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.
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.
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.
Straight line.
A linear relationship refers to a direct proportional connection between two variables, where a change in one variable results in a consistent change in the other. This relationship can be represented graphically as a straight line on a coordinate plane, typically described by the equation (y = mx + b), where (m) is the slope and (b) is the y-intercept. In this context, the slope indicates the rate of change between the variables, and a positive slope reflects a direct correlation, while a negative slope indicates an inverse correlation.
A line on a graph that compares two variables, temperature for example tells us a great deal about the relationship of these variables in the experimental system. When the line is straight it reflects a direct and proportional relationship between the two factors.
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.
it is a positive relationship
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
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.
When the dots on a graph form a straight line, it indicates a linear relationship between the variables being plotted. This alignment suggests that there is a consistent rate of change, which can be described by a linear equation. If the dots perfectly align, it might indicate a perfect linear correlation, while a scattered arrangement could suggest a strong or weak correlation.
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.
A direct relationship if the slope of the line is positive. An inverse relationship if the slope of the line is negative.