
- $t=0.761$ - $t=0.761$
- $p=0.446$ - $p=0.446$
@ -344,7 +344,7 @@ $$
\text{observed FWHM: } w_o = \num{4.06 \pm 0.08} \text{observed FWHM: } w_o = \num{4.06 \pm 0.08}
$$ $$
Applying the t-test to these two values gives Applying the $t$-test to these two values gives
- $t=0.495$ - $t=0.495$
- $p=0.620$ - $p=0.620$

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@ -369,7 +369,7 @@ See @sec:res_comp for results compatibility.
In order to compare the values $x_L$ and $x_{\chi}$ obtained from both methods In order to compare the values $x_L$ and $x_{\chi}$ obtained from both methods
with the correct ones ({$\alpha_0$, $\beta_0$, $\gamma_0$}), the following with the correct ones ({$\alpha_0$, $\beta_0$, $\gamma_0$}), the following
compatibility t-test was applied: compatibility $t$-test was applied:
$$ $$
p = 1 - \text{erf}\left(\frac{t}{\sqrt{2}}\right)\ \with p = 1 - \text{erf}\left(\frac{t}{\sqrt{2}}\right)\ \with
@ -412,7 +412,7 @@ $\chi^2$ results:
Table: $\chi^2$ results compatibility. Table: $\chi^2$ results compatibility.
It can be concluded that, with both methods, the parameters $\alpha$ and $\beta$ It can be concluded that, with both methods, the parameters $\alpha$ and $\beta$
were recovered succefully, while $\gamma$ is incompatible. However, the were recovered successfully, while $\gamma$ is incompatible. However, the
covariance was estimated using the Cramér-Rao bound, so the errors may be covariance was estimated using the Cramér-Rao bound, so the errors may be
underestimated, which must be the case for $\gamma$. underestimated, which must be the case for $\gamma$.
@ -426,9 +426,9 @@ The issue remains unsolved as no explanation was found.
## Isotropic hypothesis testing ## Isotropic hypothesis testing
What if the probability distribution function were isotropic? What if the probability distribution function were isotropic?
Is this hypothesys compatible with the observation? Is this hypothesis compatible with the observation?
If $F$ is isotropic, $\alpha_I$, $\beta_I$ and $\gamma_I$ would be $1/3$ , 0, If $F$ is isotropic, $\alpha_I$, $\beta_I$ and $\gamma_I$ would be $1/3$ , 0,
and 0 respectively, since this gives $F_I = 1/{4 \pi}$. The t-test gives a and 0 respectively, since this gives $F_I = 1/{4 \pi}$. The $t$-test gives a
$p$-value approximately zero for all the three parameters, meaning that there $p$-value approximately zero for all the three parameters, meaning that there
is no compatibility at all with this hypothesis. is no compatibility at all with this hypothesis.

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@ -212,7 +212,7 @@ $$
where $\chi_r^2$ is the $\chi^2$ per degree of freedom, proving a good where $\chi_r^2$ is the $\chi^2$ per degree of freedom, proving a good
convergence. convergence.
In order to compare $P^{\text{oss}}_{\text{max}}$ with the expected value In order to compare $P^{\text{oss}}_{\text{max}}$ with the expected value
$P_{\text{max}} = 10$, the following compatibility t-test was applied: $P_{\text{max}} = 10$, the following compatibility $t$-test was applied:
$$ $$
p = 1 - \text{erf}\left(\frac{t}{\sqrt{2}}\right)\ \with p = 1 - \text{erf}\left(\frac{t}{\sqrt{2}}\right)\ \with