There's a lot of details

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Hypothesis testing with dependent samples.

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What to do if you can't assume anything about your distributions.

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A simpler alternative to $p$-values.

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One way to think about dependent random variables.

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Short, fat matricesa are useful, actually.

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Pick your poison.

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Yes, yes you must

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Frequentist algorithms are often Bayesian.

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Hypothesis testing step-by-step.

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Most people get $p$-values wrong. This is how to understand and apply them correctly.

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How to optimise basically any function.

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Time series without time.

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Same ideas, different framework.

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Generalised Additive Models look harder than they actually are.

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NNs are easier than you think

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Adding time varying components to time varying regression

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We extend our ideas of how to do regression to a more complex class of functions.

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Simple time-varying regression

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How to handle struture in your regressions

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