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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 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.

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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

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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.

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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

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
 xxxx 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

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 xxxx 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