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Karl Pearson's Coefficient - 1 Video Lecture | Economics Class 11 - Commerce
FAQs on Karl Pearson's Coefficient - 1 Video Lecture - Economics Class 11 - Commerce
| 1. What is Karl Pearson's coefficient? |  |
Ans. Karl Pearson's coefficient, also known as Pearson's correlation coefficient, is a statistical measure that quantifies the strength and direction of the linear relationship between two variables. It is denoted by the symbol "r" and ranges from -1 to 1. A positive value of r indicates a positive correlation, while a negative value indicates a negative correlation.
| 2. How is Karl Pearson's coefficient calculated? | |
Ans. Karl Pearson's coefficient is calculated using the formula: r = (Σ((X - X̅)(Y - Ȳ))) / (n * sX * sY), where X and Y are the values of the two variables, X̅ and Ȳ are their respective means, n is the number of observations, and sX and sY are the standard deviations of X and Y, respectively.
| 3. What does a value of 0 for Karl Pearson's coefficient indicate? | |
Ans. A value of 0 for Karl Pearson's coefficient indicates no linear relationship between the two variables being analyzed. In other words, there is no correlation between the variables. However, it is important to note that the absence of a linear relationship does not imply the absence of any other type of relationship between the variables.
| 4. What is the significance of Karl Pearson's coefficient? | |
Ans. Karl Pearson's coefficient is widely used in statistics and research to measure the strength and direction of the linear relationship between two variables. It helps researchers understand the degree to which changes in one variable are associated with changes in another variable. This coefficient is particularly useful in fields such as economics, social sciences, and psychology for studying cause-and-effect relationships and making predictions based on observed data.
| 5. Can Karl Pearson's coefficient be used to determine causality between variables? | |
Ans. No, Karl Pearson's coefficient alone cannot be used to determine causality between variables. It only measures the strength and direction of the linear relationship between variables. Causality requires additional evidence and rigorous experimentation to establish a cause-and-effect relationship. Therefore, while Pearson's coefficient can provide valuable insights into the association between variables, it does not imply a causal relationship.
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