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Kompaniyalarda aksiyadorlarning rollari va huquqlarini yaxshilashBog'liq 479-495 Qarshiboyeva D JURNALPearson-pairwise correlation
Table [2]
Pearson-pairwise correlation is a statistical technique used to measure the
strength and direction of the linear relationship between two continuous variables. It is
a commonly used method for assessing the degree to which two variables change
together.
- The correlation coefficient ranges from -1 to 1. A value of 1 indicates a perfect
positive correlation, -1 indicates a perfect negative correlation, and 0 indicates
no correlation.
- The asterisks (*) next to correlation coefficients typically express statistical
significance:
Experience
107
1.972
1.004
1
4
Spheres
107
1.542
.501
1
2
Type
107
2.187
1.167
1
4
Offers
107
18.393
12.161
1
43
ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-5
485
- *: p < 0.05 - **: p < 0.01 - ***: p < 0.001
In this provided correlation matrix (Table2), it can be identified the Pearson
correlation coefficient (r). Here is an interpretation of some of the correlations based
on the coefficients:
Interpreting the entire correlation matrix involves looking at the relationships
between each pair of variables. Here are some key interpretations based on the provided
correlation coefficients:
1. “Role”:
- Strong positive correlation with FDIs (0.4660).
- Weak negative correlations between opportunities and role with (-0.1148) ,as
well as, with rights (-0.1850).
2. “Opportunities”:
- There is a weak negative correlation with role (-0.1148).
- And there is a strong positive correlation with transparency (0.9257).
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