One of the assumptions made in simple regression is that ______________. a. the error terms are exponentially distributed b. the error terms have unequal variances c. the model is linear d. the error terms are dependent e. the model is nonlinear
| a. |
the error terms are exponentially distributed
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| b. |
the error terms have unequal variances
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| c. |
the model is linear
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| d. |
the error terms are dependent
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| e. |
the model is nonlinear
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Single dependent variables but one or maybe more independent variables, often known as predictors or regressors, are frequently modelled using regression analysis. Simple linear regression is what we term it when there is just one regressor. Any regression model called simple linear regression uses a straight line to calculate the association between one independent variable and one dependent variable. Both variables ought to have numerical values. Because just one predictor variable is studied in simple linear regression, the term "simple" is used to describe it. In contrast, the term "multiple" refers to the study of two or more predictor variables in multiple linear regression, which is what we will cover later in this course.
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