x and y
This means that the more years of experience that a person has the higher his or her income is likely to be.
Average winter temperature and the cost of heating the house
'Correlation coefficient' means a statistic representing how closely two variables co-vary; it can vary from -1 (perfect negative correlation) through 0 (no correlation) to +1 (perfect positive correlation)* * * * *A key piece of information that is left out of the answer by True Knowledge (which casts very serious doubts about its name!) is that the statistic only is a measure of linearrelationship. A symmetric non-linear relationship (a parabola, for example) will show zero correlation but show anyone a graph of a parabola and then try convincing them that there is no relationship between the two variables!A correlation for two variables is a measure of the strength of a linear relationship between them. It is a measure that ranges from -1 (the variables move perfectly together but in opposite directions) to 1 (the variables move perfectly together and in the same direction). A correlation coefficient of 0 indicates no linear relationship between the variables.Two important points to note:Correlation measures linear relationship: not any other relationships. Thus a perfect relationship that is symmetric (y = x^2, for example) will have a correlation coefficient of 0.Correlation coefficient is a measure of association, not of causality. In the UK, ice cream sales and swimming accidents are correlated. This is not because eating ice cream causes swimming accidents not because people recover from swimming accidents by eating ice cream. In reality, both events are more likely on warm days - such as they are!
A negative correlation occurs when, as one variable increases, the other variable decreases. Some variables that might have a negative correlation would be: indoor heating use and temperature outside. As the temperature outside decreases, the amount of heating used will increase.
The correlation coefficient for two variables is a measure of the degree to which the variables change together. The correlation coefficient ranges between -1 and +1. At +1, the two variables are in perfect agreement in the sense that any increase in one is matched by an increase in the other. An increase of twice as much in the first is accompanied by double the increase in the second. A correlation coefficient of -1 indicates that the two variables are in perfect opposition. The changes in the two variables are similar to when the correlation coefficient is +1, but this time an increase in one variable is accompanied by a decrease in the other. A correlation coefficient near 0 indicates that the two variables do not move in harmony. An increase in one is as likely to be accompanied by an increase in the other variable as a decrease. It is very very important to remember that a correlation coefficient does not indicate causality.
Velocity and distance of an accelerating object would be one example.
Height and Weight.
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.
Effective procedure can be designed by making sure as many of the likely variables are accounted for within it. This means the person designing it needs to work through the procedure slowly and carefully.
early adulthood
This means that the more years of experience that a person has the higher his or her income is likely to be.
Early adulthood
Friday the 13th is considered an example of a superstition rather than a psychological phenomenon. Some people may experience anxiety or fear related to this date due to cultural beliefs, but it is not a universal psychological phenomenon. It is more about cultural superstitions and folklore.
The two types of variables are: independent variables and dependent variables.Independent variables are variables (ideally only one or very few per experiment) that the experimenter manipulates in the experiment. For example, if you were testing the effect of temperature on plant growth rates, you would likely have similar plants in similar conditions but in areas with different temperatures. The experimenter is changing the temperature between the groups of plants, so the temperature would be the independent variable.The dependent variables are the effects the independent variable has on the experimental subjects. They are changes not being directly controlled or manipulated by the experimenter. In the above temperature vs. plant growth example, the rate of plant growth would be the dependent variable; it depends on the temperature.
there is no solution
Tractors
Tractors