#### Q12 Part V: Linear Regression • Name:

Duration of colonial admin versus GDP
Location | Years of colonization (years) | Per capita gross domestic product (dollars) |

Majuro | 100 | 1896 |

Moen | 87 | 1335 |

Nauru | 78 | 2702 |

Palau | 120 | 6076 |

Pohnpei | 101 | 2711 |

Tarawa | 66 | 538 |

The data in this section comes from a report on years of colonization of Pacific island states and their present per capita gross domestic product (GDP). From the Wikipedia: "A region's gross domestic product, or GDP, is one of the several measures of the size of its economy. The GDP of a country is defined as the market value of all final goods and services produced within a country in a given period of time. It is also considered the sum of value added at every stage of production of all final goods and services produced within a country in a given period of time."

- _________ Calculate the slope of the best fit (least squares) line.
- _________ Calculate the y-intercept of the best fit (least squares) line.
- _________ Is the correlation positive, negative, or neutral?
- _________ Use the equation of the best fit line to calculate the predicted GDP for a country colonized for 95 years.
- _________ Use the inverse of the best fit line to calculate the predicted years of colonization for a GDP of $4500.
- _________ Calculate the linear correlation coefficient r for the data.
- _________ Is the correlation none, low, moderate, high, or perfect?
- _________ Calculate the coefficient of determination.
- _________ What percent of the variation in the years of colonization explains the variation in the GDP?
- _________ What is the predicted GDP for a nation with zero years of colonization?
- _________ What is the years of colonization predicted for a GDP of zero dollars?

Linear Regression Statistics |

Statistic or Parameter | Symbol | Equations | OpenOffice |

Slope | b | | =SLOPE(y data; x data) |

Intercept | a | | =INTERCEPT(y data; x data) |

Correlation | r | | =CORREL(y data; x data) |

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