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If you want to predict when a brand has high customer value, then the variable measuring that concept becomes the dependent variable in an analysis, which could be logistic regression, for example. … Which is the appropriate staistical method often depends on the distribution of the variable, which in case of dichotomous variables like answers to yes/no questions is often binomial, and hence logistic regression …

Assuming a large sample, I suggest using cross-validation. Randomly split your data set in two (or more) sub-sets Build your model on one sub-set of the data using the Bonferroni correction you su …

Assume you have two linear regression models, one regressing $Y_1$ on $X_1$ the other $Y_2$ on $X_2$. This gives error variance $\sigma_1^2$ and $\sigma_2^2$. …

I am interested in the case when we have a linear regression model through the origin $(0,0)$. …

In words, I would like to show that sample means and OLS regression coefficient estimates are independent random variables. Can you provide guidance on how to approach the problem? …

A --correct-- regression model of the type discussed above will also provide a correct treamtent estimate. … I have once discussed the use of regression models for estimating average treatment effects from observational studies here …

The choice between one multinomial and a series of logistic regressions is in most cases relatively artificial. Since in both approaches you select one baseline category (reference) with regard to whi …

The background of the following question is a research proposal on which I currently advise. I believe the type of experimental design is adequate to the question. However, it turns out hard to commun …

I think that the term 'by chance' is not clearly defined as long as you do not have a specific hypothesis you want to test. You could regard the full text as your population. The complete 'census' o …

The answer is to use the coefs argument of poly. The coefficients used in the orthogonal transformation are saved as attributes in a poly object. They have to be saved for later use. In the implemen …

I consider a proportional odds (ordinal regression) model, as described in Agresti (2002) and estimated by R base package function MASS. The software reports t-values but no p-values in summary. … In simple linear regression $df=n-2$ where $n$ the sample size. Is this also the case for ordinal linear regression? …

The background are model-based estimates for $\hat{E}(y|x)$ and $V(\hat{E}(y|x))$, as in regression. …

The classification problem $P(Z=1|X)$ then can be solved by machine learning or other regression models, such as logistic regression or SVM or any other technique you like. … However there are also survival models using for example random forests or regression trees which are more flexible. …

In structural models, you would add $Z_3$ to both equations. It will then have a direct effect $\beta_{z31}$, an indirect effect $(\beta_{x}\beta_{z32})$, and a total effect (sum of both) on $Y_i$. Om …

You can easily model this in structural modeling software such as Mplus. You need a model of X --> Z --> Y where Z is the mediator and inspect fit and/or residual correlations. If the model fit is p …