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Computing Pearson correlation with one dichotomous variable is called a point-biserial correlation. So you can do that and you find information about it under that term, e. g. https://en.wikipedia.org/wiki/Point-biserial_correlation_coefficient From that same page I may cite: We can test the null hypothesis that the correlation is zero in the population. ...


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A couple of points: I guess you want to use the linear mixed model to impute the missing values for the outcome Average to be used in another procedure. However, if your only purpose is to fit the mixed model, then you do not need to impute the missing data in the outcome variable. The model will provide you with correct inferences under the missing at ...


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Given a linear regression model $$E[y \vert x] = \alpha + \beta \cdot \ln(x),$$ the partial derivative with respect to $x$ is $$\frac{\partial y }{\partial x} = \beta \cdot \frac{1}{x}.$$ Solving for $\beta$, you get $$ \beta = \frac{\partial y }{\partial x} \cdot x \approx \frac{\Delta y}{\Delta x/x}.$$ I often find it helpful to multiply the denominator ...


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By modeling a predictor's effect at each level you get a more precise estimate of its association with the outcome at that level. Remember that a multilevel model splits the variance in the outcome across the N levels you specify (sounds like 3 in your case). So you have separate intercepts for patients, wards, and hospitals. Predictors at each of those ...


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Your 4 questions are difficult to answer because your are presenting summary info . The ACF and PACF are descriptive/summary statistics but not always easily inferential. Your 1000 valued time series may have either level/step shifts or be a series that needs to be differenced or transformed or segmented. There may be some seasonality but only your data ...


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If I understood you correctly, your response variable $y$ measures consumption quantities of meat-replacements products, and you are trying to study the relation between this variable and political preferences measured as "Thoughts on income inequality", "Thoughts on family" etc. You have performed a multiple linear regression model, and obtained the ...


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Syre, you say about the linear regression A linear regression residual close to zero means that the model is a good fit for the observed value. A negative residual means that the model overestimates the effect of the independent variables in that particular case. and I think this is where the misunderstanding starts - a linear regression where you ...


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From the comments to the question, it has become clear that the goal is to assess the discriminating power of the features. The second suggestion in the question is also known as a wrapper approach, because it is a sequential selection scheme wrapped around the performance (e.g. leave-one-out) of some classifier. This has the disadvantage that it depends on ...


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