A _________ indicates that as the value of one variable (X) increases, the value of the other variable (Y) will also increase.Similarly when variable X decreases, decrease in Y too takes place.
A. negative correlation
B. positive correlation
C. zero correlation
D. all of the above
Answers
Explanation:
Negative correlation is a relationship between two variables in which one variable increases as the other decreases, and vice versa. In statistics, a perfect negative correlation is represented by the value -1, a 0 indicates no correlation, and a +1 indicates a perfect positive correlation. A perfect negative correlation means the relationship that exists between two variables is negative 100% of the time.
Answer:
What is Negative Correlation?
Negative correlation is a relationship between two variables in which one variable increases as the other decreases, and vice versa. In statistics, a perfect negative correlation is represented by the value -1, a 0 indicates no correlation, and a +1 indicates a perfect positive correlation. A perfect negative correlation means the relationship that exists between two variables is negative 100% of the time.
Correlation
Understanding Negative Correlation
Negative correlation or inverse correlation is a relationship between two variables whereby they move in opposite directions. If variables X and Y have a negative correlation (or are negatively correlated), as X increases in value, Y will decrease; similarly, if X decreases in value, Y will increase. The degree to which one variable moves in relation to the other is measured by the correlation coefficient, which quantifies the strength of the correlation between two variables.
For example, if variables X and Y have a correlation coefficient of -0.1, they have a weak negative correlation, but if they have a correlation coefficient of -0.9, they would be regarded as having a strong negative correlation. The higher the negative correlation between two variables, the closer the correlation coefficient will be to the value -1. By the same token, two variables with a perfect positive correlation would have a correlation coefficient of +1, while a correlation coefficient of zero implies that the two variables are uncorrelated and move independently of each other.
The correlation coefficient (usually denoted by "r" or "R") can be determined by regression analysis. The square of the correlation coefficient (generally denoted by "R2", or R-squared) represents the degree or extent to which the variance of one variable is related to the variance of the second variable, and is typically expressed in percentage terms. For example, if a portfolio and its benchmark have a correlation of 0.9, the R-squared value would be 0.81.