advanced multiple regression analysis
TRANSCRIPT
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Fytokem Products Inc.
Advanced Multiple Regression Analysis
Presentation By:Kamalika SomeKruthik KulkarniRitesh PrasadPankaj Kumar
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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%
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1) Predicting the Size of Purchase
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1) Predicting the Size of Purchase : Scatter Plots
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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.
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Predicting Size of Purchase with Company
Size1) 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
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Residual Plot: The most relevant variable
alone Company Size
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2) Analysing the response variable - Sales
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2) Analysing the response variable – Sales: Scatter Plots
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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.
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Effect of the variable - Hours worked per Week
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Effect of the variable – Number of Customers
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3) Measuring the impact of the number of Employees
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Sales vs Number of Employees
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Tukey’s 4 Quadrant Approach
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Sales^2.5 vs (log(Number of Employees)+Number of Employees)
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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
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Residual vs Fitted
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Thank you