Keyword | CPC | PCC | Volume | Score | Length of keyword |
---|---|---|---|---|---|

linear regression using sklearn | 0.51 | 0.8 | 9899 | 54 | 31 |

linear | 1.17 | 0.9 | 8700 | 62 | 6 |

regression | 1.02 | 0.4 | 9360 | 63 | 10 |

using | 0.21 | 0.2 | 6395 | 19 | 5 |

sklearn | 0.99 | 0.6 | 4698 | 77 | 7 |

Keyword | CPC | PCC | Volume | Score |
---|---|---|---|---|

linear regression using sklearn | 0.83 | 0.4 | 6861 | 60 |

linear regression using sklearn in python | 0.02 | 0.6 | 7396 | 42 |

multiple linear regression using sklearn | 0.94 | 0.2 | 2075 | 94 |

simple linear regression using sklearn | 0.95 | 0.9 | 9022 | 9 |

linear regression using sklearn example | 1.13 | 0.2 | 4128 | 46 |

multiple linear regression in python sklearn | 0.68 | 0.6 | 8107 | 24 |

python sklearn linear regression score | 0.13 | 0.9 | 774 | 79 |

regression in python sklearn | 0.56 | 0.7 | 347 | 66 |

multi linear regression python sklearn | 1.84 | 0.4 | 3001 | 22 |

Simple linear regression is a technique that we can use to understand the relationship between one predictor variable and a response variable.. This technique finds a line that best “fits” the data and takes on the following form: ŷ = b 0 + b 1 x. where: ŷ: The estimated response value; b 0: The intercept of the regression line; b 1: The slope of the regression line

We perform a hypothesis test of the "significance of the correlation coefficient" to decide whether the linear relationship in the sample data is strong enough to use to model the relationship in the population. The sample data are used to compute r, the correlation coefficient for the sample.