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variabel terbaik yang berhubungan dengan emansipasi wanita p17
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variabel terbaik yang berhubungan dengan emansipasi wanita p17
Linear Regression:
Literature Review: Linear regression can be used to analyze the relationship between various socio-economic factors and women's participation in global value chains (GVCs). Studies employing linear regression might investigate how factors like education level, access to vocational training, and government policies correlate with women's employment rates in the manufacturing sector integrated into GVCs.
Theoretical Framework: Linear regression aligns with the Human Capital Theory, as it helps quantify the impact of investments in education and training on women's economic participation. It can also be utilized to analyze how changes in government policies, such as labor laws or vocational training programs, affect women's labor force participation rates.
Polynomial Regression:
Literature Review: Polynomial regression can offer a more nuanced understanding of the complex relationships between predictors and women's participation in GVCs. It may capture nonlinear trends or interactions between variables that linear regression might overlook.
Theoretical Framework: Polynomial regression can be employed to examine how the interactions between various socio-economic factors influence women's access to and retention in manufacturing sectors integrated into GVCs. This aligns with theories such as Structural Transformation Theory, which emphasizes the multifaceted nature of economic transitions.
Literature Review: Linear regression can be used to analyze the relationship between various socio-economic factors and women's participation in global value chains (GVCs). Studies employing linear regression might investigate how factors like education level, access to vocational training, and government policies correlate with women's employment rates in the manufacturing sector integrated into GVCs.
Theoretical Framework: Linear regression aligns with the Human Capital Theory, as it helps quantify the impact of investments in education and training on women's economic participation. It can also be utilized to analyze how changes in government policies, such as labor laws or vocational training programs, affect women's labor force participation rates.
Polynomial Regression:
Literature Review: Polynomial regression can offer a more nuanced understanding of the complex relationships between predictors and women's participation in GVCs. It may capture nonlinear trends or interactions between variables that linear regression might overlook.
Theoretical Framework: Polynomial regression can be employed to examine how the interactions between various socio-economic factors influence women's access to and retention in manufacturing sectors integrated into GVCs. This aligns with theories such as Structural Transformation Theory, which emphasizes the multifaceted nature of economic transitions.
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