Answers

Here are the answers to the preceding questions:

1. The first input value is the coefficient of determination. Its formula is the explained variation divided by the total variation. The second input value is the sign of the slope. If it is positive, the relationship is direct. If not, the relationship is inverse.
2. t-statistics tell us whether the null hypothesis that the slope is equal to zero can be rejected. The slope with a non-zero value means a relationship between the variables. This is the alternative hypothesis.
3. The model just gives trends, not exact results. The scenarios give us an idea of the range of values that the model predicts. It helps to analyze whether the results make sense or not, based on our experience.
4. The unexplained variation or errors of the linear model is the distance of the model from the expected values. These distances must be short to have an effective predictor model. If it is not, the worst-case scenario is a high standard...