If by positive you mean that an increase in the independent variable is accompanied by an increase in the dependent variable then this will be indicated by a correlation close to one.
What is considered 'close to one' depends on the field of study. In some fields where it can be quite difficult to establish relationships between variables a correlation of, say, 0.35 might be considered important, provided of course that it has been shown to be statistically significant.
A linear relationship is one where your equation forms a straight line. A positive linear relationship is one where this line has a positive gradient.
The line that connects the dots is relatively straight.
Whenever you are given a series of data points, you make a linear regression by estimating a line that comes as close to running through the points as possible. To maximize the accuracy of this line, it is constructed as a Least Square Regression Line (LSRL for short). The regression is the difference between the actual y value of a data point and the y value predicted by your line, and the LSRL minimizes the sum of all the squares of your regression on the line. A Correlation is a number between -1 and 1 that indicates how well a straight line represents a series of points. A value greater than one means it shows a positive slope; a value less than one, a negative slope. The farther away the correlation is from 0, the less accurately a straight line describes the data.
It looks like a straight line increasing as the x-values increase. So basically a line that goes from the bottom left area to the top right area on a graph.
Straight line.
A straight line on a graph usually indicates a linear relationship between two variables, in this case, two temperatures. The slope of the line can tell us the rate of change between the two temperatures. If the line has a positive slope, it means the temperatures are positively correlated, while a negative slope indicates a negative correlation.
A linear relationship is one where your equation forms a straight line. A positive linear relationship is one where this line has a positive gradient.
The line that connects the dots is relatively straight.
This usually indicates he will never be committed to your relationship. It is better to end the new relationship with him straight away.
A straight line graph plotted on the Cartesian plane
it is a positive relationship
Positive acceleration indicates an increase in speed in a straight line. This means the object is moving faster with time.
A direct correlation, it appears as a straight line on a graph and occurs when variables are related as y=xk.
Yes. * A positive correlation is when the dependant variable increases as the independent one does. * A negative correlation is when the dependant variable decreases as the independent one increases. * Perfect correlation is when all the points lie along a straight line; no correlation is when the points lie all over the place. In calculating the correlation coefficient it can have a value between -1 and 1, with 0 indication no correlation and values between 0 and ±1 showing a greater correlation until ±1 which is perfect correlation. Moderate correlation would be one of these intermediate values, eg ±0.5, which shows the points are moderately related.
"In curvilinear relationships, the data points increase together up to a certain point (like a positive relationship) and then as one increases, the other decreases (negative relationship) or vice versa." A linear relation is very simple: if one variable goes up, the other goes up (positive correlation) or goes down (negative correlation). A curvilinear relation between variables is non-linear (i.e., that cannot be described by a straight line). Basically, anythig not linear is curvilinear.
A direct relationship if the slope of the line is positive. An inverse relationship if the slope of the line is negative.
A straight line which is not vertical.