What are two reasons why multiple regression Analyses Cannot completely establish causation?

What are two reasons that multiple regression designs cannot completely establish causation? They cant establish temporal precedence; researchers cant control for variables they don’t measure (there could be a third variable that they didn’t measure that is responsible for the relationship);

Which of the three rules of causation is almost always met by a bivariate correlation? Covariance of cause and effect. … Give examples of some questions you can ask to evaluate the externals validity of a correlation study.

Why can’t a simple BIVARIATE correlational study meet all three rules for establishing causation? (1) Covariance: yes, proved that there is a correlation! (3) Internal Validity: third variables are not usually controlled for! … this can test causal claims.

Give at least three phrases indicating that a study used a multiple–regression analysis. What are two reasons that multiple regression analyses cannot completely establish causation? … With all causal claims being confirmed, it can establish evidence for the proposed variables.

Pattern and Parsimony. a variety of correlational studies that all point to a single, causal effect. -there is a pattern of results that is best explained by a single parsimonious casual explanation.

Some characters are parsimony uninformative: the minimum number of steps for these characters is the same for all possible trees, which does not allow us to choose among alternative trees. … For these characters, the minimum number of character changes (one step) will be the same for all possible trees.

In general, parsimony is the principle that the simplest explanation that can explain the data is to be preferred. In the analysis of phylogeny, parsimony means that a hypothesis of relationships that requires the smallest number of character changes is most likely to be correct.

For example, site 5 favors tree I over trees II and III, and is thus said to support tree I. The tree supported by the largest number of informative sites is the most parsimonious tree.

Fitch’s algorithm is based on this idea. With respect to a (fixed) input tree, this algorithm takes as input a node u of the tree and outputs a pair (R,C), where R is the set of bases that can label u in an optimally scoring tree rooted at u and C is the score (or cost) of such an optimally scoring tree.