2.03

Q1. Fiona calculated the correlation coefficient to be 0 when observing cell phone production over a 10-year period. She concluded that there is no relationship between the phone production and time. Is Fiona correct? Explain your reasoning.

A)Fiona's conclusion is correct. If the correlation coefficient is 0, there is a linear relationship.

B)Fiona's conclusion is not correct. If the correlation coefficient is less than 1, there is not a linear relationship, but there could be another type of relationship.

C)Fiona's conclusion is correct. If the correlation coefficient is greater than ?1, there is not a linear relationship, but there could be another type of relationship.

D)Fiona's conclusion is not correct. If the correlation coefficient is 0, there is not a linear relationship, but there could be another type of relationship.

E)Fiona's conclusion is correct. If the correlation coefficient is less than 1, there is not a linear relationship, but there could be another type of relationship.

Q2. Data were collected on the distance a baseball will travel when hit by a baseball bat at a certain speed. The speed, s, is measured in miles per hour, and distance, y, is measured in yards. The regression line is given by ? = 4.17 + 52.45s. Identify the slope and y-intercept of the regression line. Interpret each value in context.

A)The slope, b = 52.45, indicates that the distance increases by 52.45 yards for every one mile per hour of speed. The y-intercept, a = 4.17, is the distance estimated by this model if the speed is zero miles per hour.

B)The slope, b = 4.17, indicates that the distance increases by 4.17 yards for every one mile per hour of speed. The y-intercept, a = 52.45, is the distance estimated by this model if the speed is zero miles per hour.

C)The slope, b = 4.17, indicates that the distance decreases by 4.17 yards for every one mile per hour of speed. The y-intercept, a = 52.45, is the distance estimated by this model if the speed is one mile per hour.

D)The slope, b = 52.45, indicates that the distance decreases by 52.45 yards for every one mile per hour of speed. The y-intercept, a = 4.17, is the distance estimated by this model if the speed is one mile per hour.

E)The slope and y-intercept are unable to be determined from the regression line given.

Q3. The weight, y, in pounds, of human babies was tracked for the first 12 weeks after birth where t represents the number of weeks after birth. The linear model representing this relationship is ? = 6.7 + 0.52t. Clarissa wanted to predict the weight of a baby at 16 weeks. What is this an example of, and is this method a best practice for prediction? Explain your reasoning.

A)This is an example of extrapolation. Extrapolation is not a best practice for prediction as the prediction is not accurate because 16 weeks is outside the given interval of 12 weeks.

B)This is an example of extrapolation. Extrapolation is a best practice for prediction as the prediction is accurate even though 16 weeks is outside the given interval of 12 weeks.

C)This is an example of linear modeling. Linear modeling is a best practice for prediction as the prediction is accurate even though 16 weeks is outside the given interval of 12 weeks.

D)This is an example of linear modeling. Linear modeling is not a best practice for prediction as the prediction is not accurate because 16 weeks is outside the given interval of 12 weeks.

E)The type of prediction is unable to be determined by the information given.

Q4. The IQ scores and math test scores of third grade students is given by the regression line ? = ?23.5 + 0.8534s, where ? is the predicted math score and s is the IQ score. An actual math test score for a student is 73.5 with an IQ of 110. Find and interpret the residual.

A)?3.13; The regression line underpredicts the student's math test score.

B)3.13; The regression line overpredicts the student's math test score.

C)3.13; The regression line underpredicts the student's math test score.

D)?3.13; The regression line overpredicts the student's math test score.

E)82.9; The regression line overpredicts the student's math test score.

Q5. The data set for the average height of men at a ballgame compared to their shoe size is represented by the table.

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