Table 3.

Residuals:
. | ||||
---|---|---|---|---|

Min . | 1Q . | Median . | 3Q . | Max . |

−2.4145 | −0.3365 | 0.1191 | 0.4094 | 1.3290 |

Coefficients: | ||||

Estimate | SE | t value | Pr( >|t|) | |

(Intercept) | −0.28733 | 0.08092 | −3.551 | 0.000528 *** |

Education | 0.05106 | 0.01663 | 3.071 | 0.002579 ** |

Comp.First.sum | −0.38043 | 0.10809 | −3.519 | 0.000589 *** |

scale(Age) | 0.01967 | 0.05797 | 0.339 | 0.734911 |

Gender1 | −0.09847 | 0.05915 | −1.665 | 0.098286 ^{†} |

Residuals:
. | ||||
---|---|---|---|---|

Min . | 1Q . | Median . | 3Q . | Max . |

−2.4145 | −0.3365 | 0.1191 | 0.4094 | 1.3290 |

Coefficients: | ||||

Estimate | SE | t value | Pr( >|t|) | |

(Intercept) | −0.28733 | 0.08092 | −3.551 | 0.000528 *** |

Education | 0.05106 | 0.01663 | 3.071 | 0.002579 ** |

Comp.First.sum | −0.38043 | 0.10809 | −3.519 | 0.000589 *** |

scale(Age) | 0.01967 | 0.05797 | 0.339 | 0.734911 |

Gender1 | −0.09847 | 0.05915 | −1.665 | 0.098286 ^{†} |

Note: lm(formula = CardsMinusComputers.lg ∼ Education + Comp.First.sum + scale(Age) + Gender, data = d). ^{†}*p* < .1. **p* < .05. ***p* < .01. ****p* < .001.

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