refers to the degree of correlation among independent variables in a regression model. a. Multicollinearity b. Tolerance c. Rank d. Confidence level

Option A: Multicollinearity

Step-by-step explanation:

Multicollinearity is said to be present when there are high correlations between two or more independent variables called predictor variables. In simple terms, Multicollinearity refers to when the independent variables present in a regression model are correlated.

Tolerance is simply used to detect Multicollinearity.

Rank is basically the model in which the ranks of the dependent variable are said to have regressed on a set of characteristics in the experiment known as covariates.

Confidence level is the percentage of all possible samples that we can expect to be included within the true population parameter.

Looking at all the definitions, the one that fits with the statement in the question is Multicollinearity.

8 apples are left.

step-by-step explanation:

10-2=8

10 apples

take 2

now u have 8

constants mean the stopped points or the points on a graph that nothing is changing, hope this

step-by-step explanation: