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When an experiment shows that two variables are closely related, it suggests that changes in one variable are associated with predictable changes in the other variable. This can indicate a causal relationship between the variables, where one variable directly influences the other. However, it is important to consider other factors and conduct further research to establish a robust understanding of the relationship.
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What are the three different types of correlation?
The three different types of correlation are positive correlation (both variables move in the same direction), negative correlation (variables move in opposite directions), and no correlation (variables show no relationship).
What is the relationship between the variables in the suvat equations used to describe motion?
The suvat equations used to describe motion show the relationship between the variables of displacement (s), initial velocity (u), final velocity (v), acceleration (a), and time (t). These variables are interconnected and can be used to calculate different aspects of an object's motion.
What did Einstein show by his train experiment?
Einstein's train experiment demonstrated the principle of time dilation, where time is experienced differently by observers in motion relative to each other. This experiment illustrated how time slows down for objects in motion, and is a key concept in his theory of special relativity.
What is the best method to show a comparison between two variables?
It depends on the variables. An example would have been helpful. The difference between chalk and cheese is obvious - you can see and taste the difference. The difference between identical twins, for instance, is rather more difficult - it could be as small as one twin has a little mole, and the other twin doesn't. The more information you can put into your question, the better.
What are the key findings and implications of Young's double slit experiment?
The key findings of Young's double slit experiment show that light behaves as both a wave and a particle. This duality challenges traditional ideas about the nature of light. The implications of this experiment have had a significant impact on the development of quantum mechanics and our understanding of the fundamental nature of the universe.
When an experiment sHow is that two variable are closely related the experiment shows?
coorelation
When an experiment shows that two variable are closely related what does the experiment show?
The experiment shows that there is a correlation between the two variables, meaning that as one variable changes, the other variable changes in a consistent way. However, it does not necessarily establish a cause-and-effect relationship between the variables. Further analysis is needed to determine causation.
The factor in an experiment that is changed by the experinenter is the?
A scientific experiment must be done by something that can show change. All of these can be variables.
What can be used to show a cause and effect between two variables?
A controlled experiment can be used to show a cause and effect relationship. ex: an experiment studying the effect of a certain medicine on patients.
What is a way of plotting data to show changes occur in related variables?
A scatterplot.
What can a line graph tell you about the relationship between the variables in an experiment?
A line graph can tell you how changes in one variable are related to changes in the other. A line graph cannot show causality. A line graph can show non-linear relationships which some other analytical techniques may not identify. In particular, they are good for identifying relationships between the variables that change over the domain. A line graph can also help identify points where the nature of the relationship changes - eg tension and breaking point, or temperature and phase. The spread of observations about the "line of best fit" gives a measure of how closely the variables are related and how much of the measurement is systemic or random error.
What can a line graph tell you about the relationship between variables in a experiment?
A line graph can tell you how changes in one variable are related to changes in the other. A line graph cannot show causality. A line graph can show non-linear relationships which some other analytical techniques may not identify. In particular, they are good for identifying relationships between the variables that change over the domain. A line graph can also help identify points where the nature of the relationship changes - eg tension and breaking point, or temperature and phase. The spread of observations about the "line of best fit" gives a measure of how closely the variables are related and how much of the measurement is systemic or random error.
What can A line graph tell you about the relationship between the variables in experiment?
A line graph can tell you how changes in one variable are related to changes in the other. A line graph cannot show causality. A line graph can show non-linear relationships which some other analytical techniques may not identify. In particular, they are good for identifying relationships between the variables that change over the domain. A line graph can also help identify points where the nature of the relationship changes - eg tension and breaking point, or temperature and phase. The spread of observations about the "line of best fit" gives a measure of how closely the variables are related and how much of the measurement is systemic or random error.
What can line graph tell you about the relationship between the variables in an experiment?
A line graph can tell you how changes in one variable are related to changes in the other. A line graph cannot show causality. A line graph can show non-linear relationships which some other analytical techniques may not identify. In particular, they are good for identifying relationships between the variables that change over the domain. A line graph can also help identify points where the nature of the relationship changes - eg tension and breaking point, or temperature and phase. The spread of observations about the "line of best fit" gives a measure of how closely the variables are related and how much of the measurement is systemic or random error.
What can a line graph tell you about the relationships between the variables in experiment?
A line graph can tell you how changes in one variable are related to changes in the other. A line graph cannot show causality. A line graph can show non-linear relationships which some other analytical techniques may not identify. In particular, they are good for identifying relationships between the variables that change over the domain. A line graph can also help identify points where the nature of the relationship changes - eg tension and breaking point, or temperature and phase. The spread of observations about the "line of best fit" gives a measure of how closely the variables are related and how much of the measurement is systemic or random error.
What can a line graph tell you the relationship between the variables in an experiment?
A line graph can tell you how changes in one variable are related to changes in the other. A line graph cannot show causality. A line graph can show non-linear relationships which some other analytical techniques may not identify. In particular, they are good for identifying relationships between the variables that change over the domain. A line graph can also help identify points where the nature of the relationship changes - eg tension and breaking point, or temperature and phase. The spread of observations about the "line of best fit" gives a measure of how closely the variables are related and how much of the measurement is systemic or random error.
Closely related species show more in nucleotide sequences than distantly related species?
similarity
