The closer the correlation is to 1 or -1, the more linear the data is
A mistake in calculations! ;) If the calculations are done correctly then the sample correlation must lie within the closed interval [-1, 1].
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.
Indicate whether these passage contain a faulty move from correlation to cause.if so state your criticism? 1.There is a significant correlation between going to the hospital and dying,so hospital are important causal factors in the occurrence of deaths.
No. The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
A correlation coefficient of 1 (r=1) is a perfect positive correlation.
A mistake in calculations! ;) If the calculations are done correctly then the sample correlation must lie within the closed interval [-1, 1].
Size of variables
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.
Indicate whether these passage contain a faulty move from correlation to cause.if so state your criticism? 1.There is a significant correlation between going to the hospital and dying,so hospital are important causal factors in the occurrence of deaths.
Indicate whether these passages contain a faulty move from correlation to cause. If so state your criticism? 1.There is a significant correlation between going to the hospital and dying ,so hospital are important causal factors in the occurrence of deaths.
the negative sign on correlation just means that the slope of the Least Squares Regression Line is negative.
No. The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
A correlation coefficient of 1 (r=1) is a perfect positive correlation.
The correlation can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).
A serious error. The maximum magnitude for a correlation coefficient is 1.The Correlation coefficient is lies between -1 to 1 if it is 0 mean there is no correlation between them. Here they are given less than -1 value so it is not a value of correlation coefficient.
A correlation with an absolute value near one (ie, either near -1 or near 1) indicates that two variables are in a particularly simple relationship that is uncluttered if you will with the effects of error or other variables. That simple relationship is linear rather than curvilinear or something else which would be more challenging to deal with. Moreover, correlation is a single number that is fairly easy to compute.
Yes, but the relationship need not be causal.