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.
When the data points on a line graph do not fall along a straight line, it is referred to as a nonlinear relationship. This indicates that the relationship between the variables is more complex, potentially involving curves or other shapes rather than a simple linear correlation. Nonlinear patterns can arise from various factors, such as exponential growth, quadratic relationships, or cyclical trends.
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.
The four different types of correlations are positive, negative, zero, and nonlinear correlations. In a positive correlation, as one variable increases, the other also increases. In a negative correlation, as one variable increases, the other decreases. A zero correlation indicates no relationship between the variables, while a nonlinear correlation shows a relationship that does not follow a straight line, meaning the association changes in strength or direction at different levels of the variables.
A scatter plot is the best graph to show correlation between two variables. In a scatter plot, individual data points are plotted on a Cartesian plane, allowing for a visual representation of the relationship between the variables. If the points tend to cluster along a line, it indicates a strong correlation, whether positive or negative. The closer the points are to forming a straight line, the stronger the correlation.
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 correlation close to -1 or 1 indicates a strong linear relationship between two variables, but it does not guarantee that the relationship is strictly linear. Correlation measures the strength and direction of a linear association, and while a high correlation suggests that the points tend to align closely along a straight line, it can still be influenced by non-linear relationships or outliers. Therefore, it's essential to visually inspect the data and consider other analyses to confirm the nature of the relationship.
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.
A straight line on a graph indicates a linear relationship between the dependent variable and the independent variable. This means that as the independent variable changes, the dependent variable changes at a constant rate. The slope of the line represents this rate of change, while the y-intercept indicates the value of the dependent variable when the independent variable is zero. Overall, a straight line signifies predictability and a consistent correlation between the two variables.
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
A direct correlation, it appears as a straight line on a graph and occurs when variables are related as y=xk.