Drop/cm (x) | Bounce/cm (y) |
---|---|

0 | 0 |

10 | 5 |

18 | 10 |

25 | 15 |

28 | 23 |

30 | 20 |

- ______________ Does the relationship appear to be linear, non-linear, or random?
- ______________ Determine the slope of the linear regression for the data.
- ______________ Determine the y-intercept of the linear regression for the data.
- ______________ Determine the correlation coefficient r.
- ______________ Is the correlation positive or negative?
- ______________ Is the correlation none, weak, moderate, strong, or perfect?
- ______________ Determine the coefficient of determination.
- ______________ What percent in the variation in the drop "explains" the variation in the bounce?
- ______________ Use the slope and intercept above to calculate the predicted bounce for a drop of 21 centimeters.
- ______________ Use the slope and intercept to solve for the predicted drop that will produce a bounce of 7 cm.
- ______________ For the following example, presume that the linear relationship holds beyond the maximum x-value. Use the slope and intercept above to calculate the predicted bounce for a drop of 260 centimeters.
- ______________ Is there any one data point that looks like it might be an error?
- ______________ Which specific drop and bounce data point, if any, looks like it might be in error?
- If you picked a point as being an error, why did you pick that point?

Linear Regression Functions | |||
---|---|---|---|

Statistic or Parameter | Math symbol | Stat symbol | OpenOffice |

Slope | m | b | =slope(y-data;x-data) |

Intercept | b | a | =intercept(y-data;x-data) |

Correlation | r | =correl(y-data;x-data) | |

Coefficient of Determination | r^{2} |
=(correl(y-data;x-data))^2 |