Regression coefficients - Correlation & Regression, Business Mathematics & Statistics

# Regression coefficients - Correlation & Regression, Business Mathematics & Statistics Video Lecture | Business Mathematics and Statistics - B Com

115 videos|142 docs

## FAQs on Regression coefficients - Correlation & Regression, Business Mathematics & Statistics Video Lecture - Business Mathematics and Statistics - B Com

 1. What are regression coefficients and how are they used in correlation and regression analysis?
Ans. Regression coefficients are numerical values that represent the relationship between a predictor variable and a response variable in regression analysis. In correlation analysis, regression coefficients indicate the strength and direction of the linear relationship between two variables. These coefficients are used to estimate the value of the response variable based on the predictor variable(s) and can help in making predictions or understanding the impact of a predictor variable on the response variable.
 2. How do you interpret regression coefficients in correlation and regression analysis?
Ans. The interpretation of regression coefficients depends on the context and the variables involved. In correlation analysis, a positive coefficient indicates a positive linear relationship between the variables, meaning that as one variable increases, the other tends to increase as well. A negative coefficient indicates an inverse relationship, where as one variable increases, the other tends to decrease. In regression analysis, the coefficient represents the change in the response variable for a one-unit change in the predictor variable, holding other variables constant. For example, if the coefficient for a predictor variable is 0.5, it means that for every one-unit increase in the predictor variable, the response variable is expected to increase by 0.5 units.
 3. How are regression coefficients calculated in correlation and regression analysis?
Ans. Regression coefficients are calculated using statistical methods such as least squares estimation. In correlation analysis, the coefficient is calculated as the covariance between the variables divided by the product of their standard deviations. In regression analysis, the coefficients are estimated by minimizing the sum of the squared differences between the observed values of the response variable and the predicted values based on the predictor variable(s). This is done using mathematical formulas or software tools, such as Excel or statistical packages like R or SPSS.
 4. What is the difference between correlation coefficients and regression coefficients?
Ans. Correlation coefficients measure the strength and direction of the linear relationship between two variables, without considering the cause-and-effect relationship. They range from -1 to +1, where -1 indicates a perfect negative linear relationship, +1 indicates a perfect positive linear relationship, and 0 indicates no linear relationship. On the other hand, regression coefficients are specific to regression analysis and represent the impact of a predictor variable on the response variable, while controlling for other variables. They can be positive or negative and provide information about the direction and magnitude of the relationship between the variables.
 5. How do you interpret the p-values associated with regression coefficients in correlation and regression analysis?
Ans. In correlation analysis, p-values are used to determine the statistical significance of the correlation coefficient. A p-value less than a chosen significance level (e.g., 0.05) indicates that the observed correlation is unlikely to have occurred by chance, suggesting a significant relationship between the variables. In regression analysis, p-values associated with regression coefficients indicate whether the coefficient is statistically significant. A low p-value suggests that the coefficient is unlikely to be zero, meaning that there is evidence of a significant relationship between the predictor variable and the response variable. However, it is important to consider the context and the overall model fit when interpreting p-values in regression analysis.

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