nstructions: Use the technology of your choice (calculator, Excel, GeoGebra, Google Sheets) to find the regression line, the correlation coefficient of the following data sets, and interpolate or extrapolate the requested data. (Round all numbers to the nearest hundredth, when necessary.) Data Set: {(0,7),(1,5),(1.5,3),(2,5),(2.3,3.2),(3,2),(3.5,4)}{(0,7),(1,5),(1.5,3),(2,5),(2.3,3.2),(3,2),(3.5,4)} The regression line is: y=y= Answer −− Answer xx Based on the regression line, we would expect the value of response variable to be Answer when the explanatory variable is 0. (NOTE: This is the yy-intercept.) For each increase of 1 in of the explanatory variable, we can expect a(n) Answerdecreaseincrease of Answer in the response variable. (NOTE: This is the slope.) If x=2.5x=2.5, the yˆ=y^= Answer. This is an example of Answerextrapolationinterpolation. The correlation coefficient is r=r= Answer.

Instructions: Use the technology of your choice (calculator, Excel, GeoGebra, Google Sheets) to find the regression line, the correlation coefficient of the following data sets, and interpolate or extrapolate the requested data. (Round all numbers to the nearest hundredth, when necessary.)

Data Set: {(0,7),(1,5),(1.5,3),(2,5),(2.3,3.2),(3,2),(3.5,4)}{(0,7),(1,5),(1.5,3),(2,5),(2.3,3.2),(3,2),(3.5,4)}

1. The regression line is: y=y= Answer −− Answer xx
2. Based on the regression line, we would expect the value of response variable to be Answer when the explanatory variable is 0. (NOTE: This is the yy-intercept.)
3. For each increase of 1 in of the explanatory variable, we can expect a(n) Answerdecreaseincrease of Answer in the response variable. (NOTE: This is the slope.)
4. If x=2.5x=2.5, the yˆ=y^= Answer. This is an example of Answerextrapolationinterpolation.
5. The correlation coefficient is r=r= Answer.