The value of simple correlation coefficient lies in the interval:
[-1, 1]
Understanding the Range of the Simple Correlation Coefficient
The simple correlation coefficient, often denoted by \(r\) for a sample or \(\rho\) (rho) for a population, is a measure used in statistics to quantify the strength and direction of a linear relationship between two variables. It tells us how closely the data points cluster around a straight line.
One of the fundamental properties of the simple correlation coefficient is its range of possible values. This range is fixed and always lies within a specific interval.
Identifying the Simple Correlation Coefficient Interval
The simple correlation coefficient is a standardized measure, meaning its value doesn't depend on the units of the variables being studied. Its calculation ensures that the resulting value is always between -1 and +1, inclusive.
Let's break down what the values within this interval signify:
- Value of +1: Indicates a perfect positive linear correlation. As one variable increases, the other variable increases proportionally along a straight line.
- Value of -1: Indicates a perfect negative linear correlation. As one variable increases, the other variable decreases proportionally along a straight line.
- Value of 0: Indicates no linear correlation. There is no tendency for the two variables to increase or decrease together linearly. Note that a correlation of 0 does not necessarily mean there is no relationship at all, just no *linear* relationship.
- Values between 0 and +1 (e.g., 0.7): Indicate a positive linear correlation. As one variable increases, the other tends to increase, but not perfectly along a straight line. The closer the value is to +1, the stronger the positive linear relationship.
- Values between -1 and 0 (e.g., -0.7): Indicate a negative linear correlation. As one variable increases, the other tends to decrease, but not perfectly along a straight line. The closer the value is to -1, the stronger the negative linear relationship.
Therefore, the simple correlation coefficient \(r\) (or \(\rho\)) always satisfies the condition:
\(-1 \le r \le 1\)
This can be expressed in interval notation as \([-1, 1]\).
Summarizing Simple Correlation Coefficient Values
Here's a quick summary of what different values of the simple correlation coefficient indicate:
- \(r = 1\): Perfect positive linear relationship
- \(0 < r < 1\): Positive linear relationship (stronger as \(r\) approaches 1)
- \(r = 0\): No linear relationship
- \(-1 < r < 0\): Negative linear relationship (stronger as \(r\) approaches -1)
- \(r = -1\): Perfect negative linear relationship
Considering the options provided, the interval that correctly represents the range of the simple correlation coefficient is \([-1, 1]\).
Revision Table: Simple Correlation Coefficient Range
| Concept | Description | Value Range |
|---|---|---|
| Simple Correlation Coefficient | Measures strength and direction of linear relationship between two variables. | \([-1, 1]\) |
| Value of +1 | Perfect positive linear correlation. | End of range |
| Value of -1 | Perfect negative linear correlation. | End of range |
| Value of 0 | No linear correlation. | Middle of range |
Additional Information: Types of Correlation and Related Concepts
While the simple correlation coefficient measures *linear* relationships, it's important to note other aspects of correlation and related statistical measures:
- Non-linear Relationships: The simple correlation coefficient might be close to zero even if there is a strong non-linear relationship (e.g., a parabolic relationship). Visualizing the data with a scatter plot is crucial.
- Causation: Correlation does not imply causation. A strong correlation between two variables does not mean that one variable causes the other to change. There might be a confounding variable involved.
- Other Correlation Measures: There are other types of correlation coefficients for different types of data or relationships, such as Spearman's rank correlation coefficient (for monotonic relationships) or Kendall's tau. However, the simple correlation coefficient (Pearson correlation) is the most common measure for linear relationships.
- Coefficient of Determination (\(R^2\)): The square of the simple correlation coefficient (\(r^2\)) is the coefficient of determination in simple linear regression. It represents the proportion of the variance in the dependent variable that is predictable from the independent variable. Its value ranges from 0 to 1 (\([0, 1]\)).
Understanding the simple correlation coefficient's range and its limitations is key to correctly interpreting the relationship between variables in statistical analysis.
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Important Questions from Correlation Analysis
Given below are two statements: One is labelled as Assertion (A) and the other is labelled as Reason (R).
Assertion (A): If the securities with less than perfect negative correlation between their price movements are combined, portfolio risk can be reduced significantly.
Reason (R): The term with negative correlation has the effect of reducing the computed value of total portfolio risk, given other terms that are positive.
In the light of the above statements, choose the most appropriate answer from the options given below:
X, Y and Z are three uncorrelated variables having variances \(\sigma_x^2, \sigma_y^2 \:and\:\sigma_z^2\) respectively, then the correlation between X + Y and Y + Z is:
If X ∼ N (0, 1) and Y = X2 then the correlation coefficient r (X, Y) is
Calculate the correlation coefficient between the following values :
x: 3, 5, 1, 7, 5
y: 4, 3, 0, 8, 2
Consider two exponentially distributed random variables X and Y, both having a mean of 0.50. Let Z = X + Y and r be the correlation coefficient between X and Y. If the variance of Z equals 0, then the value of r is _______ (round off to 2 decimal places).
