Final answer:
Perform multiple linear regression analysis with Period, DIFF, and ADV as independent variables to predict demand. Determine the most significant predictor of demand and rank the independent variables based on their contribution to the model. Observe and interpret the significant F, R, R-squared, and p-values.
Step-by-step explanation:
- The independent variables are Period, DIFF, and ADV. The dependent variable is demand.
- To draw a scatter plot, plot the values of the dependent variable (demand) on the y-axis and the values of the independent variables (Period, DIFF, and ADV) on the x-axis.
- Use regression analysis to find the line of best fit and the correlation coefficient. This will give you the equation for the multiple linear regression model.
- The significance of the correlation coefficient indicates the strength and direction of the linear relationship between the independent variables and the dependent variable. A high positive or negative correlation coefficient indicates a strong relationship.
- To determine the most significant predictor of demand, examine the coefficients of the independent variables in the regression equation. The variable with the largest coefficient will be the most significant predictor.
- To rank the independent variables based on their degree of contribution to the model, look at the magnitude and direction of the coefficients. Variables with larger positive or negative coefficients have a greater contribution to the model.
- The significant F, R, R-squared, and p-values provide information about the overall fit of the regression model. A significant F-value indicates that at least one of the independent variables is contributing significantly to the model. The R-value represents the correlation between the observed and predicted values, and a high R-squared value indicates a good fit. The p-values for the coefficients indicate the significance of each independent variable in the model.