The explanation of data is called a theory.
relationship between scientific and philosophical method of investigation
A hypothesis is a testable statement or prediction about the relationship between variables in a research study. Variables are the elements that can change or vary, typically classified as independent (manipulated) and dependent (measured). The hypothesis often posits how changes in the independent variable will affect the dependent variable, guiding the research design and experimentation. Thus, the relationship between a hypothesis and variables is foundational for empirical investigation and analysis.
inferential statistics
direct proportion
cause a change
Independent Variable: interleukin and fatigue Dependent Variable: the relationship -----inferential statistics
As you conduct your investigation, you should consider changing one variable at a time to isolate its effects on the outcome. This allows you to accurately determine the relationship between the variable and the results, ensuring that any changes observed are directly related to the variable being tested. Keeping all other factors constant helps to maintain the integrity of your results and enhances the reliability of your conclusions.
between consumption production
There is a huge relationship between fixed cost and variable cost. These two costs are the opposite of each other.
This would indicate that there is a linear relationship between manipulating and responding variables.
The independent variable is what you are changing in the experiment to get varied results. The dependent variable is the result of what you have changed. So the dependent variable depends on the independent variable. For example, if you are experimenting with the effect of water on height of a plant, the different amounts of water that you give the plant is the independent variable. The height of the plant that you measure as the result of the water is the dependent variable.
If a graph shows the relationship between the dependent variable and the independent variable as a straight line, it indicates a linear relationship between the two variables. This means that changes in the independent variable result in proportional changes in the dependent variable. The slope of the line represents the rate of change, while the y-intercept indicates the value of the dependent variable when the independent variable is zero.