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Advance Data - Pricing of Players in the Indian Premier League

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Advance Data Analysis

ASSIGNMENT: PRICING OF PLAYERS IN THE INDIAN PREMIER LEAGUE

[pic 1]

   Submitted to -                                                                   Submitted by-

   Dr. Shailja Rego                                                                Vikas Garg

                                                                                               D023, Group 1

Objective

To recognise selling price of a cricket player in IPL depends upon which variables. Find out the relationship between selling price and these identified variables and review the effectiveness of the above relationship through multiple coefficient of determination.

Procedure

Multiple Regression Analysis (MRA) was used to attain the relationship and measure its effectiveness. The following steps were taken for this

Step1:

To modify the data and identify the dependent variable and the independent variables in the data.

Dependent variable: SQRT(S-B) where S = Sold price and Base Price = Base Price

Independent variables: Auction year, MTS, B25-35, BOW*SR-BL, AUSTRALIA, BAT, CAPTAINCY EXP, TEAM, BOW*T-WKTS, L25, BOW*ODI-SR-BL, INDIA, BAT*SIXERS T20, BAT*T-RUNS, BOW*WICKETS, BOW*ODI-WKTS, BAT*ODI-SR-B, BOW*ECO, BAT*HS T20, BAT*ODI-RUNS-S, BOW, BAT*AVE T20, BAT*SR -B T20, BOW*RUNS-C, BAT*RUN T20, BOW*AVE-BL

 

Step 2:

As Regression can be done when both dependent and independent variables are quantitative. So, all the variables which were qualitative are converted to quantitative variables by using dummy. Auction_year was also converted to quantitative by assuming year 2008 as 1, 2009 as 2 and 2011 as 3.

Step 3:

Perform regression on the data

The output of Regression is as follows-

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

Durbin-Watson

R Square Change

F Change

df1

df2

Sig. F Change

1

.723a

.523

.402

266.40448

.523

4.336

26

103

.000

1.873

Interpretation:

R square is 0.402 which is low for the model, cannot use this model

Durbin Watson statistics is 1.873 which is in the range of 1.5 to 2.5. Therefore we can say that there is no autocorrelation.

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

8000823.291

26

307723.973

4.336

.000b

Residual

7310048.478

103

70971.344

 

 

Total

15310871.769

129

 

 

 

a. Dependent Variable: SQRT(S-B)

b. Predictors: (Constant), Auction_year, MTS, B25-35, BOW*SR-BL, AUSTRALIA, BAT, CAPTAINCY EXP, TEAM, BOW*T-WKTS, L25, BOW*ODI-SR-BL, INDIA, BAT*SIXERS T20, BAT*T-RUNS, BOW*WICKETS, BOW*ODI-WKTS, BAT*ODI-SR-B, BOW*ECO, BAT*HS T20, BAT*ODI-RUNS-S, BOW, BAT*AVE T20, BAT*SR -B T20, BOW*RUNS-C, BAT*RUN T20, BOW*AVE-BL

Interpretation:

We can use above ANOVA table for checking whether the model is significant or not. As we can see the of sig. is .000 which is less than 0.05. Hence, the overall model is significant.

.000 < 0.05

Step 4:

One-Way ANOVA was used to check the significance of every dummy variable with the dependant variable. Post hoc analysis is also been done to check significance difference between samples.

AGE

ANOVA

SQRT(S-B)  

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

970108.370

2

485054.185

4.296

.016

Within Groups

14340763.399

127

112919.397

Total

15310871.769

129

Multiple Comparisons

Dependent Variable:   SQRT(S-B)  

LSD  

(I) AGE

(J) AGE

Mean Difference (I-J)

Std. Error

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

1.0

2.0

249.496410881896850*

91.490319404594350

.007

68.453579607923020

430.539242155870700

3.0

287.391895046364370*

105.310483352013210

.007

79.001453380448200

495.782336712280540

2.0

1.0

-249.496410881896850*

91.490319404594350

.007

-430.539242155870700

-68.453579607923020

3.0

37.895484164467520

73.115332480327820

.605

-106.786564033866430

182.577532362801460

3.0

1.0

-287.391895046364370*

105.310483352013210

.007

-495.782336712280540

-79.001453380448200

2.0

-37.895484164467520

73.115332480327820

.605

-182.577532362801460

106.786564033866430

*. The mean difference is significant at the 0.05 level.

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