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1 $begingroup$ proportional odds logistic regression would likely be a smart approach to this query, but I don't know if It truly is out there in SPSS. $endgroup$
Q2: A 99% confidence interval is broader than the usual ninety five%, all else currently being equivalent. As a result, It really is a lot more probably that it will include the true value. See the distinction above concerning specific and precise. If I generate a self confidence interval narrower with reduce variability and higher sample sizing it results in being additional exact since the values protect a smaller sized variety.
$begingroup$ As an alternative to depending on a test for normality in the residuals, test assessing the normality with rational judgment. Normality exams will not show you that your info is usual, only that it's not. But provided that the information absolutely are a sample you are able to be really certain they're not in fact ordinary without a check.
not be required in the very first bullet I mentioned, so The 2 will not should go hand and hand continually.
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That condition range can be extremely high when variables are calculated on scales with disparate ranges. Rescaling will then soak up the majority of the "badness" in $X$ throughout the scale components. The resulting problem are going to be significantly better conditioned. $endgroup$
Managing it as some esoteric issue just indicates now we are going to have (at the least) yet another human being strolling close to saying, "I am ninety five% assured the worth is In this particular variety." It could barely adjust your reply to correct it. One other problems you skirt could possibly be overlooked when you improved that one statement. $endgroup$
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But You can not take a look at tightness without contemplating accuracy very first. Some self confidence intervals are specific; All those are exact because they have the particular protection which they advertise. A ninety five% self confidence interval can be approximate as it works by using an asymptotic distribution. Approximate intervals determined by asymptotics are for the finite sample measurement $n$ not gonna contain the marketed coverage, that is the coverage you would probably get Should the asymptotic distribution were the exact distribution.
In the same way, if a univariate random variable $X$ has become imply centered, then $ rm var (X) = E(X^2)$ and the variance can be believed from a sample by thinking about the sample signify in the squares in the observed values.
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