They are the same.
It is a line of the form y = c where c is any constant.It is a line of the form y = c where c is any constant.It is a line of the form y = c where c is any constant.It is a line of the form y = c where c is any constant.
. . . a Line graph.Line graphs are used to track changes over short and long periods of time. When smaller changes exist, line graphs are better to use than bar graphs. Line graphs can also be used to compare changes over the same period of time for more than one group.. . . a Pie Chart.Pie charts are best to use when you are trying to compare parts of a whole. They do not show changes over time.. . . a Bar Graph.Bar graphs are used to compare things between different groups or to track changes over time. However, when trying to measure change over time, bar graphs are best when the changes are larger.. . . an Area Graph.Area graphs are very similar to line graphs. They can be used to track changes over time for one or more groups. Area graphs are good to use when you are tracking the changes in two or more related groups that make up one whole category (for example public and private groups).. . . an X-Y Plot.X-Y plots are used to determine relationships between the two different things. The x-axis is used to measure one event (or variable) and the y-axis is used to measure the other. If both variables increase at the same time, they have a positive relationship. If one variable decreases while the other increases, they have a negative relationship. Sometimes the variables don't follow any pattern and have no relationship.:)
no.. thats false.. its actually the opposite
A whole lot of numbers! The simplest example would probably be 0.3. To be exact, there exist uncountably infinitely many real numbers between any 2 distinct real numbers.
Yes, it does. Every time there are variables in direct or inverse relationship, there is a constant of proportionality.
A relationship between variables
the perception of a relationship between two variables that does not actually exist.
Yes, a correlation can exist between two variables, regardless of their nature as dependent or independent. The correlation coefficient quantifies the degree of relationship between variables, indicating how changes in one variable are associated with changes in the other. However, correlation does not imply causation.
Illusory correlation refers to the perception of a relationship between two variables that does not actually exist. This can occur when rare events are paired together in a person's mind, leading to the mistaken belief that there is a causal connection between them. In reality, the correlation is just a product of coincidence or bias.
Causation, correlation...
non sexual relationship
competition
There doesn't exist such a thing. What does exist are standardized variables, which are variables with mean = 0 and standard deviation = 1
Where only bivariate collinear relations exist, a matrix of correlation coefficients is a perfectly adequate diagnostic tool for identifying collinearity. However, they are incapable of diagnosing a collinear relationship involving more than two indepdendent variables. This is the advantage of auxilliary regression. They allow a researcher to detect a collinear relationship between as many independent variables as the researcher requires.
yes
These changing quantities are called variables. A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent, dependent, and controlled.