Sure. If you can observe that when variable A changed, variable B didn't change, and this happens repeatedly, that is a good indication that there is no relationship between those variables.
Scatter chart
scatter chart
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
Line graph is used to show relationship between two variables.
A tentative descriptive statement of the relationship between the variables is called hypothesis
Correlational surveys involve measuring the relationship between two or more variables without manipulating them. By collecting data on these variables from a sample of participants, researchers can determine the extent to which changes in one variable are associated with changes in another, providing insight into potential patterns or connections between the variables.
Correlation between two variables implies a linear relationship between them. The existence of correlation implies no causal relationship: the two could be causally related to a third variable. For example, my age is correlated with the number of TV sets in the UK but obviously there is no causal link between them - they are both linked to time.
Correlation is defined as the degree of relationship between two or more variables. It is also called the simple correlation. The degree of relationship between two or more variables is called multi correlation. when two or more variables are said to be higjly correlated it means that they have a strong relationship such that a given rise or fall in one variable will lead to a direct change in the other variable or variables. good examples of highly correlated variables are price and quantity, wage rate and out put, tax and income.
Scatter chart
1 or -1
The relationship is a function if a vertical line intersects the graph at most once.
scatter chart
Identify the variables: Determine the variables involved in the relationship. Establish causation: Determine if changes in one variable directly cause changes in another. Control for confounding variables: Consider and address other factors that may influence the relationship. Establish directionality: Determine the direction of cause and effect between the variables. Test causation: Conduct experiments or analyze data to test and confirm the causal relationship.
Linear Analysis is a technique used in statistics to determine the constant relationship between two variables.
There are no relations between different variables. If you want to enable a relationship between variables, you must write the code to implement that relationship. Encapsulating the variables within a class is the most obvious way of defining a relationship between variables.
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
type the equation that shows the relationship between the variables in this chart.