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How does the time period or number of variables effect correlation coefficient?
Wiki User
∙ 10y ago
The time period may not affect the correlation coefficient at all. If looking at the correlation between the mass and volume of steel objects, time is totally irrelevant.
The effect of the number of variables depends on whether or not the extra variables are related to ANY of the variables in the equation.
Wiki User
∙ 10y ago
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What does it mean when a positive correlation coefficient between the dependent variable Y and the independent variable X indicates?
It means that the two variables are likely dependent. The higher the number of the positive correlation the stronger the connection.
Is the correlation coefficient a pure number without units?
Yes.
What is correlation What are the different types of correlation Why is it important to determine correlation What does it mean when it is said that two variables have no correlation?
A correlation is the relationship between two or more variables. Correlations are described as either weak or strong, and positive or negative. There can be a perfect correlation between variables, or no correlation between variables. It is important to determine the correlation between variables in order to know if and how closely changes in one variable are reflected by changes in another variable. This is done by determining the coefficient of correlation (r), which describes the strength of the relationship between variables and the direction. -1 ≤ r ≤ +1 if r= +1 or -1, there is a perfect correlation if r= 0 there is no correlation between the variables. a value closer to + or - 1 demonstrates a strong correlation, while a value closer to 0 demonstrates a weak correlation. a + value demonstrates that when one variable increases the other variable increases, while a - value demonstrates that when one variable increases the other variable decreases. However, it is very important to understand that correlation is not the same as relationship. Consider the two variables, x and y such that y = x2 where x lies between -a and +a. There is a clear and well-defined relationship between x and y, but the correlation coefficient r is 0. This is true of any pair of variables whose graph is symmetric about one axis. Conversely, a high correlation coefficient does not mean a strong relationship - at least, not a strong causal relationship. There is pretty strong correlation between my age and [the log of] the number of television sets in the world. That is not because TV makes me grow old nor that my ageing produces TVs. The reason is that both variables are related to the passage of time.
What is a stronger correlation than -0.54?
The correlation coefficient ranges from 0 to ±1. The sign of the correlation coefficient shows the correlation as positive (as one increases so does the other) or negative (as one increases the other decreases). 0 represent no correlation and ±1 represents perfect correlation. The further from 0 towards ±1, the stronger the correlation, ie the greater the absolute value* of the correlation coefficient the stronger the correlation. To have a stronger correlation than -0.54 the absolute value must be greater than 0.54; ie all correlation coefficients that are less than -0.54 (eg -0.6, -0.9) and all those greater than +0.54 (eg 0.7, 0.95) are stronger correlations. Mathematically speaking, all those with a correlation coefficient r such that |r| > 0.54 *The absolute value of a number is the number ignoring its sign (ie how far it is away from 0 ignoring the direction along the number line), eg |56| = 56 |-45| = 45 |-56| = 56 Thus |-56| = |56| = 56.
How will you interpret the coefficient of correlation?
Correlation is a measure of the strength of a linear relationship between two variables. In theory it ranges between -1 and +1, although in practice, random and observation error make this value smaller.Near -1, the correlation is very strongly negative, which means that an increase in one variable is accompanied by a decrease in the other.Near +1, the correlation is very strongly positive, which means that an increase in one variable is accompanied by an increase in the other.Near 0, the correlation is weak and there is no linear pattern in which the two variables change.There are two very critical points to remember:Correlation does not measure causation. For example, the number of cars on the road is correlated to my age but my getting older does not cause more cars to be made and cars do not cause me to grow old (at least, not with most drivers!)Correlation will only measure a linear relationship. If you examine a relationship like y = x2, over a symmetric interval, the correlation coefficient will be close to 0. But there is, clearly, a very strong relationship - just that it is not linear.Finally, the importance of any correlation coefficient is subjective and depends on the context. A correlation coefficient that is high for a sociological study may be considered moderate for a high school physics experiment.
What is a correlation coefficient?
a number derived from the formula for measuring a correlation and indicating the strength and direction of a correlation
What does it mean when a positive correlation coefficient between the dependent variable Y and the independent variable X indicates?
It means that the two variables are likely dependent. The higher the number of the positive correlation the stronger the connection.
A Correlation coefficient is a number used to describe?
validity
Is the correlation coefficient a pure number without units?
Yes.
What is correlation What are the different types of correlation Why is it important to determine correlation What does it mean when it is said that two variables have no correlation?
A correlation is the relationship between two or more variables. Correlations are described as either weak or strong, and positive or negative. There can be a perfect correlation between variables, or no correlation between variables. It is important to determine the correlation between variables in order to know if and how closely changes in one variable are reflected by changes in another variable. This is done by determining the coefficient of correlation (r), which describes the strength of the relationship between variables and the direction. -1 ≤ r ≤ +1 if r= +1 or -1, there is a perfect correlation if r= 0 there is no correlation between the variables. a value closer to + or - 1 demonstrates a strong correlation, while a value closer to 0 demonstrates a weak correlation. a + value demonstrates that when one variable increases the other variable increases, while a - value demonstrates that when one variable increases the other variable decreases. However, it is very important to understand that correlation is not the same as relationship. Consider the two variables, x and y such that y = x2 where x lies between -a and +a. There is a clear and well-defined relationship between x and y, but the correlation coefficient r is 0. This is true of any pair of variables whose graph is symmetric about one axis. Conversely, a high correlation coefficient does not mean a strong relationship - at least, not a strong causal relationship. There is pretty strong correlation between my age and [the log of] the number of television sets in the world. That is not because TV makes me grow old nor that my ageing produces TVs. The reason is that both variables are related to the passage of time.
What is the difference between correlation analysis and?
Correlation analysis is a type of statistical analysis used to measure the strength of the relationship between two variables. It is used to determine whether there is a cause-and-effect relationship between two variables or if one of the variables is simply related to the other. It is usually expressed as a correlation coefficient a number between -1 and 1. A positive correlation coefficient means that the variables move in the same direction while a negative correlation coefficient means they move in opposite directions.Regression analysis is a type of statistical analysis used to predict the value of one variable based on the value of another. This type of analysis is used to determine the relationship between two or more variables and to determine the direction strength and form of the relationship. Regression analysis is useful for predicting future values of the dependent variable given a set of independent variables.Correlation Analysis is used to measure the strength of the relationship between two variables.Regression Analysis is used to predict the value of one variable based on the value of another.
What is a number multiplied to one or more variables?
a coefficient
What is a single number that describes the degree of relationship between two variables?
Correlation
What is a stronger correlation than -0.54?
The correlation coefficient ranges from 0 to ±1. The sign of the correlation coefficient shows the correlation as positive (as one increases so does the other) or negative (as one increases the other decreases). 0 represent no correlation and ±1 represents perfect correlation. The further from 0 towards ±1, the stronger the correlation, ie the greater the absolute value* of the correlation coefficient the stronger the correlation. To have a stronger correlation than -0.54 the absolute value must be greater than 0.54; ie all correlation coefficients that are less than -0.54 (eg -0.6, -0.9) and all those greater than +0.54 (eg 0.7, 0.95) are stronger correlations. Mathematically speaking, all those with a correlation coefficient r such that |r| > 0.54 *The absolute value of a number is the number ignoring its sign (ie how far it is away from 0 ignoring the direction along the number line), eg |56| = 56 |-45| = 45 |-56| = 56 Thus |-56| = |56| = 56.
What is variable proportion?
When I say number, I am also including variables and variables with a coefficient (terms). You Have to Cross-Multiply, and then solve algebraicall
How will you interpret the coefficient of correlation?
Correlation is a measure of the strength of a linear relationship between two variables. In theory it ranges between -1 and +1, although in practice, random and observation error make this value smaller.Near -1, the correlation is very strongly negative, which means that an increase in one variable is accompanied by a decrease in the other.Near +1, the correlation is very strongly positive, which means that an increase in one variable is accompanied by an increase in the other.Near 0, the correlation is weak and there is no linear pattern in which the two variables change.There are two very critical points to remember:Correlation does not measure causation. For example, the number of cars on the road is correlated to my age but my getting older does not cause more cars to be made and cars do not cause me to grow old (at least, not with most drivers!)Correlation will only measure a linear relationship. If you examine a relationship like y = x2, over a symmetric interval, the correlation coefficient will be close to 0. But there is, clearly, a very strong relationship - just that it is not linear.Finally, the importance of any correlation coefficient is subjective and depends on the context. A correlation coefficient that is high for a sociological study may be considered moderate for a high school physics experiment.
Is a correlation coefficient of -626 very strong?
A correlation coefficient has a range of -1 to 1. Any number outside of this range has been incorrectly calculated. I note that is you meant to ask - Is r= -0.626 is a very strong correlation coefficient? then the answer No, this value is not a strong indicator that a linear relationship exists. Please see related link. The diagrams showing x-y graphs and the correlation coefficients is a good way to gain a "feel" of the coefficients and strength of relationships.
