# Call center typically have high turnover. The director of human resources for a large bank has compiled data on about 70 former employees at one of the bank’s call centers in the Excel file Call Center Data. In writing an article about call center w

# Call center typically have high turnover. The director of human resources for a large bank has compiled data on about 70 former employees at one of the bank’s call centers in the Excel file Call Center Data. In writing an article about call center w

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Call center typically have high turnover. The director of human resources for a large bank has compiled data on about 70 former employees at one of the bank’s call centers in the Excel file Call Center Data. In writing an article about call center working conditions, a reporter has claimed that the average tenure is no more than two years. Formulate and test a hypothesis using these data to determine if this claim can be disputed.

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Using the date in the excel file Home Market Value, develop a multiple linear regression model for estimating the market value as a function of both the age and size of the house. Find a 95% confidence interval for the mean market value for houses that are 30 years old and have 1,800 square feet and a 95% prediction interval for a house that is 30 years old with 1,800 square feet.

**PLEASE ATTACHED IS THE CALL CENTER DATA and HOME MARKET VALUES FOR THE ASSIGNMENT.**

Please be sure your work is organized, legible, and your responses are substantive. You need to submit all details of your work including excel sheets used to arrive to the solution. It is not enough to attach your excel sheet. You MUST provide interpretation of results and describe conclusions.

## Call center typically have high turnover. The director of human resources for a large bank has compiled data on about 70 former employees at one of the bank’s call centers in the Excel file Call Center Data. In writing an article about call center

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# xx xxxx xxxxxx xxxxxxxxx xxxx high xxxxxxxxx xxx director of xxxxx resources for a large xxxx has compiled data xx xxxxx xx former employees at one xx xxx xxxxxxxx call xxxxxxx xx xxx Excel file xxxx Center Data. In writing xx article xxxxx xxxx center x

xx

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xxxx center xxxxxxxxx xxxx xxxx xxxxxxxxx xxx xxxxxxxx xx human xxxxxxxxx xxx a large xxxx has xxxxxxxx data xx about xx former xxxxxxxxx xx xxx of the xxxxxxxx xxxx centers xx the Excel xxxx Call xxxxxx xxxxx xx xxxxxxx an article about xxxx center xxxxxxx conditions, x xxxxxxxx has claimed xxxx the average tenure xx no xxxx than xxx years. xxxxxxxxx and xxxx x xxxxxxxxxx xxxxx xxxxx data xx xxxxxxxxx xx this xxxxx can xx disputed.

xx

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xxxxx xxx date in xxx excel file xxxx Market Value, develop a multiple xxxxxx regression model xxx xxxxxxxxxx xxx xxxxxx xxxxx as x function xx both xxx age xxx xxxx of xxx house. Find x 95% confidence interval xxx xxx mean xxxxxx xxxxx xxx xxxxxx xxxx are xx xxxxx xxx xxx have

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file1.xls preview (134 words)

# xxxx Market Value

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Market xxxxx | |||||||||||

xxxxx Age | xxxxxx Feet | xxxxxx xxxxx | SUMMARY xxxxxx | ||||||||

33 | 1,812 | xxxxxxxxxx | |||||||||

32 | xxxxx | $104,400.00 | xxxxxxxxxx xxxxxxxxxx | House Age | Square xxxx | ||||||

32 | 1,842 | xxxxxxxxxx | xxxxxxxx x | 0.7454947764 | |||||||

xx | xxxxx | xxxxxxxxxx | x xxxxxx | 0.5557624616 | Mean | xxxxxxxxxxxxx | Mean | xxxxxxxxxxxxxxx | |||

xx | 1,836 | $101,900.00 | Adjusted R xxxxxx | 0.5329810494 | Standard xxxxx | xxxxxxxxxxxx | xxxxxxxx Error | 33.986351355 | |||

33 | xxxxx | $108,500.00 | Standard xxxxx | xxxxxxxxxxxxxxx | xxxxxx | 28 | Median | 1666 | |||

xx | xxxxx | $87,600.00 | Observations | xx | Mode | xx | Mode | 1520 | |||

33 | 1,850 | xxxxxxxxxx | Standard Deviation | 2.4286568271 | xxxxxxxx Deviation | xxxxxxxxxxxxxx | |||||

32 | 1,791 | $89,200.00 | ANOVA | ||||||||

33 | 1,666 | $88,400.00 | df | SS | MS | x | Significance F | ||||

32 | 1,852 | xxxxxxxxxxx | xxxxxxxxxx | 2 | 2537650170.692873 | xxxxxxxxxxxxxxxxxx | 24.3954350189 | xxxxxxxxxxxx | |||

32 | 1,620 | xxxxxxxxxx | xxxxxxxx | 39 | xxxxxxxxxxxxxxxxxx | xxxxxxxxxxxxxxxxx | |||||

32 | xxxxx | xxxxxxxxxx | Total | xx | xxxxxxxxxxxxxxxxx | ||||||

xx | 2,372 | $114,000.00 | |||||||||

xx | 2,372 | xxxxxxxxxxx | xxxxxxxxxxxx | xxxxxxxx xxxxx | t xxxx | P-value | Lower xxx | Upper xxx | Lower xxxxx | xxxxx 95.0% | |

xx | xxxxx | $87,500.00 | xxxxxxxxx | 47331.3815356157 | 13884.3466436745 | xxxxxxxxxxxx | 0.0015278315 | xxxxxxxxxxxxxxxx | 75415.1231614667 | 19247.6399097647 | 75415.1231614667 |

xx | xxxxx | $116,100.00 | xxxxx Age | xxxxxxxxxxxxxxx | 607.3128420834 | xxxxxxxxxxxxx | xxxxxxxxxxxx | -2053.5673802352 | 403.244939544 | -2053.5673802352 | xxxxxxxxxxxxx |

32 | xxxxx | xxxxxxxxxx | Square Feet | xxxxxxxxxxxxx | xxxxxxxxxxxx | xxxxxxxxxxxx | xxxxxxxxxxxx | 27.3660702956 | xxxxxxxxxxxxx | xxxxxxxxxxxxx | xxxxxxxxxxxxx |

32 | xxxxx | $86,400.00 | |||||||||

xx | 1,666 | $87,100.00 | xxx xxxxxxxxx regression equation is: xxxxx x | 47331.38 x (-825.16(House Age)) + 40.91(Square xxxxx | |||||||

28 | 1,520 | $83,400.00 | |||||||||

xx | xxxxx | $79,800.00 | |||||||||

xx | 1,588 | $81,500.00 | 95% xxxxxxxxxx interval | xxxx + x standard deviation* x Stat/sqrt(sample) | |||||||

xx | xxxxx | $87,100.00 | |||||||||

28 | 1,484 | xxxxxxxxxx | xxx xxxxxxxxxx interval House xxx | xx x xxxxxxxxxxxxxxxxx xxxx | xxxxx | ||||||

28 | 1,484 | xxxxxxxxxx | |||||||||

xx | 1,520 | xxxxxxxxxx | xx + xxxxxxxxxxxxxxxxx xxxx | 30.51 | |||||||

27 | xxxxx | xxxxxxxxxx | |||||||||

xx | 1,484 | $82,000.00 | |||||||||

xx | 1,468 | xxxxxxxxxx | |||||||||

xx | 1,520 | xxxxxxxxxx | 95% xxxxxxxxxx interval xxxxxx Feet | 30 - xxxxxxxxxxxxxxxxxx (42) | -177.31 | ||||||

27 | xxxxx | xxxxxxxxxx | |||||||||

27 | 1,484 | xxxxxxxxxx | xx + 220.25*(6.10)/sqrt xxxx | 237.31 | |||||||

xx | xxxxx | xxxxxxxxxx | |||||||||

27 | 1,668 | xxxxxxxxxx | |||||||||

xx | xxxxx | xxxxxxxxxx | 95% xxxxxxxxxx interval | mean + x xxxxxxxx xxxxxxxxxx x Stat*sqrt(1 x xxxxxxxxx | |||||||

28 | 1,784 | $91,300.00 | |||||||||

xx | 1,484 | $81,300.00 | 95% xxxxxxxxxxx xxxxxxxx House Age | xxx (2.43)*(1.36)*sqrt(1 + 1/42) | 26.66 | ||||||

27 | 1,520 | xxxxxxxxxxx | |||||||||

xx | xxxxx | xxxxxxxxxx | xxx (2.43)*(1.36)*sqrt(1 + 1/42) | xxxxx | |||||||

27 | 1,684 | $96,700.00 | |||||||||

xx | 1,581 | xxxxxxxxxxx | |||||||||

xxx xxxxxxxxxxx xxxxxxxx xxxxxx xxxx | xxx (220.25)*(6.1)*sqrt(1 x 1/42) | -1329.43 | |||||||||

42 | |||||||||||

30+ (220.25)*(6.1)*sqrt(1 + xxxxx | 1389.43 |

file2.xls preview (104 words)

# Call Center

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxx Data | |||||||

xxxx = 1 Female = x | xxx = 1 No = x | xxx = 1 No x x | |||||

xxxxxx | xxxxxxxx xxx | xxxxx Call Center xxxxxxxxxx | xxxxxxx Degree | Length of Service xxxxxxx | t-Test: Paired xxx xxxxxx xxx xxxxx | ||

0 | xx | 0 | x | xxxx | |||

1 | xx | x | 0 | xxxx | xxxxxxxx Age | Length of Service xxxxxxx | |

0 | xx | 0 | 0 | 2.07 | xxxx | xxxxxxxxxxxxx | 1.8942074364 |

0 | 19 | 0 | 0 | xxxx | xxxxxxxx | xxxxxxxxxxxxx | xxxxxxxxxxxx |

x | xx | x | 0 | 4.42 | xxxxxxxxxxxx | xx | xx |

x | xx | x | 0 | 3.29 | xxxxxxx xxxxxxxxxxx | xxxxxxxxxxxxx | |

x | 19 | 1 | x | 3.05 | Hypothesized Mean Difference | x | |

x | 19 | 1 | 0 | xxxx | xx | xx | |

x | xx | x | x | xxxx | x xxxx | xxxxxxxxxxxxx | |

1 | 19 | x | 0 | 3.12 | xxx<=t) one-tail | x | |

0 | 20 | 0 | x | xxxx | x xxxxxxxx one-tail | 1.6672385492 | |

x | xx | 1 | 0 | 2.15 | xxx<=t) two-tail | 0 | |

x | xx | x | 0 | 4.03 | x Critical xxxxxxxx | xxxxxxxxxxxx | |

1 | 20 | x | 0 | xxxx | |||

x | 20 | 0 | x | 2.47 | |||

0 | 21 | x | 0 | 2.15 | |||

1 | 21 | 0 | 0 | 3.27 | Conclusion: xx can xxx xxxx xxx xxxxxxxx value xx SMALLER xxxx the statistical value. xxx xxx xxxx hypothesis is xxxxx to xx xxxxxxxxx i.e. average tenure is xxxx xxxx 2 xxxxx and claim can be disputed using %5 xxxxx xx significance. | ||

1 | xx | 0 | x | 1.10 | |||

1 | 21 | x | 0 | 1.78 | |||

x | 22 | x | x | 1.94 | |||

x | 22 | 1 | 0 | 2.91 | |||

0 | xx | 1 | x | xxxx | |||

x | xx | 1 | 0 | 2.53 | |||

x | 23 | x | 1 | 1.84 | |||

x | 23 | 1 | 0 | 2.88 | |||

1 | 23 | x | x | 2.20 | |||

x | xx | x | x | xxxx | |||

1 | 24 | 0 | x | xxxx | |||

1 | xx | x | 1 | 1.41 | |||

1 | xx | 0 | x | xxxx | |||

0 | 25 | x | 1 | xxxx | |||

x | xx | x | x | 0.63 | |||

1 | 25 | 0 | x | 1.30 | |||

x | 25 | x | 1 | xxxx | |||

0 | xx | 1 | x | 2.30 | |||

0 | xx | 1 | x | xxxx | |||

0 | xx | 1 | x | 2.13 | |||

1 | 26 | 1 | x | xxxx | |||

x | xx | x | 1 | 2.16 | |||

x | xx | x | 0 | xxxx | |||

0 | 28 | x | 0 | 1.70 | |||

x | xx | x | x | xxxx | |||

1 | 29 | 0 | 1 | xxxx | |||

x | xx | 0 | 1 | 2.15 | |||

x | xx | x | x | xxxx | |||

1 | 30 | x | 1 | xxxx | |||

x | xx | 0 | x | 1.95 | |||

0 | 31 | 0 | x | 1.02 | |||

x | xx | x | 1 | xxxx | |||

1 | xx | x | 0 | xxxx | |||

x | xx | x | 1 | 1.64 | |||

x | 32 | x | x | xxxx | |||

x | 32 | x | x | xxxx | |||

1 | 33 | x | 0 | 1.29 | |||

0 | 34 | x | x | 1.48 | |||

x | xx | x | 1 | 1.31 | |||

x | 34 | 0 | 0 | 1.46 | |||

x | xx | x | 0 | xxxx | |||

x | 36 | 0 | x | 1.16 | |||

0 | xx | x | x | xxxx | |||

x | xx | x | 1 | 1.05 | |||

x | xx | x | 0 | xxxx | |||

x | 40 | x | x | 1.24 | |||

0 | xx | x | 0 | xxxx | |||

1 | xx | 1 | x | xxxx | |||

0 | xx | 1 | 0 | 0.99 | |||

x | 43 | 1 | x | xxxx | |||

x | xx | x | x | xxxx | |||

0 | xx | 1 | x | 0.35 | |||

0 | xx | 1 | 0 | xxxx |

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