Practice quiz.

Two variable relationships

- An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost for apartments based on the size of the apartment as defined by square footage. A sample of 20 apartments in a particular residential neighborhood was selected and the data are given below.
- Construct a scatterplot and include a title and axis labels.

- Calculate the regression equation. Write out empirical equation representing the relationship between square feet and price of an apartment rental.

- Interpret the meaning of the intercept (a) and the slope coefficient (b) in your equation you calculated in b. You must be very specific (labeling slope and y intercept is not an acceptable interpretation).

- Predict the mean monthly rent for an apartment that has 1000 square feet.

- Using the printout from your regression equation (see Part 2 on the In Lab exercise regression analysis sheet) please determine if the slope coefficient is statistically different from zero.

- Your friends Jim and Jennifer are considering signing a lease for an apartment in this residential neighborhood. They are trying to decide between two apartments, one with 1000 square feet for a monthly rent of $1,175 and the other with 1200 square feet for a monthly rent of $1525. Based on your statistical analysis above (parts a-e), what would you recommend to them? Explain why.

To complete question #1, you need to turn in the excel printout of the scatterplot, and the regression equation. Cutting and pasting these into a word document would be best.

- (5 points) Suppose you were given from a data set the following information:

Mean of X | 45 |

Mean of Y | 200 |

Standard deviation of X | 12 |

Standard deviation of Y | 35 |

Correlation coefficient | .6 |

Calculate the regression line using the data above. (Find both the intercept and the slope).

Apartment | Size | Monthly Rent |

(in Square feet) | (in $) | |

1 | 850 | 950 |

2 | 1450 | 1600 |

3 | 1085 | 1200 |

4 | 1232 | 1500 |

5 | 718 | 950 |

6 | 1485 | 1700 |

7 | 1136 | 1650 |

8 | 726 | 935 |

9 | 700 | 875 |

10 | 956 | 1150 |

11 | 1100 | 1400 |

12 | 1285 | 1650 |

13 | 1985 | 2300 |

14 | 1369 | 1800 |

15 | 1175 | 1400 |

16 | 1225 | 1450 |

17 | 1245 | 1100 |

18 | 1259 | 1700 |

19 | 1150 | 1200 |

20 | 896 | 1150 |

Data for Question #1: