1. Briefly describe when qualitative versus quantitative forecasting methods would be used?
2. Linear Regression.
a. Describe what linear regression is and how it can be used.
b. Explain each variable in the equation: y = a + bx
c. Coefficient of Correlation, r.
- What does r measure?
- What values can r have and what do they mean?
d. Coefficient of Determination, r2.
- What does r measure?
- What values can r2 have and what do they mean?
3. Problem 4.35 on page 143 paraphrased: A real estate developer created a linear regression model to determine home prices. The price of the home (Y) is determined by the size (square footage = X). The model is: Y = 13,473 + 37.65X.
The coefficient of correlation is 0.63.
a. Use the model to predict the selling price of the house that is 1,860 square feet.
b. An 1,860 square foot house recently sold for $95,000. Explain why this is not what the model predicted.
c. If you were going to use multiple-regression to develop such a model, what other quantitative variables might you include?
d. What is the value of the coefficient of determination in this problem? What does this mean?