Demand Analysis Of Pizza
Demand Estimation
After studying this chapter, you should be able to:
1. Discuss how the firm’s managers use the information about demand for its product to determine correctly its profit-maximizing rate of output and price, or whether to produce a particular product at all.
2. Discuss demand respond to consumer income increase or decrease as a result of an economic expansion or contraction.
3. Specify the components of a regression model that can be used to estimate a demand equation.
4. Interpret the regression results (i.e., explain the quantitative impact that changes in the determinants have on the quantity demanded).
5. Explain the meaning of R2.
6. Evaluate the statistical significance of the regression coefficients using the t-test and the statistical significance of R2 using the F-test.
Introduction:
An important contributor to firm risk arises from sudden shifts in demand for the product or service.
Demand estimation serves two managerial objectives:
(1) it provides the insights necessary for effective management of demand, and
(2) it aids in forecasting sales and revenues.
The theory
SIMPLE LINEAR REGRESSION
Relationships, among other things, may serve as a basis for estimation and prediction.
Simple prediction—when we take the observed values of X to estimate or predict corresponding Y values.
Regression analysis uses simple and multiple predictors to predict Y from X values.
With respect to similarities and differences of correlation and regression, their relatedness would suggest that beneath many correlation problems is a regression analysis that could provide further insight about the relationship of Y with X.
The Basic Model
A straight line is fundamentally the best way to model the relationship between two continuous variables.
Regression coefficients are the intercept and slope coefficients.
Slope (β1)—the change in Y for a 1-unit change in X.
This is the ratio of change (∆) in the rise of the line relative to the run or travel along
After studying this chapter, you should be able to:
1. Discuss how the firm’s managers use the information about demand for its product to determine correctly its profit-maximizing rate of output and price, or whether to produce a particular product at all.
2. Discuss demand respond to consumer income increase or decrease as a result of an economic expansion or contraction.
3. Specify the components of a regression model that can be used to estimate a demand equation.
4. Interpret the regression results (i.e., explain the quantitative impact that changes in the determinants have on the quantity demanded).
5. Explain the meaning of R2.
6. Evaluate the statistical significance of the regression coefficients using the t-test and the statistical significance of R2 using the F-test.
Introduction:
An important contributor to firm risk arises from sudden shifts in demand for the product or service.
Demand estimation serves two managerial objectives:
(1) it provides the insights necessary for effective management of demand, and
(2) it aids in forecasting sales and revenues.
The theory
SIMPLE LINEAR REGRESSION
Relationships, among other things, may serve as a basis for estimation and prediction.
Simple prediction—when we take the observed values of X to estimate or predict corresponding Y values.
Regression analysis uses simple and multiple predictors to predict Y from X values.
With respect to similarities and differences of correlation and regression, their relatedness would suggest that beneath many correlation problems is a regression analysis that could provide further insight about the relationship of Y with X.
The Basic Model
A straight line is fundamentally the best way to model the relationship between two continuous variables.
Regression coefficients are the intercept and slope coefficients.
Slope (β1)—the change in Y for a 1-unit change in X.
This is the ratio of change (∆) in the rise of the line relative to the run or travel along