# The Bronx High School of Science, Mathematics Department by cYevV8B

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```									Bronx High School of Science                                                         Name: ______________________ Prd ____
Ms. Varuzza                                                                          Date: ______________________
M\$6: Regression Lines

1.   The accompanying table shows the enrollment of a preschool from 1980 through 2000. Write a linear regression equation to
model the data in the table.

2.   The 1999 win-loss statistics for the American League East baseball teams on a
particular date is shown in the accompanying chart.

Find the mean for the number of wins,     L , and the mean for the number of losses, L , and
determine if the point ( W , L ) is a point on the line of best fit. Justify your answer.

3.   A real estate agent plans to compare the price of a cottage, y, in a town on the seashore
to the number of blocks, x, the cottage is from the beach. The accompanying table
shows a random sample of sales and location data.

Write a linear regression equation that relates the price of a cottage to its distance from the beach.
Use the equation to predict the price of a cottage, to the nearest dollar, located three blocks from
the beach.
4.   The accompanying table shows the number of new cases reported by the Nassau and Suffolk County Police Crime Stoppers
program for the years 2000 through 2002.

If x  1 represents the year 2000, and y represents the number of new cases, find the equation of best fit using a power regression,
rounding all values to the nearest thousandth.
Using this equation, find the estimated number of new cases, to the nearest whole number, for the year 2007.

5.   Jean invested \$380 in stocks. Over the next 5 years, the value of her investment grew, as shown in the accompanying table.

Write the exponential regression equation for this set of data, rounding all values to two decimal places.
Using this equation, find the value of her stock, to the nearest dollar, 10 years after her initial purchase.

6.   The accompanying table shows the amount of water vapor, y, that will saturate 1 cubic meter of air at different temperatures,
x.

Write an exponential regression equation for this set of data, rounding all values to the nearest thousandth. Using this equation,
predict the amount of water vapor that will saturate 1 cubic meter of air at a temperature of 50°C, and round your answer to the
nearest tenth of a gram.

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