Question

What factors affect the strength (or size) of a correlation between two variables

What factors affect the strength (or size) of a correlation between two variables

Homework Answers

Answer #1

Factors affecting the magnitude or size of correlation:

  • Is the relation linear?

r can only be large if the association between the X and Y variables is linear. (in case of only linear association)

  • Are the distribution shapes of scores on X and Y the same?

r of +1 tells us that there is a one to one mapping of locations (in z score terms) of X and Y values. It is not possible to get a one to one mapping for all scores if the distribution shapes for X and Y are different from each other ( assume that both X and Y are normally distributed)

  • 3. Are there bivariate outliers in the X, Y scatter plot?

similarly other factors are as follows

  • how reliable are X ,Y measure?
  • is there restricted range for scores on X and/or Y?
  • Does the samples include groups of people for whom X,Y association differs?
  • sampling error

please like ?

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Which statement explains why correlation could be 0 even if a strong relationship between two variables...
Which statement explains why correlation could be 0 even if a strong relationship between two variables existed? Group of answer choices Since the correlation is 0, there is no strong relationship between the two variables; and a scatterplot would be misleading. Correlation can be 0 even if there is a strong linear relationship between the variables. Correlation only measures the strength of the relationship between two variables when the units of the two variables are the same. Correlation does not...
The strength of the relationship between two quantitative variables can be measured by the y-intercept of...
The strength of the relationship between two quantitative variables can be measured by the y-intercept of the simple linear regression equation. the slope of a simple linear regression equation. both the coefficient of correlation and the coefficient of determination. the coefficient of determination. the coefficient of correlation.
A negative correlation between variables X and Y means that ____. A. the correlation between variables...
A negative correlation between variables X and Y means that ____. A. the correlation between variables X and Y is very weak B. scores on variable X has little predicting power on the corresponding scores on variable Y C. higher scores on variable X correspond to lower scores on variable Y and vice versa D. Higher scores on variable X correspond to higher scores on variable Y while lower scores on variable X correspond to lower scores on variable Y...
In each example, explain whether there is a significant linear correlation between the two variables, and...
In each example, explain whether there is a significant linear correlation between the two variables, and determine what proportion of the variation can be explained by the linear association between the variables: Linear correlation coefficient between bear chest size and weight is 0.993, N = 21 Linear correlation coefficient between the number of registered automatic weapons and the murder rate is 0.885, N = 1000 Linear correlation coefficient between the weight in female subjects and the BMI metric is 0.936,...
What is the regression line? Give an example of simple correlation between two variables.
What is the regression line? Give an example of simple correlation between two variables.
correlation measures the degree to which two variables are related to one another. Here are the...
correlation measures the degree to which two variables are related to one another. Here are the definitions of the three possibilities: Positive correlations: In this type of correlation, both variables increase or decrease at the same time. A correlation coefficient close to +1.00 indicates a strong positive correlation. Negative correlations: This type of correlation indicates that as the amount of one variable increases, the other decreases (and vice versa). A correlation coefficient close to -1.00 indicates a strong negative correlation....
A Correlation Coefficient is a measurement of the relationship between two variables. A positive correlation means...
A Correlation Coefficient is a measurement of the relationship between two variables. A positive correlation means that as one variable increases, the second variable increases too. A negative correlation means that as one variable increases, the second variable decreases, or as one variable decreases, the second variable increases. Positive and negative correlations exists in nature, science, business, as well as a variety of other fields. Please watch the following video for a graphical explanation of the correlation coefficient: For Discussion...
If you find a Pearson's correlation of zero between two continuous variables, this means: A. There...
If you find a Pearson's correlation of zero between two continuous variables, this means: A. There is no relationship between the two variables B. There is a strong relationship between the two variables C. There may be a non-linear relationship between the two variables D. Two of these answers are correct
You wish to determine if there is a linear correlation between the two variables at a...
You wish to determine if there is a linear correlation between the two variables at a significance level of α=0.01. You have the following bivariate data set. x y 8.8 46.6 30.2 68.7 42.7 61.8 30.8 83.1 27.3 45.1 26.9 46.1 32 110.2 9.2 25.9 18.4 102.7 42 51.8 10.4 86.1 46.3 41.1 12.7 42.9 19.6 27.3 22.9 59.5 26.9 59.7 2.4 46.8 What is the critical value for this hypothesis test? rc.v. =   What is the correlation coefficient for...
    37. A correlation coefficient of r = -0.98 between two quantitative variables A and B...
    37. A correlation coefficient of r = -0.98 between two quantitative variables A and B indicates that             A. As A increases, B tends to increase.             B. Changes in A cause changes in B. C. As A increases, B tends to decrease.             D. There is a very weak association between A and B, and change in A will not affect B. Please show work using excel functions!