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<title>Course on Practical Neuroimaging in Python — Practical neuroimaging analysis</title>
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<div class="section" id="day-13-testing-hypotheses-with-t-and-f-contrasts">
<h1>Day 13: testing hypotheses with t and F contrasts<a class="headerlink" href="#day-13-testing-hypotheses-with-t-and-f-contrasts" title="Permalink to this headline">¶</a></h1>
<p>5 / 22 / 2015</p>
<dl class="docutils">
<dt>Reading:</dt>
<dd><ul class="first last simple">
<li>slides from Jeanette Mumford about linear model. There are many slides, but see these between 17 and 77:
<a class="reference external" href="http://mumford.fmripower.org/2010_summercourse/stat_modeling_2010.pdf">http://mumford.fmripower.org/2010_summercourse/stat_modeling_2010.pdf</a></li>
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<dt>Two recording commenting these slides - may (or may not) help you going through them:</dt>
<dd><ul class="first last">
<li>slides 17-51: <a class="reference external" href="http://nipy.bic.berkeley.edu/practical_neuroimaging/glm_estimation.avi">http://nipy.bic.berkeley.edu/practical_neuroimaging/glm_estimation.avi</a></li>
<li>slides 52-77: <a class="reference external" href="http://nipy.bic.berkeley.edu/practical_neuroimaging/hypothesistesting.avi">http://nipy.bic.berkeley.edu/practical_neuroimaging/hypothesistesting.avi</a></li>
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<li>Motion parameters as confounds;</li>
<li>low frequencies as confounds;</li>
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<li>bias</li>
<li>variance</li>
</ul>
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<li>t and F tests, and associated contrasts, simple Anova;</li>
<li>Example: Contrasts for motion;</li>
<li>Correlation between regressors;</li>
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