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We Know That the Linear Regression Must Be Used If

question 42

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We know that the linear regression must be used if we have a strong relationship between We know that the linear regression must be used if we have a strong relationship between   and   (or in other words when   is proportional to   ) . Choose the statement that best describes the values of   and   which indicate the fact that the relationship is very strong. ​ A) The value of   is very large and the value of   is close to 1. B) The value of   is very large and the value of   is close to 0. C) The value of   is small and the value of   is close to 0. D) The value of   is small and the value of   is close to 1. and We know that the linear regression must be used if we have a strong relationship between   and   (or in other words when   is proportional to   ) . Choose the statement that best describes the values of   and   which indicate the fact that the relationship is very strong. ​ A) The value of   is very large and the value of   is close to 1. B) The value of   is very large and the value of   is close to 0. C) The value of   is small and the value of   is close to 0. D) The value of   is small and the value of   is close to 1. (or in other words when We know that the linear regression must be used if we have a strong relationship between   and   (or in other words when   is proportional to   ) . Choose the statement that best describes the values of   and   which indicate the fact that the relationship is very strong. ​ A) The value of   is very large and the value of   is close to 1. B) The value of   is very large and the value of   is close to 0. C) The value of   is small and the value of   is close to 0. D) The value of   is small and the value of   is close to 1. is proportional to We know that the linear regression must be used if we have a strong relationship between   and   (or in other words when   is proportional to   ) . Choose the statement that best describes the values of   and   which indicate the fact that the relationship is very strong. ​ A) The value of   is very large and the value of   is close to 1. B) The value of   is very large and the value of   is close to 0. C) The value of   is small and the value of   is close to 0. D) The value of   is small and the value of   is close to 1. ) . Choose the statement that best describes the values of We know that the linear regression must be used if we have a strong relationship between   and   (or in other words when   is proportional to   ) . Choose the statement that best describes the values of   and   which indicate the fact that the relationship is very strong. ​ A) The value of   is very large and the value of   is close to 1. B) The value of   is very large and the value of   is close to 0. C) The value of   is small and the value of   is close to 0. D) The value of   is small and the value of   is close to 1. and We know that the linear regression must be used if we have a strong relationship between   and   (or in other words when   is proportional to   ) . Choose the statement that best describes the values of   and   which indicate the fact that the relationship is very strong. ​ A) The value of   is very large and the value of   is close to 1. B) The value of   is very large and the value of   is close to 0. C) The value of   is small and the value of   is close to 0. D) The value of   is small and the value of   is close to 1. which indicate the fact that the relationship is very strong. ​


Definitions:

Interaction

The mutual or reciprocal action or influence between entities or variables in a study.

Two-factor Factorial Design

An experimental setup that involves two independent variables and examines their interaction as well as the effect of each variable separately on the dependent variable.

Total SS

The total sum of squares, which quantifies the total variation in a dataset by measuring the sum of the squared differences from the mean.

Two-way Analysis of Variance

A statistical technique that evaluates the impact of two categorical independent variables on a continuous dependent variable.

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