SolutionA 
Case: part-I: Two years ago the top slice company moved from just making golf balls to also producing oversized drivers. Top slice makes three different models the bomber, the hook king and the sir slice a lot. As the names suggest, the last two club help correct for golfers who either hook or slice the ball when driving. While Top-slice is pleased with the growing sales for all three models, the numbers present Jacob Lee, the production manager, with a dilemma. Jacob knows that the current manufacturing work cell is capable of producing only 2700 drivers per month, and total sales seem to be rapidly approaching that number. Jacobs’s staff has sold him it will take at least three months to plan for and implement an expanded work cell.
1. Develop a quantitative forecast model for Jacob Lee. Which modeling technique did you choose, and why? What are the assumptions behind your model?
Answer:
Here in this case linear regression will be used as a forecasting model. The reason why the regression will be used is because by following below given graphs it is very clear that the data follows linear trend with no seasonal variation.
Assumptions behind the regression model:
1. Linear relationship between time and variable to be predicted
2. Randomly distributed error variables
3. Available data represents observed values and both the time and variable to be predicted are measured with no error
4. Independent variable variance is larger than zero
5. Time is the only factor influencing the demand
Forecasting model for Bombers
Model
Unstandardized Coefficients
Standardized Coefficients
B
Std. Error
Beta
(Constant)
1397.819
2.333
TIME_PERIOD
13.028
.163
.998
Model Equation for Bombers: Y= 1397.819 + 13.028 (Time Period)
Regression model for Hook King:
Model
Unstandardized Coefficients
Standardized Coefficients
B
Std. Error
Beta
(Constant)
369.779
.905
TIME_PERIOD
4.954
.063
.998
Model Equation for Hook Kings: Y= 369.779+4.954(Time Period)
Regression Model for Sir A
1. Develop a quantitative forecast model for Jacob Lee. Which modeling technique did you choose, and why? What are the assumptions behind your model?
Answer:
Here in this case linear regression will be used as a forecasting model. The reason why the regression will be used is because by following below given graphs it is very clear that the data follows linear trend with no seasonal variation.
Assumptions behind the regression model:
1. Linear relationship between time and variable to be predicted
2. Randomly distributed error variables
3. Available data represents observed values and both the time and variable to be predicted are measured with no error
4. Independent variable variance is larger than zero
5. Time is the only factor influencing the demand
Forecasting model for Bombers
Model
Unstandardized Coefficients
Standardized Coefficients
B
Std. Error
Beta
(Constant)
1397.819
2.333
TIME_PERIOD
13.028
.163
.998
Model Equation for Bombers: Y= 1397.819 + 13.028 (Time Period)
Regression model for Hook King:
Model
Unstandardized Coefficients
Standardized Coefficients
B
Std. Error
Beta
(Constant)
369.779
.905
TIME_PERIOD
4.954
.063
.998
Model Equation for Hook Kings: Y= 369.779+4.954(Time Period)
Regression Model for Sir A