A linear relationship is given by the equation y=ax+b
Anything which can be modelled by this equation is a linear relationship. For example, the circumference of a circle is pi times d. This can be modelled by y=ax+b where y is the circumference, a is pi, x is the diameter and b is 0. Therefore, the circumference of a circle as the diameter changes is a perfect linear relationship.
It is in a strait line.
Nothing happens. It simply means that there is no linear relationship between the two variables. It is possible that there is a non-linear relationship or that there is none.
The graph of a linear proportion will be a straight line passing through the origin. The equation will have the form y = mx, also written as y = kx.
The graph is a straight line.
y=mx+b
It means that here is no linear relationship between the two variables. There may be a perfect non-linear relationship, though.
It means that here is no linear relationship between the two variables. There may be a perfect non-linear relationship, though.
is the relationship linear or exponential
Is this a serious question? The moon is a nonexample. My face is a nonexample.
You can create a scatter plot of the two variables. This may tell you if there is a relationship and, if so, whether or not it is linear. If there seems to be a linear relationship, you can carry out a linear regression. Note that the absence of a linear relationship does not mean that there is no relationship. The coordinates of the points on a circle do not show a linear relationship: the correlation coefficient is zero but there is a perfect and simple relationship between the abscissa and the ordinate. Even if there is evidence of a linear relationship, it may be valid only within the range of observations: do not extrapolate. For example, the increase in temperature of a body is linearly related to the amount of heat energy aded. However, for a solid, there will come a stage when the additional heat will not increase the temperature but will be used to melt (or sublimate) the solid. So the linear relationship will be broken.
Positive Linear Relationships are is there is a relationship in the situation. In some equations they aren't linear, but other relationships are, that's a positive linear Relationship.
The correlation coefficient takes on values ranging between +1 and -1. The following points are the accepted guidelines for interpreting the correlation coefficient:0 indicates no linear relationship.+1 indicates a perfect positive linear relationship: as one variable increases in its values, the other variable also increases in its values via an exact linear rule.-1 indicates a perfect negative linear relationship: as one variable increases in its values, the other variable decreases in its values via an exact linear rule.Values between 0 and 0.3 (0 and -0.3) indicate a weak positive (negative) linear relationship via a shaky linear rule.Values between 0.3 and 0.7 (0.3 and -0.7) indicate a moderate positive (negative) linear relationship via a fuzzy-firm linear rule.Values between 0.7 and 1.0 (-0.7 and -1.0) indicate a strong positive (negative) linear relationship via a firm linear rule.The value of r squared is typically taken as "the percent of variation in one variable explained by the other variable," or "the percent of variation shared between the two variables."Linearity Assumption. The correlation coefficient requires that the underlying relationship between the two variables under consideration is linear. If the relationship is known to be linear, or the observed pattern between the two variables appears to be linear, then the correlation coefficient provides a reliable measure of the strength of the linear relationship. If the relationship is known to be nonlinear, or the observed pattern appears to be nonlinear, then the correlation coefficient is not useful, or at least questionable.
The direction of a linear relationship is positive when the two variables increase together and decrease together. The direction is negative if an increase in one variable is accompanied by a decrease in the other. The strength of the relationship tells you, in the context of a scatter plot of the two variables, how close the observations are to the line representing the linear relationship. There are various very closely related measures: regression coefficient or product moment correlation coefficient (PMCC) are commonly used. These can take any value between -1 and +1. A value of -1 represents a perfect negative relationship, +1 represents a perfect positive relationship. A value of 0 represents no linear relationship (there may be a non-linear one, though). Values near -1 or +1 are said show a strong linear relationship, values near 0 a weak one. There is no universal rule about when a relation goes from being strong to moderate to none.
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.
If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.
Positive Linear Relationships are is there is a relationship in the situation. In some equations they aren't linear, but other relationships are, that's a positive linear Relationship.
no ,horizontal line is a linear relationship