# 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 have xxxx xxxxxxxxx The director of human xxxxxxxxx for x large bank has compiled xxxx xx about xx xxxxxx employees xx xxx of xxx bank’s call xxxxxxx in xxx Excel xxxx xxxx Center xxxxx In writing an article xxxxx call center x

xxx

xxxx center xxxxxxxxx xxxx high xxxxxxxxx xxx xxxxxxxx xx human resources xxx a large xxxx has compiled xxxx on about xx xxxxxx employees at xxx xx xxx xxxxxxxx call centers xx the xxxxx file Call xxxxxx Data. xx xxxxxxx an article xxxxx call center xxxxxxx xxxxxxxxxxx x reporter has claimed that the xxxxxxx xxxxxx is xx xxxx than two xxxxxx Formulate and test a xxxxxxxxxx using xxxxx xxxx to xxxxxxxxx xx this claim xxx xx disputed.

xx

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xxxxx xxx date xx xxx xxxxx file Home xxxxxx Value, xxxxxxx a xxxxxxxx linear regression model for estimating the xxxxxx xxxxx as x function xx both the xxx xxx xxxx xx the xxxxxx xxxx a 95% xxxxxxxxxx interval for the xxxx market xxxxx for houses xxxx xxx xx years xxx and have

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file1.xls preview (232 words)

# xxxx Market Value

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxHome xxxxxx Value | |||||||||||

House Age | xxxxxx Feet | Market xxxxx | xxxxxxx OUTPUT | ||||||||

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

32 | xxxxx | $104,400.00 | Regression xxxxxxxxxx | House Age | Square Feet | ||||||

xx | xxxxx | $93,300.00 | xxxxxxxx R | 0.7454947764 | |||||||

xx | 1,812 | xxxxxxxxxx | x xxxxxx | xxxxxxxxxxxx | Mean | xxxxxxxxxxxxx | Mean | 1695.2619047619 | |||

32 | 1,836 | $101,900.00 | xxxxxxxx R xxxxxx | 0.5329810494 | xxxxxxxx Error | 0.3747498843 | xxxxxxxx xxxxx | xxxxxxxxxxxx | |||

33 | xxxxx | $108,500.00 | xxxxxxxx Error | xxxxxxxxxxxxxxx | Median | xx | xxxxxx | xxxx | |||

xx | xxxxx | xxxxxxxxxx | Observations | xx | xxxx | xx | Mode | xxxx | |||

33 | xxxxx | $96,000.00 | xxxxxxxx xxxxxxxxx | xxxxxxxxxxxx | xxxxxxxx Deviation | xxxxxxxxxxxxxx | |||||

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

xx | xxxxx | $88,400.00 | xx | SS | MS | F | xxxxxxxxxxxx F | ||||

32 | xxxxx | $100,800.00 | Regression | x | 2537650170.692873 | 1268825085.3464365 | 24.3954350189 | xxxxxxxxxxxx | |||

xx | xxxxx | $96,700.00 | Residual | 39 | xxxxxxxxxxxxxxxxxx | xxxxxxxxxxxxxxxxx | |||||

32 | xxxxx | $87,500.00 | Total | 41 | 4566069761.904759 | ||||||

32 | 2,372 | xxxxxxxxxxx | |||||||||

32 | xxxxx | xxxxxxxxxxx | xxxxxxxxxxxx | Standard Error | t xxxx | xxxxxxx | xxxxx 95% | Upper xxx | Lower xxxxx | xxxxx 95.0% | |

xx | xxxxx | xxxxxxxxxx | xxxxxxxxx | 47331.3815356157 | 13884.3466436745 | 3.4089743472 | xxxxxxxxxxxx | 19247.6399097647 | xxxxxxxxxxxxxxxx | xxxxxxxxxxxxxxxx | 75415.1231614667 |

xx | xxxxx | xxxxxxxxxxx | xxxxx xxx | xxxxxxxxxxxxxxx | 607.3128420834 | xxxxxxxxxxxxx | xxxxxxxxxxxx | -2053.5673802352 | xxxxxxxxxxxxx | xxxxxxxxxxxxxxxx | 403.244939544 |

32 | 1,620 | xxxxxxxxxx | Square xxxx | xxxxxxxxxxxxx | 6.6965239941 | 6.1092991654 | xxxxxxxxxxxx | xxxxxxxxxxxxx | 54.4560666013 | xxxxxxxxxxxxx | 54.4560666013 |

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

xx | 1,666 | xxxxxxxxxx | The xxxxxxxxx xxxxxxxxxx equation xxx Value = | xxxxxxxx x (-825.16(House xxxxx + xxxxxxxxxxxx xxxxx | |||||||

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

27 | 1,484 | xxxxxxxxxx | |||||||||

xx | 1,588 | $81,500.00 | 95% xxxxxxxxxx interval | xxxx + - standard xxxxxxxxxx x xxxxxxxxxxxxxxxxx | |||||||

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

28 | 1,484 | xxxxxxxxxx | 95% confidence interval xxxxx Age | xx - (2.43)(1.36)/sqrt xxxx | 29.49 | ||||||

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

28 | 1,520 | xxxxxxxxxx | 30 + |

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file2.xls preview (299 words)

# Call Center

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxx Data | |||||||

Male x xxxxxxxx x 0 | xxx = xxxx x x | xxx = xxxx = 0 | |||||

Gender | Starting xxx | Prior Call Center Experience | College xxxxxx | Length of Service (years) | t-Test: xxxxxx xxx xxxxxx for Means | ||

x | 18 | x | x | xxxx | |||

x | xx | x | 0 | xxxx | xxxxxxxx Age | xxxxxx of xxxxxxx xxxxxxx | |

x | xx | x | 0 | 2.07 | xxxx | 27.8428571429 | 1.8942074364 |

x | 19 | 0 | x | xxxx | xxxxxxxx | xxxxxxxxxxxxx | 1.2059960224 |

0 | xx | 0 | 0 | 4.42 | Observations | xx | xx |

0 | xx | x | 0 | xxxx | xxxxxxx Correlation | -0.6078345192 | |

0 | 19 | 1 | x | 3.05 | Hypothesized Mean xxxxxxxxxx | x | |

x | xx | 1 | 0 | 0.49 | df | 69 | |

1 | 19 | x | 0 | 0.61 | x Stat | 24.9032490612 | |

x | xx | 0 | x | 3.12 | P(T<=t) xxxxxxxx | 0 | |

0 | 20 | 0 | 0 | 2.95 | x xxxxxxxx one-tail | xxxxxxxxxxxx | |

x | 20 | 1 | 0 | 2.15 | xxx<xxx two-tail | x | |

0 | xx | x | x | 4.03 | x xxxxxxxx xxxxxxxx | 1.9949453901 | |

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

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

0 | xx | 0 | 0 | 2.15 | |||

1 | xx | 0 | 0 | 3.27 | Conclusion: xx can xxx xxxx xxx critical value is xxxxxxx xxxx the xxxxxxxxxxx value. xxx xxx xxxx hypothesis is xxxxx to xx xxxxxxxxx i.e. xxxxxxx xxxxxx xx xxxx xxxx 2 years and xxxxx can be xxxxxxxx using xx xxxxx of |

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