Give an example of two variables that are correlated in a strong and positive direction and two that are correlated in a strong a negative direction. Comment on any other factors that could influence their relationship and what can an cannot be inferred from these correlations.
Strong positive correlation :
Example : Ice cream sales in dollars, daily high temperatures. As temperature increases and weather becomes hotter, people tend to purchase more and more ice creams.
Other factor : Marketing of ice cream can also influence the above relationship.
Strong negative correlation :
Example : The weight of a car and miles per gallon. Cars that are heavier tend to get less miles per gallon of gas.
Other factor : Horsepower of car can also influence the above relationship
Correlation only indicates the strength of association between variables. It does not reflect the impact of the unit change in one variable on the other variable.
Give an example of two variables that are correlated in a strong and positive direction and...
Give me a real-life example of Two variables that have a strong positive correlation. Use Google or other research methods to find the correlation coefficient to support your claim. What reasons or conditions attribute to the strong correlation? Do some research, but do not make up a numerical example. Two variables that have a strong negative correlation. Use Google or other research methods to find the correlation coefficient to support your claim. What reasons or conditions attribute to the strong...
If two variables are each correlated significantly with the dependent variable, then the multiple correlation will be a) the sum of the two correlations. b) the sum of the two correlations squared. c) no less than the larger of the two individual correlations. d) It could take on any value.
If two variables (x and y) have a very strong linear relationship, it can be inferred that a) y causes a change in x b) A third variable causes changes in x and y c) x causes a change in y d) There cannot be any causal relationship between x and y e) None of the above
What does correlation does not imply causation mean? Give an example of two real-life variables that are correlated but do not have a causal relationship. Also give an example of two variables that are correlated and that also have a causal relationship.
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....
10A) Give an example of a paired data set (with at least 5 pairs) that demonstrates a strong (but not perfect) negative linear correlation. x y 10B) Give a real life example of two variables that are likely to be positively correlated. Specifically explain why you believe your chosen two variables are positively correlated. 10C) Suppose some researchers studied children in high schools and found a strong positive linear correlation between the availability of vending machines and obesity. From this...
Pick any two variables that you feel may be related and estimate what you think the strength of the correlation coefficient would be for those two variables. In your response, estimate the value of r. For example, specify a strong (.7 to .9), medium (.4 to .6), or low (0 to .3) value for r. The value of the coefficient can be positive or negative. For example, consider an increase in police patrols in a neighborhood and the number of...
A set of bivariate data consists of these measurements on two variables, x and y: 2 4 4 6 8 4 7 6 (a) Make a scatterplot. Comment on the form, direction, and strength of the relationship. The relationship appears to be linear, positive, and fairly weak. The relationship appears to be linear, negative, and fairly strong. The relationship appears to be linear, positive, and fairly strong. The relationship appears to be linear, negative, and fairly weak. ● The relationship...
An r² (coefficient of determination) of –1.00 indicates: A. a strong positive relationship between the two variables. B. a strong negative relationship between the two variables. C. an error in computation. D. no relationship between the two variables.
Question #1 Explain what is meant by a positive relationship between two variables and a negative relationship between two variables. Describe examples of situations in which one would expect to find a positive relationship and when one would expect to find a negative relationship. Can one assign direction when both variables in a table are dichotomous?