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PLEEEEEAAASE ASAP!
a) Describe how the line of best fit and the correlation coefficient can be used to determine the correlation between the two variables on your graph.

b) Describe the type of correlation between the two variables on your graph. How do you know?

c) Does the correlation between the variables imply causation? Explain.

d) How do you calculate the residuals for a scatterplot?

e) Calculate the residuals for your scatterplot in step 2d.

f) Create a residual plot for your data.

g) Does your residual plot show that the linear model from the regression calculator is a good model? Explain your reasoning.

Graph&data are attached

PLEEEEEAAASE ASAP A Describe How The Line Of Best Fit And The Correlation Coefficient Can Be Used To Determine The Correlation Between The Two Variables On Your class=
PLEEEEEAAASE ASAP A Describe How The Line Of Best Fit And The Correlation Coefficient Can Be Used To Determine The Correlation Between The Two Variables On Your class=

Sagot :

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Answer:

Kindly check explanation

Step-by-step explanation:

Given the data showing the relationship between GPA and hours of study :

From the regression model given :

Regression equation is:

y = 0.141x + 1.096

Also, the regression Coefficient, R = 0.957

a) Describe how the line of best fit and the correlation coefficient can be used to determine the correlation between the two variables on your graph.

From the regression equation, we can infer if the relationship or correlation between the two variables is positive or negative from the value of the slope, a positive slope Value means a positive relationship while a negative slope value means a negative relationship.

The Correlation Coefficient, R also gives the strength of relationship, with values close to - 1 or 1 depicting a strong relationship while positive and negative R values also depictava positive or negative relationship.

Here there is a strong positive relationship between GPA and Hours.

Correlation does not imply causation. Correlation only shows the type of relationship between variables and it does not mean that high GPA values are causes by long hours of study and vice versa.

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