statistics for managers, multiple regression analysis
TRANSCRIPT
Statistics For Managers
Live-Project
On
Multiple Regression Analysis
Project Introduction
• Under this project, we collected sales data from an insurance brooking company “NetAmbit Insurance Brooking Pvt Ltd.”, for consecutive 30 months starting from July-2010 to December-2012.
• By using Graphical data representation technique, we made histogram and scattering diagrams for the available data.
• We identified independent variables, & thereafter used “Minitab” Statistical software tool to obtain Multiple regression equation.
Data Overview
• Data Obtained, included:
• Number if Insurance Policies sold monthly
• Premium received (month-wise)
• Brokerage received (month-wise)
• Interval : July-2010 to December-2012
• In specified Interval, the company generated an approximate Revenue of 400 Crores.
Collected Data (Brief) Month (MMM-YY) Insurance Policy (Unit
Sales)
July – 2010 235
August – 2010 2822
September – 2010 2618
October – 2010 4246
November – 2010 5532
December – 2010 6441
January – 2011 4978
February – 2011 6103
Mar – 2011 12787
April – 2011 11226
May – 2011 11426
June – 2011 13880
July – 2011 13640
August – 2011 13647
September – 2011 14413
October – 2011 13694
November – 2011 16191
December – 2011 20256
January – 2012 17962
February – 2012 18280
March – 2012 21810
April – 2012 12999
May – 2012 18855
June – 2012 19924
July – 2012 18683
August – 2012 19541
September – 2012 19941
October – 2012 19199
November – 2012 19844
December - 2012 22756
Unit Policy Sales
0
5000
10000
15000
20000
25000
2010 2011 2012
jan
feb
march
april
may
june
july
aug
sept
oct
nov
dec
Started & Hired; Invested in Technology; JFM exponential increase; Approx flat in mid-yr; high in yr-end; drop after JFM
Problem
Sales Figure shows a dip
during the month of April.
Why?
Regression Line
0
5
10
15
20
25
30
35
0 5000 10000 15000 20000 25000
Highly ???????
Depth Analysis:
Jan-12 to Dec-12
0
2
4
6
8
10
12
14
0 5000 10000 15000 20000 25000
April
Highly ???????
Depth Analysis:
Jan-11 to Dec-11
0
2
4
6
8
10
12
14
0 5000 10000 15000 20000 25000
April
Highly ???????
Depth Analysis:
Jul-10 to Dec-10
0
1
2
3
4
5
6
7
0 1000 2000 3000 4000 5000 6000 7000
April Not Applicable; Company started
operations from July
Highly ???????
Cause of Problem Independent
Variable Name Cause
New Financial Year
As April marks the beginning of a new Financial year,
people do not generally feel a need to purchase life
insurance policy and therefore the sales show a downward
trend during the month of April
JFM ended
*(JFM = Jan + Feb + Mar)
Most of the people tend to purchase the policies by the
end the financial year, in the month of JAN-MAR for
Tax planning, which becomes a prime reason for low sales
in the month of April
Premium paid during JFM months
Premium for policies like LIC, etc. have to be paid By the
end of a financial year which impacts the financial
position of the customers affecting their ability to buy new
policies during this period. Now the propensity to save is
more evident.
Impact of other expenses
People are burdened with other expenses in the beginning
of a new financial year which also affects the sale of life
insurance policies. Some of the expenses may be the
academic fees of children, interest on loans,
Independent Variable Name
Cause of Problem
Cause of Problem
Limitation Faced
• We rationally came up with independent variable, as discussed before.
• But Multiple Regression Analysis using MINITAB, required discrete numeric values available in Companies data.
• So, we had to take following numbers to determine multiple regression equation:
• Number of Policies sold
• Premium Received
• Brokerage Received
Multiple Regression Equation
Equation Obtained using MINITAB:
Premium = 405048 + (3735 x Policy count) + (3.45 x Brokerage)
Predictor Coef SE Coef T P
Constant 405048 217536 1.86 0.063
Policy count 3735.3 137.8 27.11 0.000
Brokrage In 3.4499 0.1123 30.73 0.000
S = 3498572 R-Sq = 94.2% R-Sq(adj) = 94.2%
Conclusion and Solution
• Being Insurance brooking company, sales see a dip during April, because customers have spent enough money during JFM months and they tend to save money after that.
• Hence in this case, as a brooking company, I know that principal cost of booking a policy cannot be decreased by policy issuing firm, though my brokerage charges can be decreased by my side, so the grand total amount paid my customer is decreased, and as a result policy count will increase.
• This can be said as P-value for brokerage is 0.00, making this independent variable as a significant predictor of premium.
Thank You !!!
A live-project presentation by
Abhitanjay Chaudhary (A0102214093);
Owais Ashraf (A0102214161);
Rajat Sharma (A0102214079);
Anant Prakash Gupta (A0102214086)