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The problem with ordinal independent variable is that since, by definition, the true metric intervals between its levels are not known, no appropriate type relationship - apart from umbrella "monotonic" - can be assumed apriori. We have to do something about it, for example - to "screen or to combine variants" or to "prefer what maximizes ...
$\begingroup$ > Personally, I don't find the independent/dependent variable language to be that helpful. Those words connote causality, but regression can work the other way round too (use Y to predict X). The independent/dependent variable language merely specifies how one thing depends on the other.
Also data that forms an X or a V or a ^ or < or > will all give covariance 0, but are not independent. If y = sin(x) (or cos) and x covers an integer multiple of periods then cov will equal 0, but knowing x you know y or at least |y| in the ellipse, x, <, and > cases.
If you log the independent variable x to base b, you can interpret the regression coefficient (and CI) as the change in the dependent variable y per b-fold increase in x. (Logs to base 2 are therefore often useful as they correspond to the change in y per doubling in x, or logs to base 10 if x varies over many orders of magnitude, which is ...
"Independent" variables: time spent (% at work, % sleeping, % exercising), body mass composition (% fat, % muscle, % bone) Dependent variable: Smoker (1) or Non-Smoker (0) What kind of regression model should I use when subsets of the "independent" variables are percentages and are therefore not completely independent of each other?
The above depicts a regression model object with GDP as the dependent variable and FDI lag 1 & lag 2 as the independent variable. You also need to specify the data frame you are using. In this case, I call it econdata. You can readily extract the main related statistical output of that regression by using the very handy summary() function.
Stationarity should be sought for both to avoid any spurious correlations (that some or all variables actually just increase/decrease over time, independent of other factors), and there are methods of correction that can be applied to the entire model (i.e., including a trend as a DV) or just to specific variables (i.e., taking first differences of non-stationary variables) to address this if ...
Should you scale your education variable accordingly and rerun your model, simply multiply the coefficient by the constant (i.e., 525,600) to arrive back at the slope coefficient associated with education expressed in yearly units.
Your "Reality" variable with a beta of 2422.87 is suspect, despite a statistically significant p-value. This should be further evidence of using a different regression model. Using Poisson or Negative Binomial regression will have different interpretations of the betas though, so be careful.
I would like to find the correlation between a continuous (dependent variable) and a categorical (nominal: gender, independent variable) variable. Continuous data is not normally distributed. Before, I had computed it using the Spearman's $\rho$. However, I have been told that it is not right.