Skirt lengths and intelligence are randomly correlated having a correlation coefficient of zero to plus 0.15 ie knowing the measure of one does not predict the value of the other--they are independent variables. To say such and such are not correlated is to say you have not compared the variables. They may have identity with a value of plus one, or they may be inversely related having a value of negative one, or they may be randomly correlated with a value of zero--but to compare is to correlate.
Generally, if you can reduce, you should, unless the directions say you don't have to.
it means that if you have a set of rules that you follow them and if somebody turns their back you still follow their rules instead of take advantage of the person that is there and also break the rules.
Correlation analysis seeks to establish whether or not two variables are correlated. That is to say, whether an increase in one is accompanied by either an increase (or decrease) in the other most of the time. It is a measure of the degree to which they change together. Regression analysis goes further and seeks to measure the extent of the change. Using statistical techniques, a regression line is fitted to the observations and this line is the best measure of how changes in one variable affect the other variable. Although the first of these variables is frequently called an independent or even explanatory variable, and the second is called a dependent variable, the existence of regression does not imply a causal relationship.
It means that one of them depends on the other. When you change one, the other usually changes in some way. Many relations are functions like y=3x2 - x + 7. Other relations, such as x2 + y2 = 25, are not functions, but they are still relations.
Not necessarily. They must decrease together (the question does not say so). Also, the decreases may not be sufficient for the to be correlated. It is less likely that they are negatively correlated, but with the amount of information in the question that is about all that can be said.
This means that the more years of experience that a person has the higher his or her income is likely to be.
if two variables are positively related,it means that the two variables change in the same direction.that is,if the value of one of the variables increases,the value of the other variable too will increase.for example,if employment as an economic variable increases in a country,and price of goods too increases then we can say that these two variables(employment and price) are positively related
Skirt lengths and intelligence are randomly correlated having a correlation coefficient of zero to plus 0.15 ie knowing the measure of one does not predict the value of the other--they are independent variables. To say such and such are not correlated is to say you have not compared the variables. They may have identity with a value of plus one, or they may be inversely related having a value of negative one, or they may be randomly correlated with a value of zero--but to compare is to correlate.
If I have two source of noise let as say two laser diodes so the pink noise that generate fro both of them is it correlated or uncorrelated
When I say number, I am also including variables and variables with a coefficient (terms). You Have to Cross-Multiply, and then solve algebraicall
You can say that the variables are inversely proportional.
You add to it and say stuff. Coments (positive), answer and ask, you know
The correlation coefficient is a measure of linear association between two (or more) variables. It does not measure non-linear relationships nor does it say anything about causality.
A relationship between variables
A relationship between variables
Independent variable is one that does not vary with respect to other variables while other variables called the dependent variables varies with the variation of the independent variable. for ex: if 'x' is is an independent variable that represents say 'time' lets take another variable the dependent like volume(v) . now we say the volume (v) varies with respect to time and not the other way. so, here 'x' is independent variable & 'v' is dependent variable