Give examples of two variables with a strong positive and negative linear correlation
hifadhinitz
Answered question
2022-09-03
(b) Explain in your own words why the linear correlation coefficient should not be used when its absolute value is too low or close to zero. Give an example.
(c) In the passage below identify the explanatory variable and the response variable. Explain why.
A nutritionist wants to determine if the amounts of water consumed each day by persons of the same weight and on the same diet can be used to predict individual weight loss.
Answer & Explanation
Radman76
Beginner2022-09-04Added 7 answers
The variables rate of smoking and alcohol use shows a strong positive linear correlation. As the alcohol use increases, the rate of smoking also increases.
A strong negative linear correlation is observed when one variable increase but the decrease in another variable happens linearly.
The variables amount of playing games each week and GPA of students are strongly negative linear correlation because the more amount students spent in playing games each week, the GPA’s decreases.
(b) When the absolute value of the linear correlation coefficient is too low or close to zero, it shouldn’t be used. It indicates that there is no correlation between the variables, there is no relationship, interdependence, or connection between the variables.
It shows that the variables have nothing to do with each other and use of such variables makes no sense.
The variables, the amount of tea drunk and level of intelligence shows zero correlation.
(c) Explanatory or independent variable: Amount of water consumed each day because it is not depending on any other factor or variable.
Response or dependent variable: Weight loss because the variable weight loss is depending on the amount of water consumed each day.
Inbrunstlr
Beginner2022-09-05Added 2 answers
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Let x and y be two random variables such that:
Corr(x,y) = b, where Corr(x,y) represents correlation between x and y, b is a scalar number in range of [-1, 1]. Let y' be an estimation of y. An example could be y'=y+(rand(0,1)-0.5)*.1, rand(0,1) gives random number between 0, 1. I am adding some noise to the data.
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