Final answer:
In a multiple linear regression analysis, the independent variables are the potential predictor variables, while the dependent variable is the time it takes to make a hamburger. The analysis involves drawing a scatter plot, using regression to find the line of best fit and the correlation coefficient, and interpreting the significance of the correlation coefficient. This helps determine if there is a linear relationship between the variables.
Step-by-step explanation:
a. The independent variables are the five potential predictor variables that the fast food company identified. The dependent variable is the time it takes to make a hamburger.
b. To draw a scatter plot, you would plot the values of the independent variables on the x-axis and the values of the dependent variable on the y-axis. Each data point represents a combination of values for the predictor variables and the time it takes to make a hamburger.
c. Regression analysis can be used to find the line of best fit and calculate the correlation coefficient. This line represents the relationship between the independent variables and the dependent variable. The correlation coefficient measures the strength and direction of the linear relationship between the variables.
d. The significance of the correlation coefficient can be interpreted by performing statistical tests. These tests determine whether the observed correlation is statistically significant or if it could be due to chance. A significant correlation suggests that there is a meaningful relationship between the independent variables and the time it takes to make a hamburger.
e. To determine if there is a linear relationship between the variables, you can examine the scatter plot and the line of best fit. If the data points follow a clear and consistent pattern along the line, it indicates a linear relationship. If the points are scattered and do not align with the line, it suggests a weak or non-linear relationship.