1. Explain the Measures of Relationship ?
2. what is simple regression analysis, explain its components ?
3. Explain the difference between multiple correlation and regression?
4. what does mean partial correlation?
5. explain the Association in Case of Attributes in research?
1. Measures of Relationship refer to the statistical techniques used to determine the degree and strength of association between two or more variables. The most common measures of relationship include correlation coefficient, covariance, and regression analysis.
2. Simple regression analysis is a statistical technique used to estimate the relationship between two variables - the dependent variable (Y) and the independent variable (X). The components of simple regression analysis include the regression equation (Y = a + bX), where a is the intercept and b is the slope of the line, and the coefficient of determination (R-squared), which measures the proportion of the variance in the dependent variable that is predictable from the independent variable.
3. The main difference between multiple correlation and regression is that correlation measures the strength and direction of the relationship between two or more variables, while regression analysis predicts the value of the dependent variable based on the values of one or more independent variables. In multiple regression analysis, multiple independent variables are included in the analysis to predict the dependent variable.
4. Partial correlation is a statistical technique used to measure the relationship between two variables while controlling for the effects of one or more other variables. It is often used to determine the unique contribution of a specific variable on the relationship between two other variables.
5. Association in Case of Attributes in research refers to the relationship between categorical variables or attributes. Measures of association for categorical variables include chi-square test, phi coefficient, and contingency coefficient. These measures help researchers understand the strength and direction of the relationship between different categories of variables.