<p>If you are going to do data analysis, you need to understand how the fitting procedure works. This means a lot of statistics, and new terminology and so on, which is hard and takes much time.</p>
<p>If you want to start to fit, e.g., a spectrum, you should definitely read the <a href="http://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/manual.html" rel="nofollow noreferrer">XSPEC</a> guide. You can find a pdf online as well.</p>
<p>In short, fitting is to take a model and measure how well this model fits to your data. To quantify this &quot;how well&quot;, <strong>usually</strong> the chi-squared distribution is considered:</p>
<p><span class="math-container">$\chi^2 =  \sum\limits_{i=1}^n(\frac{X_i - \mu_i}{\sigma_i})^2$</span></p>
<p>where <span class="math-container">$X$</span> is your data (observed value), <span class="math-container">$\mu$</span> is your expected value (which, in this case, corresponds with the prediction of the model), and <span class="math-container">$\sigma$</span> is the variance on your data point (the error).</p>
<p>This is the most general information that you need.
You can read more <a href="http://physics.ucsc.edu/%7Edrip/133/ch4.pdf" rel="nofollow noreferrer">here</a>, and especially on the <a href="http://www2.units.it/ipl/students_area/imm2/files/Numerical_Recipes.pdf" rel="nofollow noreferrer">Numerical Recipes</a> (don't be scared by the huge format of the last document, the pages you need are only few around Chapter 15).</p>
<p>For fun, you can just try to play with XSPEC, to take confidence, and see at least how things change when you change your model or your data (especially if you know which model is the best-fit for your data). To understand everything will take years, but if you never start, you'll never arrive ;)</p>

CC BY-SA 3.0
2014-11-29T04:23:16.263Z
2014-11-29T04:23:16.263Z
2020-06-17T09:47:40.653Z
Py-ser
<p>Whenever I read any Astro papers in <a href="http://arxiv.org/" rel="nofollow">http://arxiv.org/</a> they usually talk about Data fitting or modelling or sentences like 'we fitted the spectral region by analytic functions  i.e. by multi-component Gaussian model plus continuum'.</p>

<p>Is there any websites, online books or maybe YouTube videos which teaches these techniques in detail?</p>

<p>Any info will be much appreciated!</p>

CC BY-SA 3.0
2014-11-28T23:10:45.650Z
The link itself is dead and gone (well, it leads *somewhere*); anyone interested may want [this](http://arxiv.org/) one.
HDE 226868
CC BY-SA 3.0
2014-11-29T01:07:05.190Z
oops sorry. posted it blindly. will edit now.
Hari Seldon
<astrophysics><data-analysis>
Where can I learn these Astrophysical techniques? [Read below]
Hari Seldon
# Where can I learn these Astrophysical techniques? [Read below]

(Asked by: username_0 on 2014-11-28)

Whenever I read any Astro papers in <http://arxiv.org/> they usually talk about Data fitting or modelling or sentences like 'we fitted the spectral region by analytic functions i.e. by multi-component Gaussian model plus continuum'.

Is there any websites, online books or maybe YouTube videos which teaches these techniques in detail?

Any info will be much appreciated!

**Tags:** astrophysics, data-analysis

> **Comments:**
> **username_1**: The link itself is dead and gone (well, it leads *somewhere*); anyone interested may want [this](http://arxiv.org/) one.  
> **username_0**: oops sorry. posted it blindly. will edit now.  

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## 1 answers

### Answer by username_2 on 2014-11-29. Score: 2

If you are going to do data analysis, you need to understand how the fitting procedure works. This means a lot of statistics, and new terminology and so on, which is hard and takes much time.

If you want to start to fit, e.g., a spectrum, you should definitely read the [XSPEC](http://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/manual.html) guide. You can find a pdf online as well.

In short, fitting is to take a model and measure how well this model fits to your data. To quantify this "how well", **usually** the chi-squared distribution is considered:

$\chi^2 = \sum\limits\_{i=1}^n(\frac{X\_i - \mu\_i}{\sigma\_i})^2$

where $X$ is your data (observed value), $\mu$ is your expected value (which, in this case, corresponds with the prediction of the model), and $\sigma$ is the variance on your data point (the error).

This is the most general information that you need.
You can read more [here](http://physics.ucsc.edu/%7Edrip/133/ch4.pdf), and especially on the [Numerical Recipes](http://www2.units.it/ipl/students_area/imm2/files/Numerical_Recipes.pdf) (don't be scared by the huge format of the last document, the pages you need are only few around Chapter 15).

For fun, you can just try to play with XSPEC, to take confidence, and see at least how things change when you change your model or your data (especially if you know which model is the best-fit for your data). To understand everything will take years, but if you never start, you'll never arrive ;)


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