The assumption that there are no exact linear relationships among the independent variables in a multiple linear regression model fails if , where n is the sample size and k is the number of parameters. a. n < k + 1 b. n > 2 c. n = k + 1 d. n > k

a. n < k + 1

Step-by-step explanation:

Multiple linear regression model is used for analyzing the linear relationship between the independent variables and dependent variable. It is used when there is the need to predict the relationship of a variable based on the values of two or more other variables. These variables could be nominal, ordinal, or interval/ratio level variables.

Some assumptions of multiple linear regression analysis are:

i.  There must be a linear relationship between the outcome variable and the independent variables.

ii. The residuals are normally distributed.

iii. That the independent variables are not highly correlated with each other.

The assumption that there are no exact linear relationships among the independent variables in a multiple linear regression model fails if n < k + 1.

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step-by-step explanation: