## Answers

Enter data like this:

FactorA | FactorB | Variable |

A1 | B1 | 34.2 |

A1 | B1 | 33.3 |

A1 | B1 | 30.4 |

A1 | B1 | 27.9 |

A1 | B2 | 36.3 |

A1 | B2 | 25.9 |

A1 | B2 | 28.5 |

A1 | B2 | 34.7 |

A1 | B3 | 29.7 |

A1 | B3 | 38.9 |

A1 | B3 | 37.6 |

A1 | B3 | 35.6 |

A2 | B1 | 45.5 |

A2 | B1 | 39.7 |

A2 | B1 | 38.3 |

A2 | B1 | 35.9 |

A2 | B2 | 37.5 |

A2 | B2 | 37.9 |

A2 | B2 | 42.4 |

A2 | B2 | 37.5 |

A2 | B3 | 33.2 |

A2 | B3 | 45 |

A2 | B3 | 40.1 |

A2 | B3 | 44.5 |

A3 | B1 | 31 |

A3 | B1 | 35.1 |

A3 | B1 | 29.5 |

A3 | B1 | 39.1 |

A3 | B2 | 38.3 |

A3 | B2 | 35.7 |

A3 | B2 | 36.3 |

A3 | B2 | 33.5 |

A3 | B3 | 41.4 |

A3 | B3 | 39.8 |

A3 | B3 | 39 |

A3 | B3 | 36.7 |

**Rcode:**

> data = read.csv(".\anova.csv") #read data

> View(data)

> model = lm(Variable~FactorA+FactorB+FactorA*FactorB,data=data) #fit a model

> anova(model) #anova

**Output:**

Analysis of Variance Table

**Response: Variable Df Sum Sq Mean Sq F value Pr(>F) FactorA 2 297.51 148.756 10.4667 0.0004313 *** FactorB 2 86.69 43.343 3.0497 0.0639497 . FactorA:FactorB 4 26.40 6.599 0.4643 0.7612715 Residuals 27 383.73 14.212**

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Here, Factor A is signficant only at alpha 0,0.01,0.05 etc.

FactorB and Interaction effects are not significant.

*Please rate my answer and comment for doubt.*

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