Correlation & regression analysis

1. Correlation & Regression Analysis
Anubhab Tiwari &Arjama Mukherjee
Core-marketing

2. SCPS model
Sector Product Company Area of
operation
Population
Foods &
beverage
Pizza Pizza hut Pune, Koregaon
park
2 lakh
[Source : Plazacentre.in]

3. Why research methodology ?
Helps in providing information to assist managers to make better
decisions
Helps in demand forecasting
Helps in new product development

4. Forecast sales of pizza hut
Pizza Hut in Pune, Koregaon park desires to build a practically implementable Sales
forecasting model that would enable the firm to forecast Sales territory-wise ,
month-wise based on the following Causative variables :
 Number of Delivery boys
 Advertising Spend
 Number of Outlets
 Pizza varieties served
 Competitor Activity (1= V Low; 5= V High)
 Customer base for Home deliveries

5. Correlation analysis
• In statistics, dependence is any statistical relationship
between two random variables or two sets of data.
• Correlation refers to any of a broad class of statistical
relationships involving dependence

6. Steps in Correlation analysis
• Fits the straight line data
• Weather the slope is negative or positive
• Quality of fit
Scatter along the y axis
Scatter along the fitted line

7. Y axis
X axis

9. Regression analysis
• Statistical process for estimating the relationship among variables
• The focus is on the relationship between a dependent variable and one
or more independent variables
• It shows that how a changes in independent variable varies the typical
value of the dependent variables which is held fixed
• It estimates the conditional expectation of the dependent variable
given in the independent variable

10. Uses of regression analysis
• Prediction and forecasting
• Helps in understanding which among the independent variables are
related to the dependent variable
• Casual relationship between the independent and dependent variables

11. Methods of regression analysis
• Linear regression: Refers to any two models where the increasing or
decreasing any model n times will cause a corresponding increase or
decrease of n times in the other variables too.
• Ordinary least square regression : Where doubling one variable will
not double the other variable.

12. Thank You