Please, give examples of two variables that have a perfect positive linear correlation and two variables that have a perfect negative linear correlation.

Leonel Schwartz
2022-10-02
Answered

Please, give examples of two variables that have a perfect positive linear correlation and two variables that have a perfect negative linear correlation.

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seppegettde

Answered 2022-10-03
Author has **7** answers

Perfect positive linear correlation:

Correlation coefficient value is +1 then the two variables have perfect positive correlation.

Perfect negative linear correlation:

Correlation coefficient value is -1 then the two variables have perfect negative correlation.

Example of perfect positive linear correlation is: income and expenditure, heights and weights of a group of persons.

Example of perfect negative linear correlation: price and demand of a commodity, the volume and pressure of a perfect gas.

Correlation coefficient value is +1 then the two variables have perfect positive correlation.

Perfect negative linear correlation:

Correlation coefficient value is -1 then the two variables have perfect negative correlation.

Example of perfect positive linear correlation is: income and expenditure, heights and weights of a group of persons.

Example of perfect negative linear correlation: price and demand of a commodity, the volume and pressure of a perfect gas.

asked 2022-09-03

(a) Give examples of two variables that have a strong positive linear correlation and two variables that have strong negative linear correlation.

(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.

(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.

asked 2022-09-14

For a data set of weights and highway fuel consumption amounts of four types of automobile, the linear correlation coefficient is found and the P-value is 0.001. Please, write a statement that interprets the P-value and includes a conclusion about linear correlation.

asked 2022-08-24

A set of data with correlation coefficient of -0.55 has a

a) strong negative linear correlation

b) weak negative linear correlation

c) moderate negative linear correlation

d) little or no linear correlation

a) strong negative linear correlation

b) weak negative linear correlation

c) moderate negative linear correlation

d) little or no linear correlation

asked 2022-07-20

a. Two variables have a positive linear correlation. Does the dependent variable increase or decrease as the independent variable increases? Explain.

b. Two variables have a negative linear correlation. Does the dependent variable increase or decrease as the independent variable increases?

c. Describe the range of values for the correlation coefficient, r.

d. What does the sample correlation coefficient r measure? Which value indicates a stronger correlation:

${r}_{1}=0.975$ or ${r}_{2}=-0.987$. Explain, please.

b. Two variables have a negative linear correlation. Does the dependent variable increase or decrease as the independent variable increases?

c. Describe the range of values for the correlation coefficient, r.

d. What does the sample correlation coefficient r measure? Which value indicates a stronger correlation:

${r}_{1}=0.975$ or ${r}_{2}=-0.987$. Explain, please.

asked 2022-07-16

Given the linear correlation coefficient r and the sample size n, determine the critical values of r and use your finding to state whether or not the given r represents a significant linear correlation. Use a significance level of 0.05. $r=0.767,n=25$

a. Critical values: $r=\pm 0.396$, no significant linear correlation

b. Critical values: $r=\pm 0.487$ , no significant linear correlation

c. Critical values: $r=\pm 0.396$ , significant linear correlation

d. Critical values: $r=\pm 0.487$ , significant linear correlation

a. Critical values: $r=\pm 0.396$, no significant linear correlation

b. Critical values: $r=\pm 0.487$ , no significant linear correlation

c. Critical values: $r=\pm 0.396$ , significant linear correlation

d. Critical values: $r=\pm 0.487$ , significant linear correlation