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A process by which we estimate the value of dependent variable on the basis of one or more independent variables is called:
- a)Correlation
- b)Regression
- c)Residual
- d)Slope
Correct answer is option 'B'. Can you explain this answer?
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A process by which we estimate the value of dependent variable on the ...
Regression analysis is a statistical process by which we estimate the value of a dependent variable on the basis of one or more independent variables. It is a widely used statistical tool for analyzing and modeling relationships between variables.
Regression analysis involves the following steps:
1. Data Collection: Collect relevant data on the dependent and independent variables to be analyzed.
2. Data Preparation: Clean and prepare the data for analysis by checking for missing values, outliers, and other inconsistencies.
3. Model Specification: Choose the appropriate type of regression model based on the nature of the data and the research question being addressed.
4. Model Estimation: Estimate the parameters of the regression model using statistical techniques such as ordinary least squares (OLS).
5. Model Evaluation: Evaluate the goodness of fit of the model and check for violations of its assumptions.
6. Prediction: Use the estimated model to make predictions about the value of the dependent variable based on values of the independent variables.
Regression analysis is commonly used in fields such as economics, finance, marketing, and social sciences to explain and predict relationships between variables. It can also be used for forecasting, trend analysis, and hypothesis testing.
Regression analysis involves the following steps:
1. Data Collection: Collect relevant data on the dependent and independent variables to be analyzed.
2. Data Preparation: Clean and prepare the data for analysis by checking for missing values, outliers, and other inconsistencies.
3. Model Specification: Choose the appropriate type of regression model based on the nature of the data and the research question being addressed.
4. Model Estimation: Estimate the parameters of the regression model using statistical techniques such as ordinary least squares (OLS).
5. Model Evaluation: Evaluate the goodness of fit of the model and check for violations of its assumptions.
6. Prediction: Use the estimated model to make predictions about the value of the dependent variable based on values of the independent variables.
Regression analysis is commonly used in fields such as economics, finance, marketing, and social sciences to explain and predict relationships between variables. It can also be used for forecasting, trend analysis, and hypothesis testing.
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