Q: How scatter diagram helpful in study of correlation?

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An advantage of using a correlational study is that it allows you to investigate variables that cannot be directly manipulated.

Sometimes when we graph the relationship between two varying quantities of real life on a coordinate plane we get dots scattered on coordinate plane. For example graph average math score of students from grade 8. Let's consider their 8-week score. (1, 50) (2, 52), (3, 49), (4, 58), (5, 60), (6, 65), (7, 64), (8, 68). If you plot these coordinates you will get the points scattered points. 1) It is helpful to study the relationship between two varying quantities. 2) It says whether the relation is positive or negative. Sometimes we may not have any relation. 3) Such relation is called as correlation. 4) In the above example the graph has positive correlation. As the week increases the average scores also increases. There may be some down fall. Overall, it is a positive variation.

That's a question that can only really be answered via a study. Take a random sample of people (from your school for example) and plot their weight against their average daily walking distance (you may have to make your subjects carry a pedometer during the study period). Do you see a negative relationship on the graph?As a second step, calculate the correlation coefficient. As negative correlation gets stronger the correlation coefficient will get closer to -1.

Any variable can be a correlation variable. In some cases there may be no apparent correlation but that, in itself, that means nothing. For example, the x and y coordinates in the equation of a circle (or any symmetric shape) are not correlated. On the other hand, there is a pretty good correlation between my age and the number of cars in the world.A correlation variable is simply a variable that you study to see if changes in the variable that you are interested in is, in any way, related to changes in the correlation variable, and to get some idea of the degree to which they move in line.

What is the probability of a type I error? What does this mean?How would you use this same information but set it up in a way that allows you to conduct a t-test? An ANOVA?

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Pairs of scores from a correlational study are usually plotted on a scatter plot. This allows researchers to visualize the relationship between the variables and assess the strength and direction of the correlation.

Correlation study is restricted to linear relationships between the variable(s) being studied.

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Causation cannot be determined.

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No correlational study is not cause and effect because correlation does not measure cause.

An advantage of using a correlational study is that it allows you to investigate variables that cannot be directly manipulated.

Sometimes when we graph the relationship between two varying quantities of real life on a coordinate plane we get dots scattered on coordinate plane. For example graph average math score of students from grade 8. Let's consider their 8-week score. (1, 50) (2, 52), (3, 49), (4, 58), (5, 60), (6, 65), (7, 64), (8, 68). If you plot these coordinates you will get the points scattered points. 1) It is helpful to study the relationship between two varying quantities. 2) It says whether the relation is positive or negative. Sometimes we may not have any relation. 3) Such relation is called as correlation. 4) In the above example the graph has positive correlation. As the week increases the average scores also increases. There may be some down fall. Overall, it is a positive variation.

A correlational study is used to determine the relationship between two variables. It shows whether and how two variables change together, but does not establish causation.

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You might be referring to a positive correlation between grades and number of study hours.

That's a question that can only really be answered via a study. Take a random sample of people (from your school for example) and plot their weight against their average daily walking distance (you may have to make your subjects carry a pedometer during the study period). Do you see a negative relationship on the graph?As a second step, calculate the correlation coefficient. As negative correlation gets stronger the correlation coefficient will get closer to -1.