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ancova

VIEWS: 9 PAGES: 13

									          Gender Bias in
         Maharashtra Bank
              PRESENTATION ON
                   ANCOVA
GUIDED BY:-               PRESENTED BY:-

DR. SUMEET GUPTA           APARNA ROY
                           EKTA TIWARI
                           DISHA DAMKE
                           AMIT DUBEY
                           HARISH VERMA
                           DEEPAK YADU
        INTRODUCTION
 Gender bias is ultimately shown in terms
  of salary discrimination
     salary discrimination is based on
      four things
     Age
     Experience
     Position
     qualification
      OBJECTIVE OF
       RESEARCH
• To find out the gender discrimination
  in Maharashtra bank.
       HYPOTHESIS
• Null hypothesis, (H0):-There is no
  gender discrimination.
• Research hypothesis, (H1):- There is
  gender discrimination.
       RESEARCH
     METHODOLOGY
1. Data collection method:- Through
   observation (Historical data
   recording method).
2. Sample size:-Sample size 35
   (employees).
  DATA PREPARATION
• CODING:-Coding is a process
  whereby the data is assigned a
  numerical code and values so that it
  can be more easily fitted into
  appropriate categories. Coding is
  necessary where machine tabulation
  is used.
      OBSERVATION
• Here we are taking factors like:- age,
  gender, qualification, designation,
  experience and seeing whether any
  discrimination is found on the basis
  of salary.
       Data Analysis-multiple
            regression
            Unstandar             Standardi    t       Sig.
              dized                  zed
            Coefficien            Coefficien
                ts                    ts
                B      Std. Error   Beta
(Constant) 12001.87 5149.47                     2.33     0.03   (Constant)
Qualification 1612.41     705.88        0.28    2.28     0.03   Qualification
Experience       31.80       6.99       0.47    4.55     0.00   Experience
Designation -1042.18      296.61       -0.42   -3.51     0.00   Designation
Age              19.13    113.71        0.02    0.17     0.87   Age
Data analysis by multiple
  regression method
 NAME                   Gender   Gender       Qualification Designation
                                          Salary      Experience      Age
 SURAJ KUMAR MOHANTI      M        0      32397     4     324    1     51
 GANGADHAR ROHANKAR       M        0      13330     4      19    2     52
 KU.RASHMI REKHASATI      F        1      14583     4      38    3     26
 MEGHSHYAM DAMKE          M        0      24779     3     372    6     56
 SHAILENDRA GOVERDHAN     M        0      24125     4     372    7     54
 PREETAM BHATACHARYA      M        0      23587     3     324    6     49
 KAPOOR WASNIK            M        0      23914     3     348    5     55
 LALCHAND SONI            M        0      23838     4     324    6     51
 B.L.RAO                  M        0      23070     3     288    6     49
           adjusted salary
PRATALLA KU. CHAUHAN    M   0   22724   3   288    6   48   20663
MANRHARLAL BAGHEL       M   0   23724   3   348    9   54   19559
UTTAM KU. MOHITE        M   0   13264   1   372   12   52   13932
SHAN MOHAMMED QURESHI   M   0   13602   0   312    8   49   14523
RAJENDRA VERMA          M   0    9772   1   396   11   45   15604
RAM BABU                M   0    7355   0   108   13   39    2634
ANAND MOHAN SHARMA      M   0   29400   4   324    2   48   27589
SMT.RUMA PAUL           F   1   27100   4   300    2   43   26730
O.P.AGRAWAL             M   0   23300   4   312    9   47   19893
RAJU BHOGAL             M   0    8500   2    16   11   42    5075
MANOJ YADAV             M   0    2300   0    40   11   30    2384
SMT.B.DUBEY      F   1   31400   4   264   2    47   25661
N.K.TONDAN       M   0   19300   3   264   6    46   19861
SMT.K.KATHAR     F   1    7800   3    57   7    45   12217
G.K.RIGRI        M   0    8300   2   180   9    41   12355
R.K.VERMA        M   0    4800   2    52   11   31    6009
GOLU YADAV       M   0    5900   0   156   11   29    6053
S.HASAN          M   0   22900   4   324   3    48   26546
A.NAIDU          F   1   18800   3   312   6    47   21407
BARNALI BISWAS   F   1   19300   3   324   6    49   21827
P.DURDASHI       F   1   18700   4   324   6    48   23420
B.DUBEY          M   0   24300   4   348   4    52   26344
S.EKKA           F   1   16500   3   228   6    38   18563
L.H.GOMUKH       M   0   12300   0   396   10   53   15187
G.PATEL          M   0    7900   0    51   11   28    2695
                              ANCOVA
Salary
                 Sum of        df      Mean Square     F
                 Squares
                                                           significa
Between         82788195.12          1 82788195.12 1.16538 nce is
Groups                                                     0.288
Within Groups   2344309642          33 71039686.12
Total           2427097837          34
       CONCLUSION
• There is no gender discrimination in
  Bank Of Maharashtra.

								
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