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Study Resources (Business Management)

  53. A bank is interested in identifying different attributes of its customers and below is the sample data of 150 customers. For Gender, 0 represents Male and 1 represents Female. For Personal loan, 0 represents a customer who has not taken personal loan and 1 represents a customer who has taken.
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  46. A company asked one of their analysis team to analyze and create models that help decide whether they should manufacture a particular product or outsource its production. The different components are given below. Fixed Cost, FC = $25,000 Material Cost per Unit, MC = $2.15 Labor Cost per Unit, LC = $2.00Outsourcing Cost.
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  31. The ____________ button, located in the Formula Auditing group, creates arrows pointing to the selected cell from cells that are part of the formula in that cell.  a. Trace Dependentsb. Trace Precedents  c. Error Checkingd. Watch Window 32. Arrows pointing from the selected cell to cells that depend on the selected cell are generated by using the ___________.
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63. A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:  In XLMiner’s Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan.
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    52. The selling price of each product sold in a furnishing showroom, and the number of units of each of these product sold during a period of one month are given below. The rental cost of the showroom is $225 and the other costs incurred are included in the cost/unit.  Product code Price/Unit($) Cost/Unit($) Units AD12 232 162.4 12 FD23 334 233.8 24 BD34 342 239.4 5 AG56 267 186.9 11 ET76 345 241.5 15 FA56 235 164.5 23 DE78 546 382.2 34 BF32 245 171.5 22   Display.
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  42. The Gatson manufacturing company has estimated the following components for a new product. Fixed cost = $50,000Material cost per unit = $2.15Labor cost per unit = $2.00Revenue per unit = $7.50 a. Construct a spreadsheet model and then construct a one-way data table with production volume as the column input and profit.
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  21. The SUM function in Excel  a. adds up all the numbers in the cells diagonally.  b. adds up only positive numbers in a range of cells.  c. adds up all the numbers in a range of cells.  d. adds up the cells specified by a given condition or criteria. 22. The _________ function pairs each element of the first array.
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  58. The following table shows the average monthly distance traveled (in billion miles) by vehicles on urban highways for five different years. Urban Highways - Average Monthly Distance Traveled by Vehicles (Billion Miles) Years Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Year 1 4.22 5.32 5.21 5.12 4.92 4.49 4.55 4.49 4.44 4.39 4.37 4.35 Year 2 4.31 5.44 5.34 5.24 4.98 4.59 4.68 4.65 4.61 4.68 4.74 4.79 Year 3 4.38 5.51 5.41 5.36 4.98 4.63 4.71 4.78 4.82 4.88 4.85 4.89 Year 4 4.45 5.59 5.5 5.41 5.01 4.72 4.78 4.79 4.82 4.92 5.06 5.11 Year 5 4.51 5.65 5.62 5.49 5.12 4.8 4.88 4.82 4.95 5.12 5.22 5.44   ? a. Construct a time series plot. What type of pattern exists in the data? b. Use.
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  48. To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below: Land Value ($)BuildingValue ($)AcresBathsToiletsFireplacesBedroomsAge              Sale Price ($) 18100925000.5111453.9114885 236001527000.22211319.7180895 259001343000.3211315.9162038 221001296000.23221441154496 239001687000.32211439.9196973 224001183000.25211341.8145075 241001233000.26111470.9151480 263001338000.26211337.8164762 249001394000.24211433166528 13600872000.17110334.7105762 361002104000.6212252.9250170 195001013000.16111267.8125082 388002247000.44211421.7265066 235001390000.22110310.8166697 263001642000.3521033.9194881 219001224000.17211315.7146818 234001496000.22211315.7176048 150001022000.12110397.8119584 150001022000.12110397.7121759 9200220000.171104120.934947 9200220000.171104120.935214 5600480000.121103103.957142 9000588000.2411038872192 210001096000.21210336.7133848 235001659000.1521145.7194079 360002625000.2231142.9300407 237001149000.22110437.7141700 220001027000.2110348.9128866 19900958000.23211278.9119189 221001163000.18110330.8141018 246001655000.29111443193661 215001134000.17111344.9137308 15000811000.16110262.999817 157001292000.23210346.7148909 14200816000.15110357.9100701 10700497000.15110299.865082 16600727000.18111491.892614 255001107000.21111348137889 15100743000.23111371.891180 7400555000.15110296.864119 285001294000.25110449.9160139 25100839000.2110345.8113043 501001646000.23210344217684 833002760000.61311247.9360936 1245005523001.0541245.7679795 470002144000.22212492.9264115 646001850000.58211491254075 339001388000.22111497.9173987 411001563000.18111376200251 29100964000.28110157.8130214 564002564000.4111356.8316874 454002192000.21111379.8267672 23800921000.15111491.9119769 528001728000.27212274.8229499 25100992000.19110336.7128456 272001526000.18211316.7181102 281001029000.18111375.8132977 28800988000.19120353.9131411 334001039000.45211484.9139697 20700956000.14111389.8120046 256001019000.2110257.8131026 258001107000.18110351.9141202 293001477000.2111490.9181575 260001160000.18111344144513 25900735000.16110281.8100953 328001250000.35111368.7160546 311001668000.2112257.7199970 258001053000.17110358.8134647 27200948000.17110342.9124311 250001059000.16111382133543 292001175000.2111353.8151392 30000933000.26110255.7124476 204001120000.13211380.9136599 23600834000.16110257.7110399 16200858000.1111267105027 293001239000.22111444.8157819 27000978000.18110346.8129675 25600863000.16110361.7115952 462002205000.57211450.8268552 229001600000.15311320.7187870 271001052000.21110351.8135549 307001071000.3110370142738 291001024000.23210258135284 347001504000.28112368.9189790 20000804000.24110366.9105302 357001594000.2821141.7196936 351001615000.2521138.8201349 337001625000.2121148.8198580 337001625000.2121148.8200228 364001761000.2921148.9215634 332001223000.221034.9157208 392001692000.3621135.9212662 331001801000.221145.8217543 16000984000.19110449.9118491 24900638000.45211283.991539 220001213000.27120434.9147802 200001076000.23111336.7131948 339002308000.27211310268444 221001538000.3111346.8180464 228001111000.23110352137326 247001178000.32110348.7145115 387001187000.81111347.8159644 258001080000.26210253.3135049 317001405000.34111340.6174475 822001717001.23211356.4257467 195001476000.53211228.2169311 244001320000.25211314.2157570 225001198000.18211315.5143676 259001171000.29210317.7146960 22700950000.25110355.6121175 21200567000.23110296.681869 340001638000.26211415.2199361 189001180000.17110345.5139981 339001516000.26211325.3186637 238001335000.21211313.6161123 239001190000.21211314.3146054 185001105000.19212432.2130575 363001225000.61112356.2162270 473002988000.36311431.4348138 366002387000.28212325.5278839 Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price.
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  43. A bank is interested in identifying different attributes of its customers and below is the sample data of 150 customers. In the data table for the dummy variable Gender, 0 represents Male and 1 represents Female.  And for the dummy variable Personal loan, 0 represents a customer who has not taken.
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  41. The Gatson manufacturing company has estimated the following components for a new product. Fixed cost = $50,000Material cost per unit = $2.15Labor cost per unit = $2.00Revenue per unit = $7.50? a.Build an influence diagram that illustrates how to calculate profit. b.Using mathematical notation, construct a mathematical model for calculating profit. c.Implement your model.
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  54. Consider the following time series.   t yt 1 1,234 2 1,201 3 1,103 4 987 5 945 6 891 7 817 8 734   a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. c. What is the forecast for t = 9?   .
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  48.Given below is a sample list of 20 products in a grocery store with the product code, the price, and the associated discount rates. Product code Price ($) Discount (%) A003 4.00 5 A345 2.70 0 B985 4.50 5 C765 1.50 0 F302 3.00 5 B453 6.80 10 A109 9.50 10 F432 4.80 5 D234 5.40 10 B432 2.60 0 D765 6.90 10 A406 2.60 5 D203 5.40 10 F405 3.60 0 C432 5.20 5 C106 3.20 5 D324 1.30 0 F456 5.20 10 A156 2.50 5 B654 1.10 0   ? a.Display your use of the VLOOKUP function and find the price of the products A109, F432, B985, D203, C432, B654, and A345. b.Display your use.
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  41. A bank is interested in identifying different attributes of its customers and below is the sample data of 150 customers. For Gender, 0 represents Male and 1 represents Female. For Personal loan, 0 represents a customer who has not taken personal loan and 1 represents a customer who has taken.
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  46. A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below: ? Average BalanceAgeGenderMarriedDivorcedFamily sizeLoan Default 1,222.33610010 6,291.04101130 1,051.05211041 1,118.33610020 1,176.83501020 1,052.04611030 1,314.63711010 439.73411151 1,232.75601110 1,855.45101020 322.44401051 1,570.73911030 2,729.05111010 1,397.84111020 1,464.14711120 40.34611031 1,296.43201020 2,142.75801040 2,756.33211020 1,451.14911130 1,003.94811020 1,245.74011061 3,011.14201010 1,222.33011020 2,225.95101050 2,708.24011020 2,341.63011110 1,817.74210020 1,417.35211030 4,291.65911040 1,310.73411020 1,144.03811030 1,088.44701020 1,341.93110021 1,269.15611110 1,435.54611020 113.75811031 4,646.53910030 1,003.93811020 1,773.54601020 3,349.14511130 647.05411131 3,901.65811040 1,603.23811020 1,308.13901020 4,061.53500010 2,283.15711130 1,023.04011020 1,083.25201041 1,158.62610010 1,052.03011020 592.23511031 6,834.44101030 1,505.75711030 1,170.05601161 1,509.64311040 1,061.05201030 517.26001051 1,661.74611120 1,279.55701010 1,656.54601120 1,319.84911020 1,227.53801010 1,748.85101030 1,060.03811040 1,119.64411020 1,135.05901130 2,777.15010020 1,535.63401010 352.05901051 1,605.84211020 5,737.24801020 3,354.34500010 10,096.15711020 9,164.05301030 6,796.74811040 2,108.95711051 265.25201031 1097.05511030 1,041.05801020 1,224.95101010 1,557.74411130 3,202.25701020 1,173.03911030 1,794.34101011 2,423.54701120 171.85201031 1,2157.95011130 4,107.05211020 887.94700021 1,165.14401030 643.55001011 1,529.14511010 2,142.75901010 1,035.05401020 1,003.94811030 1,509.64311010 1,118.33710020 1,124.84611021 1,891.84911011 6,796.74811010 1,709.85401020 1,011.73001030 1,270.45201020 1,663.03911030 1,648.73311020 1,887.94700020 1,244.43711030 2,465.13601130 6,086.93811030 1,262.65711030 1,513.52701010 1,170.32901030 1,557.74511120 2,454.75711010 710.84101011 3,711.85601020 1,748.85201021 1,248.35911120 1,002.65101020 1,130.04101010 1,040.34701020 1,595.45011030 1,144.03411010 1,582.45211030 1,049.05300010 1,577.23911021 561.04000031 3,349.14601120 1,704.64511030 1,245.73501020 16,191.85401030 2,185.63701030 1,167.74011010 1,535.63411020 1,319.85001010 1,145.64911110 1,304.25301030 1,851.54900030 2,099.84000010 1,152.03401010 1,219.74901020 1,235.35011030 1,811.23011010 732.52800021 5,630.65601030 2,420.96701030 2,454.75801030 1,557.74601120 4,017.37211020 4,017.37211010 1,351.04101120 1,507.02900030 1,050.76011010 1,657.83601110 1,115.03700010 245.95901021 1,058.55601030 1,377.06011010 1,079.33401010 1,456.34101030 2,063.42900020 1,106.62600020 1,119.66001110 2,496.35311120 1,578.54201010 1,284.74711030 1,409.55311130 1,085.84401120 1,083.03811030 1,556.44911030 1,080.64101030 1,457.63301020 1,478.44711030 1,690.35101010 1,458.95611030 1,465.44601030 1,002.65110010 1,728.03401010 1,015.63110020 1,163.84801010 1,299.05901021 1,400.43811110 1,005.22711030 1,341.94010020 1,032.53900010 1,236.64801010 1,087.13401110 1,170.35501010 1,237.95501010 1,296.45101120 1,182.03710010 1,133.03900010 1,629.25201030 1,830.73601030 1,137.83611030 2,011.45601130 170.33601111 1,135.23911031 195.02911011 ? ? In XLMiner’s Partition with Oversampling procedure, partition the data so there is 50 percent.
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  51. A bank is interested in identifying different attributes of its customers and below is the sample data of 150 customers. For Gender, 0 represents Male and 1 represents Female. For Personal loan, 0 represents a customer who has not taken personal loan and 1 represents a customer who has taken.
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  53. The monthly sales (in hundreds of dollars) of a company are listed below. Month Sales ($100s) January 12,354 February 13,657 March 14,536 April 13,478 May 16,590 June 19,790 July 17,987 August 18,657 September 19,765 October 18,678 November 20,678 December 23,675   a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. c. What is the.
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    60. The monthly market shares of General Electric Company for 12 consecutive months follow. Develop three-month and four-month moving averages for this time series. Does the three-month or the four-month moving average provide the better forecasts based on MSE? Explain your reasoning. ?? Month Market Shares 1  23.39 2  23.56 3  23.02 4  23.03 5  23.60 6  23.37 7  23.21 8  23.40 9  23.31 10  23.94 11  23.39 12  23.50 ? ?       .
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  50. The average cost/unit for the production of a particular component at a manufacturing plant varies with the number of units produced in each batch. The data are given below. ?  Number of units produced  Cost/unit  0–49  $37.72  50–100  $25.02 ? Suppose the selling price of each unit is $35.Use a two-way data table to show how the profit changes.
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  55. The yearly sales (in millions of dollars) of an automobile manufacturing company during the period 2000-2011 are given below. Year Sales ($millions) y 2000 470 2001 485 2002 499 2003 515 2004 532 2005 532 2006 556 2007 576 2008 583 2009 587 2010 601 2011 605 a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for.
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  44. A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below: Average BalanceAgeGenderMarriedDivorcedFamily sizeLoan Default 1,222.33610010 6,291.04101130 1,051.05211041 1,118.33610020 1,176.83501020 1,052.04611030 1,314.63711010 439.73411151 1,232.75601110 1,855.45101020 322.44401051 1,570.73911030 2,729.05111010 1,397.84111020 1,464.14711120 40.34611031 1,296.43201020 2,142.75801040 2,756.33211020 1,451.14911130 1,003.94811020 1,245.74011061 3,011.14201010 1,222.33011020 2,225.95101050 2,708.24011020 2,341.63011110 1,817.74210020 1,417.35211030 4,291.65911040 1,310.73411020 1,144.03811030 1,088.44701020 1,341.93110021 1,269.15611110 1,435.54611020 113.75811031 4,646.53910030 1,003.93811020 1,773.54601020 3,349.14511130 647.05411131 3,901.65811040 1,603.23811020 1,308.13901020 4,061.53500010 2,283.15711130 1,023.04011020 1,083.25201041 1,158.62610010 1,052.03011020 592.23511031 6,834.44101030 1,505.75711030 1,170.05601161 1,509.64311040 1,061.05201030 517.26001051 1,661.74611120 1,279.55701010 1,656.54601120 1,319.84911020 1,227.53801010 1,748.85101030 1,060.03811040 1,119.64411020 1,135.05901130 2,777.15010020 1,535.63401010 352.05901051 1,605.84211020 5,737.24801020 3,354.34500010 10,096.15711020 9,164.05301030 6,796.74811040 2,108.95711051 265.25201031 1097.05511030 1,041.05801020 1,224.95101010 1,557.74411130 3,202.25701020 1,173.03911030 1,794.34101011 2,423.54701120 171.85201031 1,2157.95011130 4,107.05211020 887.94700021 1,165.14401030 643.55001011 1,529.14511010 2,142.75901010 1,035.05401020 1,003.94811030 1,509.64311010 1,118.33710020 1,124.84611021 1,891.84911011 6,796.74811010 1,709.85401020 1,011.73001030 1,270.45201020 1,663.03911030 1,648.73311020 1,887.94700020 1,244.43711030 2,465.13601130 6,086.93811030 1,262.65711030 1,513.52701010 1,170.32901030 1,557.74511120 2,454.75711010 710.84101011 3,711.85601020 1,748.85201021 1,248.35911120 1,002.65101020 1,130.04101010 1,040.34701020 1,595.45011030 1,144.03411010 1,582.45211030 1,049.05300010 1,577.23911021 561.04000031 3,349.14601120 1,704.64511030 1,245.73501020 16,191.85401030 2,185.63701030 1,167.74011010 1,535.63411020 1,319.85001010 1,145.64911110 1,304.25301030 1,851.54900030 2,099.84000010 1,152.03401010 1,219.74901020 1,235.35011030 1,811.23011010 732.52800021 5,630.65601030 2,420.96701030 2,454.75801030 1,557.74601120 4,017.37211020 4,017.37211010 1,351.04101120 1,507.02900030 1,050.76011010 1,657.83601110 1,115.03700010 245.95901021 1,058.55601030 1,377.06011010 1,079.33401010 1,456.34101030 2,063.42900020 1,106.62600020 1,119.66001110 2,496.35311120 1,578.54201010 1,284.74711030 1,409.55311130 1,085.84401120 1,083.03811030 1,556.44911030 1,080.64101030 1,457.63301020 1,478.44711030 1,690.35101010 1,458.95611030 1,465.44601030 1,002.65110010 1,728.03401010 1,015.63110020 1,163.84801010 1,299.05901021 1,400.43811110 1,005.22711030 1,341.94010020 1,032.53900010 1,236.64801010 1,087.13401110 1,170.35501010 1,237.95501010 1,296.45101120 1,182.03710010 1,133.03900010 1,629.25201030 1,830.73601030 1,137.83611030 2,011.45601130 170.33601111 1,135.23911031 195.02911011 ? In XLMiner’s Partition with Oversampling procedure, partition the data so there is 50 percent.
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  50. A research team wanted to assess the relationship between age, systolic blood pressure, smoking, and risk of stroke. A sample of 150 patients who had a stroke is selected and the data collected are given below. Here, for the variable Smoker, 1 represents smokers and 0 represents nonsmokers. ? AgeBlood PressureSmokerRisk 86177145 76189165 56155016 7898145 6714507 77209134 60199167 66166154 80125167 62117156 5983012 72134132 70145145 73188026 67163167 6487187 67123123 64204023 62145134 75213156 59196176 64124134 71145154 78120143 65156156 67167013 69143176 67187154 75193134 728506 71152123 7089145 68132134 63165168 70221023 75173187 66145167 64132156 71167145 58155026 70134165 6392176 65143028 63143145 6687134 65154139 80135172 67156024 60187022 60125134 62176159 66187167 57152154 68154026 66134134 72165156 70173027 70132176 61167145 60165134 82119176 70184012 63167156 75132189 60176176 7278156 7293012 65154154 7277156 60134178 64165138 65187159 7023406 75123147 7599021 62103128 67114139 69156013 73160147 68107156 67142143 61165126 61141145 71128187 60138145 67117134 6714708 73135156 67154176 71174143 67126156 63142152 73167023 72159145 70133152 67147145 69157032 69175167 62125165 74150149 71124158 75176012 63173152 71172149 66112164 66130169 71125161 71104134 70125158 66102164 68176158 66167154 63156147 75187034 61113156 70142137 72105157 63140112 61137134 71142167 66105154 68149136 6013405 64128143 65111147 70106126 67101137 74170148 61130159 72164162 65123168 6221109 7598156 6567153 62145145 67132149 67145139 62132146 67154156 74167163 61156012 75187159 75193139 61132147 65156152 74123157 75156034 70167012 64165148 76123141 ? Partition the.
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  55. ?A bank is interested in identifying different attributes of its customers and below is the sample data of 150 customers. For Gender, 0 represents Male and 1 represents Female. For Personal loan, 0 represents a customer who has not taken personal loan and 1 represents a customer who has taken.
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1. Which of the following is true of spreadsheet packages used in business analytics?  a. They are more expensive than specialized packages.  b. They require substantial user training.  c. They come preloaded on computers.  d. They do not have specialized functions to perform detailed analyses. 2. Spreadsheet models are referred to as what-if models because they  a. are mathematical and logic-based models.  b. allow easy.
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    49. The following times series shows the demand for a particular product over the past 10 months. Month Value 1 324 2 311 3 303 4 314 5 323 6 313 7 302 8 315 9 312 10 326   a. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for month 11. b. Compare the three-month moving average forecast with the exponential smoothing forecast using α =.
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  45. A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below: ? Average BalanceAgeGenderMarriedDivorcedFamily sizeLoan Default 1,222.33610010 6,291.04101130 1,051.05211041 1,118.33610020 1,176.83501020 1,052.04611030 1,314.63711010 439.73411151 1,232.75601110 1,855.45101020 322.44401051 1,570.73911030 2,729.05111010 1,397.84111020 1,464.14711120 40.34611031 1,296.43201020 2,142.75801040 2,756.33211020 1,451.14911130 1,003.94811020 1,245.74011061 3,011.14201010 1,222.33011020 2,225.95101050 2,708.24011020 2,341.63011110 1,817.74210020 1,417.35211030 4,291.65911040 1,310.73411020 1,144.03811030 1,088.44701020 1,341.93110021 1,269.15611110 1,435.54611020 113.75811031 4,646.53910030 1,003.93811020 1,773.54601020 3,349.14511130 647.05411131 3,901.65811040 1,603.23811020 1,308.13901020 4,061.53500010 2,283.15711130 1,023.04011020 1,083.25201041 1,158.62610010 1,052.03011020 592.23511031 6,834.44101030 1,505.75711030 1,170.05601161 1,509.64311040 1,061.05201030 517.26001051 1,661.74611120 1,279.55701010 1,656.54601120 1,319.84911020 1,227.53801010 1,748.85101030 1,060.03811040 1,119.64411020 1,135.05901130 2,777.15010020 1,535.63401010 352.05901051 1,605.84211020 5,737.24801020 3,354.34500010 10,096.15711020 9,164.05301030 6,796.74811040 2,108.95711051 265.25201031 1097.05511030 1,041.05801020 1,224.95101010 1,557.74411130 3,202.25701020 1,173.03911030 1,794.34101011 2,423.54701120 171.85201031 1,2157.95011130 4,107.05211020 887.94700021 1,165.14401030 643.55001011 1,529.14511010 2,142.75901010 1,035.05401020 1,003.94811030 1,509.64311010 1,118.33710020 1,124.84611021 1,891.84911011 6,796.74811010 1,709.85401020 1,011.73001030 1,270.45201020 1,663.03911030 1,648.73311020 1,887.94700020 1,244.43711030 2,465.13601130 6,086.93811030 1,262.65711030 1,513.52701010 1,170.32901030 1,557.74511120 2,454.75711010 710.84101011 3,711.85601020 1,748.85201021 1,248.35911120 1,002.65101020 1,130.04101010 1,040.34701020 1,595.45011030 1,144.03411010 1,582.45211030 1,049.05300010 1,577.23911021 561.04000031 3,349.14601120 1,704.64511030 1,245.73501020 16,191.85401030 2,185.63701030 1,167.74011010 1,535.63411020 1,319.85001010 1,145.64911110 1,304.25301030 1,851.54900030 2,099.84000010 1,152.03401010 1,219.74901020 1,235.35011030 1,811.23011010 732.52800021 5,630.65601030 2,420.96701030 2,454.75801030 1,557.74601120 4,017.37211020 4,017.37211010 1,351.04101120 1,507.02900030 1,050.76011010 1,657.83601110 1,115.03700010 245.95901021 1,058.55601030 1,377.06011010 1,079.33401010 1,456.34101030 2,063.42900020 1,106.62600020 1,119.66001110 2,496.35311120 1,578.54201010 1,284.74711030 1,409.55311130 1,085.84401120 1,083.03811030 1,556.44911030 1,080.64101030 1,457.63301020 1,478.44711030 1,690.35101010 1,458.95611030 1,465.44601030 1,002.65110010 1,728.03401010 1,015.63110020 1,163.84801010 1,299.05901021 1,400.43811110 1,005.22711030 1,341.94010020 1,032.53900010 1,236.64801010 1,087.13401110 1,170.35501010 1,237.95501010 1,296.45101120 1,182.03710010 1,133.03900010 1,629.25201030 1,830.73601030 1,137.83611030 2,011.45601130 170.33601111 1,135.23911031 195.02911011 ? ? In XLMiner’s Partition with Oversampling procedure, partition the data so there is 50 percent.
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  52. A bank is interested in identifying different attributes of its customers and below is the sample data of 150 customers. For Gender, 0 represents Male and 1 represents Female. For Personal loan, 0 represents a customer who has not taken personal loan and 1 represents a customer who has taken.
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  47. To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below: Land Value ($)BuildingValue ($)AcresBathsToiletsFireplacesBedroomsAge              Sale Price ($) 18100925000.5111453.9114885 236001527000.22211319.7180895 259001343000.3211315.9162038 221001296000.23221441154496 239001687000.32211439.9196973 224001183000.25211341.8145075 241001233000.26111470.9151480 263001338000.26211337.8164762 249001394000.24211433166528 13600872000.17110334.7105762 361002104000.6212252.9250170 195001013000.16111267.8125082 388002247000.44211421.7265066 235001390000.22110310.8166697 263001642000.3521033.9194881 219001224000.17211315.7146818 234001496000.22211315.7176048 150001022000.12110397.8119584 150001022000.12110397.7121759 9200220000.171104120.934947 9200220000.171104120.935214 5600480000.121103103.957142 9000588000.2411038872192 210001096000.21210336.7133848 235001659000.1521145.7194079 360002625000.2231142.9300407 237001149000.22110437.7141700 220001027000.2110348.9128866 19900958000.23211278.9119189 221001163000.18110330.8141018 246001655000.29111443193661 215001134000.17111344.9137308 15000811000.16110262.999817 157001292000.23210346.7148909 14200816000.15110357.9100701 10700497000.15110299.865082 16600727000.18111491.892614 255001107000.21111348137889 15100743000.23111371.891180 7400555000.15110296.864119 285001294000.25110449.9160139 25100839000.2110345.8113043 501001646000.23210344217684 833002760000.61311247.9360936 1245005523001.0541245.7679795 470002144000.22212492.9264115 646001850000.58211491254075 339001388000.22111497.9173987 411001563000.18111376200251 29100964000.28110157.8130214 564002564000.4111356.8316874 454002192000.21111379.8267672 23800921000.15111491.9119769 528001728000.27212274.8229499 25100992000.19110336.7128456 272001526000.18211316.7181102 281001029000.18111375.8132977 28800988000.19120353.9131411 334001039000.45211484.9139697 20700956000.14111389.8120046 256001019000.2110257.8131026 258001107000.18110351.9141202 293001477000.2111490.9181575 260001160000.18111344144513 25900735000.16110281.8100953 328001250000.35111368.7160546 311001668000.2112257.7199970 258001053000.17110358.8134647 27200948000.17110342.9124311 250001059000.16111382133543 292001175000.2111353.8151392 30000933000.26110255.7124476 204001120000.13211380.9136599 23600834000.16110257.7110399 16200858000.1111267105027 293001239000.22111444.8157819 27000978000.18110346.8129675 25600863000.16110361.7115952 462002205000.57211450.8268552 229001600000.15311320.7187870 271001052000.21110351.8135549 307001071000.3110370142738 291001024000.23210258135284 347001504000.28112368.9189790 20000804000.24110366.9105302 357001594000.2821141.7196936 351001615000.2521138.8201349 337001625000.2121148.8198580 337001625000.2121148.8200228 364001761000.2921148.9215634 332001223000.221034.9157208 392001692000.3621135.9212662 331001801000.221145.8217543 16000984000.19110449.9118491 24900638000.45211283.991539 220001213000.27120434.9147802 200001076000.23111336.7131948 339002308000.27211310268444 221001538000.3111346.8180464 228001111000.23110352137326 247001178000.32110348.7145115 387001187000.81111347.8159644 258001080000.26210253.3135049 317001405000.34111340.6174475 822001717001.23211356.4257467 195001476000.53211228.2169311 244001320000.25211314.2157570 225001198000.18211315.5143676 259001171000.29210317.7146960 22700950000.25110355.6121175 21200567000.23110296.681869 340001638000.26211415.2199361 189001180000.17110345.5139981 339001516000.26211325.3186637 238001335000.21211313.6161123 239001190000.21211314.3146054 185001105000.19212432.2130575 363001225000.61112356.2162270 473002988000.36311431.4348138 366002387000.28212325.5278839 Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price.
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      52. The below time series gives the indices of Industrial Production in U.S for 10 consecutive years. Year IP 1 79.62 2 86.54 3 88.14 4 89.23 5 93.45 6 97.4 7 99.34 8 96.98 9 100.22 10 103.56   a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. c. What.
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  48. The following times series shows the demand for a particular product over the past 10 months. Month Demand 1 324 2 311 3 303 4 314 5 323 6 313 7 302 8 315 9 312 10 326   a. Construct a time series plot. What type of pattern exists in the data? b. Develop a three-month moving average for this time series. Compute MSE and a forecast for month 11.         .
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  59. The monthly market shares of General Electric Company for 12 consecutive months follow. Construct a time series plot. What type of pattern exists in the data? ?? Month Market Shares 1  23.39 2  23.56 3  23.02 4  23.03 5  23.60 6  23.37 7  23.21 8  23.40 9  23.31 10  23.94 11  23.39 12  23.50 ? ?         .
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  43. The Gatson manufacturing company has estimated the following components for a new product. Fixed cost = $50,000Material cost per unit = $2.15Labor cost per unit = $2.00 Revenue per unit = $7.50    Construct a spreadsheet model and then use use a two-way data table to show how the profit changes as a.
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  45. A company asked one of their analysis team to analyze and create models that help decide whether they should manufacture a particular product or outsource its production. The different components are given below. Fixed Cost, FC = $25,000 Material Cost per Unit, MC = $2.15 Labor Cost per Unit, LC = $2.00Outsourcing Cost.
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  31. One minus the overall error rate is often referred to as the _____ of the model.  a. sensitivityb. accuracy  c. specificityd. cutoff value 32. An observation classified as part of a group with a characteristic when it actually does not have the characteristic is termed as a(n)  a. false negative.  b. false positive.  c. residual.  d. outlier. 33. Separate error rates with respect to the false negative and.
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  49. The average cost/unit for the production of a particular component at a manufacturing plant varies with the number of units produced in each batch. The data are given below. ?  Number of Units Produced  Cost/Unit  0–49  $37.72  50–100  $25.02 ? Suppose the selling price of each unit is $35. a. Build a model to calculate the profit of the manufacturing.
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    51. The following data shows the quarterly profit (in thousands of dollars) made by a particular company in the past 3 years. Year Quarter Profit ($1000s) 1 1 45 1 2 51 1 3 72 1 4 50 2 1 49 2 2 45 2 3 79 2 4 54 3 1 42 3 2 58 3 3 70 3 4 56   a. Use α = 0.3 to compute the exponential smoothing values for the time series. Compute MSE and the forecast of profit (in $1000s) for the next quarter. b. Compare the.
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  44. A company asked one of their analysis team to analyze and create models that help decide whether they should manufacture a particular product or outsource its production. The different components are given below. Fixed Cost, FC = $25,000 Material Cost per Unit, MC = $2.15 Labor Cost per Unit, LC = $2.00Outsourcing Cost.
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    50. The following data shows the quarterly profit (in thousands of dollars) made by a particular company in the past 3 years. Year Quarter Profit ($1000s) 1 1 45 1 2 51 1 3 72 1 4 50 2 1 49 2 2 45 2 3 79 2 4 54 3 1 42 3 2 58 3 3 70 3 4 56   a. Construct a time series plot. What type of pattern exists in the data? b. Develop a three-period moving average for this time series. Compute MSE and a forecast of.
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  56. Consider the following time series data: t yt 1 0.345 2 0.366 3 0.398 4 0.356 5 0.456 6 0.478 7 0.543 8 0.596 9 0.634 10 0.698 a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. c. What is the forecast for t = 11?   .
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  57. Consider the following quarterly time series. ? Quarter Year 1 Year 2 Year 3 1 923 1,112 1,243 2 1,056 1,156 1,301 3 1,124 1,124 1,254 4 992 1,078 1,198 ? a. Construct a time series plot. What type of pattern exists in the data?b. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if quarter 1,.
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  21. A(n)  __________ matrix displays a model’s correct and incorrect classification.  a. cumulative lift  b. classification confusion  c. decile-wise lift chart  d. ROC curve 22. _______ attempts to classify a categorical outcome as a linear function of explanatory variables.  a. Linear regression  b. Logistic regression  c. Classification model  d. Supervised learning 23. How many Class 1's are correctly classified as Class 1 in the Table below?  Classification Confusion Matrix    Predicted Class Actual Class 1 0 1  221 100 0  30 3,000 ?  a. 221  b. 100  c. 30  d. 3,000 24. How many.
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  11. Which of the following would be a likely mathematical expression for Total Variable Cost? ?   ?  a. Total Variable Cost = Production Volume × Revenue per Unit  b. Total Variable Cost = Material Cost per Unit × Labor Cost per Unit  c. Total Variable Cost = Total Cost – (Material Cost per Unit + Labor Cost per Unit)  d. Total.
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62. A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:  In XLMiner’s Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan.
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1. The set of recorded values of variables associated with a single entity is a(n)  a. observation.  b. data point.  c. classification.  d. location. 2. A(n)_______________  is often displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.  a. record  b. data point  c. classification  d. location 3. A characteristic or quantity of interest that can take on different values is a(n)   a. variable.  b. observation.  c. record.  d. quality. 4. Estimation.
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  49. A research team wanted to assess the relationship between age, systolic blood pressure, smoking, and risk of stroke. A sample of 150 patients who had a stroke is selected and the data collected are given below. Here, for the variable Smoker, 1 represents smokers and 0 represents nonsmokers. ? AgeBlood PressureSmokerRisk 86177145 76189165 56155016 7898145 6714507 77209134 60199167 66166154 80125167 62117156 5983012 72134132 70145145 73188026 67163167 6487187 67123123 64204023 62145134 75213156 59196176 64124134 71145154 78120143 65156156 67167013 69143176 67187154 75193134 728506 71152123 7089145 68132134 63165168 70221023 75173187 66145167 64132156 71167145 58155026 70134165 6392176 65143028 63143145 6687134 65154139 80135172 67156024 60187022 60125134 62176159 66187167 57152154 68154026 66134134 72165156 70173027 70132176 61167145 60165134 82119176 70184012 63167156 75132189 60176176 7278156 7293012 65154154 7277156 60134178 64165138 65187159 7023406 75123147 7599021 62103128 67114139 69156013 73160147 68107156 67142143 61165126 61141145 71128187 60138145 67117134 6714708 73135156 67154176 71174143 67126156 63142152 73167023 72159145 70133152 67147145 69157032 69175167 62125165 74150149 71124158 75176012 63173152 71172149 66112164 66130169 71125161 71104134 70125158 66102164 68176158 66167154 63156147 75187034 61113156 70142137 72105157 63140112 61137134 71142167 66105154 68149136 6013405 64128143 65111147 70106126 67101137 74170148 61130159 72164162 65123168 6221109 7598156 6567153 62145145 67132149 67145139 62132146 67154156 74167163 61156012 75187159 75193139 61132147 65156152 74123157 75156034 70167012 64165148 76123141 Partition the.
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  54. ?A bank is interested in identifying different attributes of its customers and below is the sample data of 150 customers. For Gender, 0 represents Male and 1 represents Female. For Personal loan, 0 represents a customer who has not taken personal loan and 1 represents a customer who has taken.
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  42. A bank is interested in identifying different attributes of its customers and below is the sample data of 150 customers. In the data table for the dummy variable Gender, 0 represents Male and 1 represents Female.  And for the dummy variable Personal loan, 0 represents a customer who has not taken.
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  11. ______________ involves descriptive statistics, data visualization, and clustering.  a. Data exploration  b. Data partitioning  c. Data preparation  d. Model assessment 12. ___________ is dividing the sample data into three sets for training, validation, and testing of the data-mining algorithm performance.  a. Data sampling  b. Data partitioning  c. Data preparation  d. Model assessment 13. Applying descriptive statistics and data visualization to the training set to understand the data and assist in.
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