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ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-5Bog'liq 479-495 Qarshiboyeva D JURNAL
ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-5
487
1.
Role and FDIs (Foreign Direct Investments): There is a moderate positive
correlation of 0.4660 between Role and FDIs, indicating a tendency for higher roles in
organizations to coincide with increased levels of Foreign Direct Investments.
2.
Role and transparency: There is a strong relationship of 0.926 between
them since p value is 0.000, which means that increases in role make rises in
transparency or reverse ways.
3.
Transparency and rights: There is a strong negative correlation of -0.2082
between Rights and Transparency, suggesting that as the rights framework improves,
transparency tends to decrease in this context.
By this table of correlation, it can be determined the relationship between any
two variables. And by means of this correlation, we can know that if we want to change
in any of these variables, what can happen to other variables whether there will be
positive or negative changes or no any changes. It will help us to find solution or make
a decision in some challenges.
Regression
ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-5
488
Regression is a useful way to observe how variables fit together for any desired
degree of complexity. The main purpose of regression analysis is to describe the
relationship between variables, but it can also be used to: Estimate the value of a
variable using the known values of other variables. Predict outcomes and shifts of a
variable based on its relationship with other variables. Therefore, regression of our
database is determined by Stata 15. In order to find relationship between our variables
with more detailed data. Since our survey questions especially consist of categorical
variables, each of categorical variables are opened . This interpretation can be extended
across all categorical independent variables included in a regression model. Each
coefficient for a specific level of a categorical variable will provide information on how
that level affects the outcome relative to the omitted category or baseline level.
_cons 21.13629 8.095769 2.61 0.011 5.012174 37.26042
others -2.947494 2.200725 -1.34 0.184 -7.330619 1.43563
Private company -3.966108 1.993011 -1.99 0.050 -7.935533 .0033169
LLC(limited liability company) -4.975591 2.403012 -2.07 0.042 -9.761605 -.189578
Type
service -.6122325 1.572795 -0.39 0.698 -3.744725 2.52026
Spheres
others 3.884519 3.960078 0.98 0.330 -4.002661 11.7717
6-10 -.6330892 3.080273 -0.21 0.838 -6.767986 5.501807
11-15 1.761938 2.915674 0.60 0.547 -4.045131 7.569006
Experience
other -1.127323 4.160903 -0.27 0.787 -9.41448 7.159835
master degree 4.453899 3.269637 1.36 0.177 -2.058148 10.96595
bachelor 3.581454 2.774864 1.29 0.201 -1.945167 9.108075
PhD 1.074753 3.514755 0.31 0.761 -5.925488 8.074994
Education_level
others -3.049368 3.627591 -0.84 0.403 -10.27434 4.175606
30-35 -2.058738 3.165274 -0.65 0.517 -8.362928 4.245453
24-29 -.2543103 2.899548 -0.09 0.930 -6.02926 5.52064
Age
male -1.635466 1.570426 -1.04 0.301 -4.763239 1.492308
Gender
91-100 13.82378 9.304564 1.49 0.141 -4.707863 32.35543
81-90 11.29974 9.201185 1.23 0.223 -7.026004 29.62549
61-80 8.098301 9.185768 0.88 0.381 -10.19674 26.39334
41-60 11.8363 9.326513 1.27 0.208 -6.739065 30.41166
21-40 9.568776 9.362092 1.02 0.310 -9.077447 28.215
FDIs
medium .9783239 1.931242 0.51 0.614 -2.868078 4.824726
low -.1442816 2.65726 -0.05 0.957 -5.436675 5.148111
Rights
5 67.9559 5.978264 11.37 0.000 56.04916 79.86264
4 55.2936 5.894299 9.38 0.000 43.55409 67.03311
3 40.73999 5.850482 6.96 0.000 29.08775 52.39224
2 4.475581 5.875867 0.76 0.449 -7.227222 16.17838
transparency
5 .6190439 3.315635 0.19 0.852 -5.984616 7.222704
4 -3.119538 2.108917 -1.48 0.143 -7.31981 1.080733
3 -3.389496 2.246504 -1.51 0.135 -7.863797 1.084805
2 -1.830233 2.258296 -0.81 0.420 -6.328019 2.667553
opportunityforbeingshareholde
role Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 54394.972 106 513.160113 Root MSE = 6.4086
Adj R-squared = 0.9200
Residual 3121.33865 76 41.0702454 R-squared = 0.9426
Model 51273.6333 30 1709.12111 Prob > F = 0.0000
F(30, 76) = 41.61
Source SS df MS Number of obs = 107
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