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Answer:
In a linear regression model, a coefficient (slope) of 0.75 for a predictor variable indicates that for every one-unit increase in that variable, the dependent variable is predicted to increase by 0.75 units, holding all other variables constant.
For example, if the regression model is predicting weight (kg) from height (cm), a coefficient of 0.75 would mean that for every 1 cm increase in height, weight is expected to increase by 0.75 kg, assuming other factors like age and gender are held fixed.
The sign of the coefficient (positive or negative) indicates the direction of the relationship. A positive coefficient like 0.75 suggests a positive linear association between the predictor and outcome variables.
However, it's important to note that a coefficient near zero, like 0.01, would indicate that variable has little influence on the response, even if the relationship is statistically significant. The magnitude of the coefficient reflects the strength of the effect.
In summary, a regression coefficient of 0.75 implies a moderately strong positive linear relationship between the predictor and outcome variables in the model. But correlation does not necessarily imply causation, and the coefficient should be interpreted in the context of the specific variables and research question.