Explain what it means to have a strong positive relationship in this situation.

Explain what it means to have a weak negative relationship in this context.

What is an equation of the line of best fit?

What is the value of the correlation coefficient?[br]

 Which value for the correlation coefficient is most likely to match a line of best fit of the form [math]y=mx+b[/math] for this situation?

The researcher creates a line of best fit, [math]y=0.091x+0.060[/math], and wants to find the residuals for the companies that have been in business for 3 years. [br][br]Find the residuals for the two points representing companies that have been in business for 3 years, [math]\left(3,0.42\right)[/math] and [math]\left(3,0.3\right)[/math].[br]

Compare the residuals for the two companies who have been in business for 3 years. How are they different? How are they similar? What does the information about the residuals for the two companies tell you about their fair trade business? [br]

Which value for r indicates the worst for the data?

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[/img]