**Lift(B, C) = c(B->C)/s(C) = s(B u C)/(s(B) x s(C))**

Q1: Lift Analysis

Please calculate the following lift values for the table correlating burger and chips below:

- Lift(Burger, Chips) = s(BuC) = 600/1000 / s(B)= 800/1000 * 1000/1000 = .6/.8*1 =
**.75 Negative** - Lift(Burgers, ^Chips) = 400/1000/ 1000/1000 * 600/1000 =
**.66 Negative** - Lift(^Burgers, Chips) = 200/1000/ 400/1000*800/1000 =
**.625 Negative** - Lift(^Burgers, ^Chips) = 200/1000 / 400/1000* 600/1000 =
**.769 Negative**

Please also indicate if each of your answers would suggest independent, positive correlation, or negative correlation?

Chips | ^Chips | Total Row | |

Burgers | 600 | 400 | 1000 |

^Burgers | 200 | 200 | 400 |

Total Column | 800 | 600 | 1400 |

Q2:

Please calculate the following lift values for the table correlating shampoo and ketchup below:

- Lift(Ketchup, Shampoo) 100/1000 / 300/1000* 300/1000 =
**111 Positive** - Lift(Ketchup, ^Shampoo) 200/1000 / 300/1000*600/1000 =
**111 Positive** - Lift(^Ketchup, Shampoo)= 200/1000 / 600/1000*600/1000 =
**555 Negative** - Lift(^Ketchup, ^Shampoo) = 400/1000 / 600/1000*600/1000 =
**111 Positive**

Please also indicate if each of your answers would suggest independent, positive correlation, or negative correlation?

Shampoo | ^Shampoo | Total Row | |

Ketchup | 100 | 200 | 300 |

^Ketchup | 200 | 400 | 600 |

Total Column | 300 | 600 | 900 |

Q3: Chi Squared Analysis

Please calculate the following chi squared values for the table correlating burger and chips below (Expected values in brackets). = **16.25**

- Burgers & Chips
**Negatively** - Burgers & Not Chips
**Positively** - Chips & Not Burgers
**Positively** - Not Burgers and Not Chips
**Negatively**

For the above options, please also indicate if each of your answer would suggest independent, positive correlation, or negative correlation?

Chips | ^Chips | Total Row | |

Burgers | 900 (800) | 100 (200) | 1000 |

^Burgers | 300 (400) | 200 (100) | 500 |

Total Column | 1200 | 300 | 1500 |

Q4: Chi Squared Analysis

Please calculate the following chi squared values for the table correlating burger and sausages below (Expected values in brackets).= **0**

- Burgers & Sausages
**(independent)** - Burgers & Not Sausages
**(independent)** - Sausages & Not Burgers (
**independent**) - Not Burgers and Not Sausages (
**independent)**

For the above options, please also indicate if each of your answer would suggest independent, positive correlation, or negative correlation?

Sausages | ^Sausages | Total Row | |

Burgers | 800 (800) | 200 (200) | 1000 |

^Burgers | 400 (400) | 100 (100) | 500 |

Total Column | 1200 | 300 | 1500 |

Q5:

Under what conditions would Lift and Chi Squared analysis prove to be a poor algorithm to evaluate correlation/dependency between two events? **Where there is a high amount of Null Transactions**

Please suggest another algorithm that could be used to rectify the flaw in Lift and Chi Squared? **Kulczynski**