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Basically, you are on the right track. You will find a discussion about the aspect of normality in Normality of dependent variable = normality of residuals? Some assumptions of the classic linear model are indeed about errors (using residuals as realizations of them): Are they uncorrelated? (Relevant for inference and optimality of the OLS-estimators) Do ...

4

I find the differentiation between the residuals and the raw data unhelpful since both refer more to your actual sample and not the underlying population distribution. It's better to think of as some requirement being "in-group requirements" and others "between group assumptions". For example, variance homonenity is a "between-group assumption" since it say ...

3

The likelihood theory is a fairly general framework. Most textbooks state results for the separated cases of continuous r.vs and for that for discrete r.vs. However mixed cases occur in practice, as is the case here. For a discrete r.v. $A$, the likelihood of an observation $a$ is defined as the probability of getting the observed value $a$, say $p_A(a)$. ...

3

What you describe needs special treatment, it is not what we usually mean by "truncated random variables"-and what we usually mean is that the random variable does not range outside the truncated support, meaning that there is not a concentration of probability mass at the point of truncation. To contrast cases: A) "Usual" meaning of a truncated rv For any ...

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There are some step-by-step guides in Shalizi's notes here : http://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch18.pdf, one being the cars data set from R and another being art and music articles from the New York Times. (Inferring the topic of an article from the words contained in it is a very active research area.) If you don't know/don't want to learn ...

1

I'll answer the question in the title even though I think noone should ever do what I am about to describe. @Emma it is good you came to this site, you should ask instead what is the best way to compare multiple categories of likert scale data. Also you should search for information about what is special about the number 0.05. I knew nothing about ...

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I think you are confusing the true objective function with the learner's objective function. Often, in machine learning and data science, we approximate a function using a model by following the gradient of the loss function with respect to the parameters of the model. For example, let us approximate a true objective function $t(x)$ using a model ...

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You may want to read up on the APRIORI algorithm. It avoids unneccessary itemsets by clever pruning. {A} = 4 ; {B} = 2 ; {C} = 5 ; {D} = 4 ; {E} = 6 B is not frequent, remove. Construct and count two-itemsets (no magic yet, except that B is already out) {A,C} = 3; {A,D} = 3; {A,E} = 4; {C,D} = 3; {C,E} = 5; {D,E} = 3 All of these are frequent ...

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I found a slightly extended definition in this source (which includes a good explanation). Here is a more reliable (published) source: CHARM: An efficient algorithm for closed itemset mining by Mohammed J. Zaki and Ching-jui Hsiao. According to this source: An itemset  is  closed  if  none  of  its  immediate  supersets  has   the  same  ...

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In association rule learning, the confidence of a rule is defined as follows: $$conf(X\Rightarrow Y) = \frac{support(X\cup Y)}{support(X)}$$ The confidence is the amount of times a rule has been encountered in the data, conditional on the amount of times its left hand side was encountered. $100\%$ confidence implies that any record containing $X$ also ...

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Best way would be to take photos of a few birds (together with the colour card) several times under each of a few different lighting conditions. Then model the measured brightness.bird.plumage with brightness.colour.chart as a continuous fixed effect, possibly non-linear, & individual.bird as a random effect. Significant interaction or heteroskedasticity ...

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