No, it would not. It is possible that the statistical model is under-specified and that the variables being studied are all "caused" by another variable.
The object you are testing in a hypothesisThe Independant or manipulated variable is what 'I' or you are changing....if you are measuring the effect of fertilizer on plant height, the Independant variable is the presence/absence of fertilizer.
Usually the expression is employed in the context of the relationship between a dependent variable and another variable. The latter may or may not be independent: often it is time but that is not necessary. In some cases there is some indication that that there is a linear relationship between the two variables and that relationship is referred to as a trend.Note that a trend is not the same as causation. There may appear to be a strong linear trend between two variables but the variables may not be directly related at all: they may both be related to a third variable. Also, the absence of linear trends does not imply that the variables are unrelated: there may be non-linear relationships.
An "hypothesis" doesn't have independent and dependent variables until you design an experiment to test it. If you want to test the effect that salt in solution has on the freezing point of the solution, then the independent variable is the presence, absence, or concentration of the salt. The dependent variable is the freezing temperature you measure in each condition.
A contingency square in psychology refers to a matrix that shows the relationship between different conditions or variables in research. It helps researchers analyze how the presence or absence of one variable is related to the presence or absence of another variable. Contingency squares are often used in studies examining the interaction between two or more factors.
In Alexander Fleming's experiment, the independent variable was the presence or absence of penicillium mold, which he introduced to the culture plates. The dependent variable was the growth or inhibition of bacterial colonies in the vicinity of the mold.
The independent variable in a biotic response experiment is the factor that is deliberately changed or manipulated by the experimenter. Examples could include the presence or absence of a predator, changes in temperature, or variations in nutrient availability.
It can be. But equally, a scatterplot that looks really random can be used to show the absence of a relationship between two variables.
Yes, the independent variable would be the presence or absence of music. By manipulating this variable, you can observe the effect of music on plant growth while keeping other factors constant.
Correlation. It will merely determine whether or not there is a linear relationship between the variables. However, the absence of correlation is not absence of a relation - only that the relationship is not linear.For example, if you take any set of points that are symmetrically placed about a vertical axis - such as from a circle, ellipse or parabola, or parts of a sine or cosine curve - then the correlation will be 0. But, the fact that these are well-defined curves clearly implies a very definite [non-linear] relationship.
In Redi's experiment on spontaneous generation, the manipulated variable was the presence or absence of gauze on the jars to prevent flies from accessing the meat, while the responding variable was the presence or absence of maggots developing on the meat.
There are various forms. In linear programming, a dummy variable may be used to convert an inequality into an equation. For example x < 10 can be written as x + u = 10 where u > 0. In this case, it is also called a slack variable. Dummy variables are used in regression to indicate the presence or absence of a factor, or for binary variables. For example, male/female could be coded numerically as 0/1 where, because the question is binary, the exact coding does not matter.
Absence of causal connection refers to a situation where there is no direct relationship or link between two events or factors. It implies that one event does not directly cause the other to occur, and there is no clear cause-and-effect relationship between them. This lack of causal connection suggests that the events are independent of each other.