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There should be one dependent variables. Depending on the type of research you are doing, the amount of independent variables will change.
If you are doing research on a large scale, you will use more independent variables. If it's on a small scale, you will use very little. If you are not able to run your regression it means your sample size is too small or you have too many independent variables.
What else can I help you with?
What should be close to the real thing in an experiment?
In an experiment, the conditions, variables, and procedures should closely resemble real-life situations to ensure the results are valid and applicable to the real world. This includes controlling for as many extraneous variables as possible and designing the experiment in a way that reflects the natural environment or scenario being studied.
How many experimental variables should you have?
It is recommended to only have one experimental variable in a scientific study to properly isolate its effects and draw valid conclusions. Multiple variables can complicate the results and make it difficult to determine which variable is responsible for the observed effects.
How many variables can be tested at a time?
The number of variables that can be tested at a time may vary depending on the experimental design and resources available. In practice, it is common to test one to three variables simultaneously in order to effectively analyze and interpret the results. However, some experimental designs may allow for testing more variables at once.
How many variables do you test within one experiment?
It is recommended to test one variable at a time in an experiment to ensure that any observed effects can be attributed to that specific variable. This approach allows for clearer interpretation of results and helps to avoid confounding factors that might impact the outcome.
What is true about a controlled experiment?
A controlled experiment involves manipulating one variable (independent variable) while keeping all other variables constant in order to observe the effect on another variable (dependent variable). This allows for causal relationships to be inferred between the independent and dependent variables. Control groups are used in controlled experiments to provide a baseline for comparison.
