I need a definition for this
constant rate of change when graphing in mathematics and for
Positive and Negative correlation
and also what the pattern is in this
a) 1000, 100, 10, 1
I'll give you a definition for positive and negative correlation.
Correlation is a linear relationship between two variables. Correlation can range from -1.00 through 0 to + 1.00. Zero means an absence of a linear relationship. -1.00 is a perfect negative correlation. +1.00 is a perfect positive correlation. Whether the correlation is negative or positive, the closer it is to -1.00 or +1.00, the stronger it is.
An easy way to think of positive correlation might be: when one goes up, the other goes up. Negative correlation: when one goes up, the other goes down. The closer to 0, the weaker the correlation.
I hope this part will help.
what is the difference between linear and non-linear correlation?
The difference between linear and non-linear correlation lies in the nature of the relationship between two variables.
Linear correlation refers to a situation where the relationship between two variables can be represented by a straight line on a graph. In other words, as one variable increases (or decreases), the other variable also changes in a constant and proportional manner. This relationship can be positive (both variables increase together) or negative (one variable increases while the other decreases).
Non-linear correlation, on the other hand, describes a relationship that cannot be represented by a straight line. In this case, the relationship between the variables may be curved, irregular, or follow any other non-linear pattern. The changes in one variable do not necessarily correspond to constant or proportional changes in the other variable. Non-linear correlations can take various forms, such as exponential, logarithmic, quadratic, or sinusoidal.
To determine the type of correlation, it is usually helpful to plot the data points on a graph and observe the pattern. If the points fall fairly close to a straight line (regardless of whether it slopes upward or downward), it suggests a linear correlation. If the points form a curved shape or do not conform to a linear pattern, it indicates a non-linear correlation.
Understanding the type of correlation between variables is crucial in analyzing data and making predictions. It allows us to identify and describe the relationship between variables accurately.