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Linear Regression and Correlation - Solved Example Video Lecture | Quantitative Aptitude for CA Foundation
FAQs on Linear Regression and Correlation - Solved Example Video Lecture - Quantitative Aptitude for CA Foundation
| 1. What is linear regression? |  |
Ans. Linear regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It aims to find the best-fitting linear equation that represents the relationship between the variables.
| 2. How is linear regression used in data analysis? | |
Ans. Linear regression is commonly used in data analysis to understand the relationship between variables and make predictions. It helps identify trends, measure the strength of relationships, and estimate future values based on historical data.
| 3. What is correlation? | |
Ans. Correlation measures the strength and direction of the linear relationship between two variables. It ranges from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no linear correlation.
| 4. How is correlation different from linear regression? | |
Ans. Correlation measures the strength and direction of the relationship between two variables, while linear regression aims to find the best-fitting linear equation to represent the relationship. Correlation does not imply causation, whereas linear regression can be used to predict the dependent variable based on the independent variable.
| 5. What is the significance of correlation coefficient in linear regression? | |
Ans. The correlation coefficient in linear regression represents the strength and direction of the linear relationship between the independent and dependent variables. It helps determine how well the regression line fits the data points and provides insights into the predictive power of the model. A higher correlation coefficient indicates a stronger relationship, while a value close to 0 indicates a weak or no relationship.
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Sep 05, 2025
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