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Mind Map: Correlation | Economics Class 11 - Commerce

The document Mind Map: Correlation | Economics Class 11 - Commerce is a part of the Commerce Course Economics Class 11.
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FAQs on Mind Map: Correlation - Economics Class 11 - Commerce

1. What is correlation and why is it important in statistics?
Ans.Correlation is a statistical measure that describes the extent to which two variables change together. It is important because it helps researchers understand relationships between variables, which can inform predictions and decisions in various fields such as finance, healthcare, and social sciences.
2. How can correlation be measured?
Ans.Correlation can be measured using various statistical tools, with the most common being the Pearson correlation coefficient. This coefficient ranges from -1 to 1, indicating the strength and direction of a linear relationship between two variables. A value close to 1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation.
3. What are the different types of correlation?
Ans.The main types of correlation include positive correlation, negative correlation, and zero correlation. Positive correlation occurs when both variables increase together, negative correlation occurs when one variable increases while the other decreases, and zero correlation indicates no relationship between the variables.
4. Can correlation imply causation?
Ans.No, correlation does not imply causation. While two variables may be correlated, it does not mean that one variable causes changes in the other. There may be other underlying factors or variables influencing the observed relationship.
5. What are some common misconceptions about correlation?
Ans.Some common misconceptions about correlation include the belief that a high correlation means a strong causal relationship, or that correlation can only be positive. Additionally, some may think that correlation coefficients can only be calculated for normally distributed data, when in fact they can be calculated for various types of data distributions.
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