line graph
Time Series.
Yes, a mathematical expression can have no variables, but such an expression is usually not very useful. An example of a valid expression without variables is: 1+1=2
It gives a measure of the extent to which values of the dependent variable move with values of the independent variables. This will enable you to decide whether or not the model has any useful predictive properties (significance). It also gives a measure of the expected changes in the value of the dependent variable which would accompany changes in the independent variable. A regression model cannot offer an explanation. The fact that two variables move together does not mean that changes in one cause changes in the other. Furthermore it is possible to have very closely related variables which, because of a wrongly specified model, can show no correlation. For example, a LINEAR model fitted to y=x2 over a symmetric range for x will show zero correlation!
They are useful in situation with many variables and can create useful digital images and can represent how a system or process works or.... all of the above....
Line Graph
Data tabel
line graph
line graph
A regression graph is most useful for predicting dependent variables, as it shows the relationship between the independent and dependent variables, allowing for the prediction of future values.
i believe the answer is.... A strong OBSERVATION can be useful for making predictions
Predictions
when a sets of data can be separated by 2 orders of variables, which are the independent & dependent variables.
Time Series.
Constant Variables
The number of electrons an element has determine the organization. This table helps in making predictions about how an element will chemically react.
Ex Post Facto (also called Causal Comparative Research) is useful whenever: • We have two groups which differ on an independent variable and we want to test hypotheses about differences on one or more dependent variables OR • We have two groups which already differ on a dependent variable and we want to test hypotheses about differences on one or more independent variables
Ex Post Facto (also called Causal Comparative Research) is useful whenever: • We have two groups which differ on an independent variable and we want to test hypotheses about differences on one or more dependent variables OR • We have two groups which already differ on a dependent variable and we want to test hypotheses about differences on one or more independent variables