Q: When high scores go with high scores and low with low the correlation coefficient what will be between 0 and?

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A correlation coefficient of 1 or -1 would be the highest possible statistical relationship. However, the calculation of correlation coefficients between non independent values or small sets of data may show high coefficients when no relationship exists.

There is quite a high degree of linear agreement between the two variables with one showing an increase when the other shows a decrease.

The relationship between two random independently distributed variables is considered to be theoretically the weakest when the correlation coefficient is zero and theoretically the strongest when the correlation coefficient is one, indicating a positive relationship between two variables and negative one, indicating a negative relationationship between two variables. I state that this is a theoretical result as if variables are not random, independently distributed, then a high correlation coefficient can result. For example, let us say that we obtained the following data on age and frequency of accidents: Age 18 1 in 18 people have accidents in a year Age 25 1 in 25 people have accident in a year Age 30 1 in 30 people have accidents in a year Age 35 1 in 6 people have accidents. Age 40 1 in 400 people have accidents If I selectively calculated a correlation coefficient this data including only the three groups ages 18, 25 and 30, you can see I will have a correlation coefficient of 1, however the data was not a random sample of all ages. See related link.

It depends on the Reduced Velocity and amplitude of oscillation. Lift Coefficient could be as high as 1.0, and as low as -10.0 at very low reduced velocities.

The answer depends on where in the world. In much of the temperate zone it will be a positive correlation but in some tropical areas people are more likely to stay indoors when the temperature gets very high.

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No. If the correlation coefficient is close to 1 or -1, then the two variables have a high degree of statistical linear correlation. See the related link, particularly the graphs which illustrate correlation.

Correlation coefficient My understanding is: two variables as they relate to one another and how accurately you can predict their behavior to one another when together. Basically the strength of the linear association between two variables. When the variables have a tendency to go up and down together, this is a positive correlation coefficient. Variables with a tendency to go up and down in opposition, (one ends up with a high value and the other a low value) this is negatiove correlation coefficient. An example would be the amount of weight a mom gains during pregnancy and the birth weight of the baby

A correlation coefficient of 1 or -1 would be the highest possible statistical relationship. However, the calculation of correlation coefficients between non independent values or small sets of data may show high coefficients when no relationship exists.

There is quite a high degree of linear agreement between the two variables with one showing an increase when the other shows a decrease.

there is a positive correlation between parental income and children's IQ scores

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.

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 â‰¤ r â‰¤ +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.

The relationship between two random independently distributed variables is considered to be theoretically the weakest when the correlation coefficient is zero and theoretically the strongest when the correlation coefficient is one, indicating a positive relationship between two variables and negative one, indicating a negative relationationship between two variables. I state that this is a theoretical result as if variables are not random, independently distributed, then a high correlation coefficient can result. For example, let us say that we obtained the following data on age and frequency of accidents: Age 18 1 in 18 people have accidents in a year Age 25 1 in 25 people have accident in a year Age 30 1 in 30 people have accidents in a year Age 35 1 in 6 people have accidents. Age 40 1 in 400 people have accidents If I selectively calculated a correlation coefficient this data including only the three groups ages 18, 25 and 30, you can see I will have a correlation coefficient of 1, however the data was not a random sample of all ages. See related link.

yes there is a correlation between high tide and moon rise because the higher the moon gets in the sky the higher the tide will be.

RhoHV stands for correlation coefficient (ρ) between the horizontal and vertical polarizations in radar meteorology. It is used to distinguish different types of precipitation particles based on their shapes and orientations. A low RhoHV value indicates non-uniform shapes or orientations, while a high value suggests more uniform particles such as raindrops.

No

The coefficient of friction between the two surfaces was found to be 0.7, indicating a high resistance to sliding.