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Fytokem Products Inc.
Advanced Multiple Regression Analysis
Presentation By:
Kamalika Some
Kruthik Kulkarni
Ritesh Prasad
Pankaj Kumar
Case Study
• Canada based company producing pharmaceutical ingredients.
• Facing poor sales with domestic customers due to lack of demand.
• Introduction of Tyrostat in the international market – Success.
• Increase in sales by an average of 22%
1) Predicting the Size of Purchase
1) Predicting the Size of Purchase : Scatter
Plots
1) Predicting the Size of Purchase
1) Adjusted R-squared is 70%.
2) Company Size is a significant variable.
3) P-value of Cost of delivery and Similar
products >0.05, which indicates non-
significance of these variables in the
model.
Predicting Size of Purchase with
Company Size
1) Adjusted R-square is 66%.
2) P-value for company size is <0.05
which indicates significance.
3) Size of Purchase = 23.904 + 1.782
* Company Size
Residual Plot:
The most relevant variable alone
Company Size
2) Analysing the response variable - Sales
2) Analysing the response variable – Sales:
Scatter Plots
2) Analysing the response variable - Sales
1) Adjusted R-squared is very low.
2) P-value for explanatory variables are
>0.05.
3) Exploratory variables do not explain
the response variable.
Effect of the variable - Hours worked per Week
Effect of the variable – Number of Customers
3) Measuring the impact of the number of
Employees
Sales vs Number of Employees
Tukey’s 4 Quadrant Approach
Sales^2.5 vs (log(Number of
Employees)+Number of Employees)
3) Measuring the impact of the number of
Employees
1) Adjusted R-squared is 80%.
2) Transformed exploratory variable,
log(Number of employees)+Number of
employees explains 80% of the variability
of response variable.
3) 2.5*Sales=-352961.7 + 86210.2 *
log(Number of employees) -477 *
Number of employees
Residual vs Fitted
Thank you

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Advanced Multiple Regression Analysis

  • 1. Fytokem Products Inc. Advanced Multiple Regression Analysis Presentation By: Kamalika Some Kruthik Kulkarni Ritesh Prasad Pankaj Kumar
  • 2. Case Study • Canada based company producing pharmaceutical ingredients. • Facing poor sales with domestic customers due to lack of demand. • Introduction of Tyrostat in the international market – Success. • Increase in sales by an average of 22%
  • 3. 1) Predicting the Size of Purchase
  • 4. 1) Predicting the Size of Purchase : Scatter Plots
  • 5. 1) Predicting the Size of Purchase 1) Adjusted R-squared is 70%. 2) Company Size is a significant variable. 3) P-value of Cost of delivery and Similar products >0.05, which indicates non- significance of these variables in the model.
  • 6. Predicting Size of Purchase with Company Size 1) Adjusted R-square is 66%. 2) P-value for company size is <0.05 which indicates significance. 3) Size of Purchase = 23.904 + 1.782 * Company Size
  • 7. Residual Plot: The most relevant variable alone Company Size
  • 8. 2) Analysing the response variable - Sales
  • 9. 2) Analysing the response variable – Sales: Scatter Plots
  • 10. 2) Analysing the response variable - Sales 1) Adjusted R-squared is very low. 2) P-value for explanatory variables are >0.05. 3) Exploratory variables do not explain the response variable.
  • 11. Effect of the variable - Hours worked per Week
  • 12. Effect of the variable – Number of Customers
  • 13. 3) Measuring the impact of the number of Employees
  • 14. Sales vs Number of Employees
  • 16. Sales^2.5 vs (log(Number of Employees)+Number of Employees)
  • 17. 3) Measuring the impact of the number of Employees 1) Adjusted R-squared is 80%. 2) Transformed exploratory variable, log(Number of employees)+Number of employees explains 80% of the variability of response variable. 3) 2.5*Sales=-352961.7 + 86210.2 * log(Number of employees) -477 * Number of employees