1. Marketing Segmentation Cluster analysis U.S. Cities

2. Segmentation City%age Black%age Hispanic%age AsianMedian AgeUnemployment rate Per capita income(000's) Albuquerque335232518 Atlanta672131522 Austin1223329319 Baltimore5911331122 Boston2611530524 Charlotte3212 320 Chicago3920431924 Cincinnati381131821 Cleveland4751321322 Columbus231229313 Dallas3021230922 Denver1323234723 Detroit763131921 El Paso3691291113 Fort Worth2220230920 Fresno93013281316 Honolulu157137524 Houston28 430722 Indianapolis221132521 Jacksonville253232719 Kansas City304133621 Las Vegas1113433520 Long Beach14241430821 Los Angeles144010311121 Memphis551132920 Miami27631361217 Milwaukee316230522 Minneapolis132432523 Nashville231133324 New Orleans624232718 NY29247341127 Oakland441415331024 Oklahoma City165232617 Omaha133132520 Philadelphia406333923 Phoenix520231419 Pittsburgh261235721 Portland83535720 Sacramento15161532820 St. Louis481133823 San Antonio756130517 San Diego9211231820 San Francisco11142936631 San Jose5272030826 Seattle1041235528 Toledo204132619 Tucson429231319 Tulsa143133420 Virginia Beach143429618 Group U.S. cities into groups that are similar on demographics attributes such as percentage of Asians percentage of Blacks percentage of Hispanics Median age Unemployment rate Median income level

3.  STANDARDIZE 函數 (STANDARDIZE)  STANDARDIZE(x, mean, standard_dev)  X 必要 。 這是要標準化的 值。  Mean 必要 。 這是分配的算術平均 值。 (MEAN)  Standard_dev 必要 。 這是分配的標準差 。 (STDDRV) z Blackz Hispanicz Asianz Agez Unemp z income -1.178721.238954-0.362570.061342-0.75146-0.87523 2.355188-0.76443-0.4523-0.43962-0.751460.324386 -0.681770.510449-0.27285-1.44154-1.49534-0.57533 1.91345-0.82514-0.45230.5623011.4801550.324386 0.091278-0.21806-0.09339-0.94058-0.751460.924195 0.422582-0.82514-0.362570.061342-1.49534-0.27542 0.8091030.328323-0.18312-0.439620.7362820.924195 0.753886-0.82514-0.4523-0.439620.3643460.024482 1.250842-0.58231-0.45230.0613422.2240280.324386 -0.07437-0.82514-0.36257-1.44154-1.49534-2.37475 0.3121470.389031-0.36257-0.940580.7362820.324386 -0.626550.510449-0.362571.063261-0.007590.624291 2.852145-0.70373-0.4523-0.439620.7362820.024482 -1.178723.30305-0.4523-1.441541.480155-2.37475 -0.129590.328323-0.36257-0.940580.736282-0.27542 -0.847420.935410.624433-1.94252.224028-1.47504 -1.28916-0.582315.8286532.566139-0.751460.924195 0.2017120.813993-0.18312-0.94058-0.007590.324386 -0.12959-0.82514-0.45230.061342-0.751460.024482 0.03606-0.70373-0.362570.061342-0.00759-0.57533 0.312147-0.64302-0.45230.562301-0.379530.024482 -0.73698-0.09664-0.183120.562301-0.75146-0.27542 -0.571330.5711580.714161-0.940580.3643460.024482 -0.571331.5424970.355249-0.439621.4801550.024482 1.69258-0.82514-0.45230.0613420.736282-0.27542 0.1464952.938798-0.45232.065181.852091-1.17514 0.367364-0.5216-0.36257-0.94058-0.751460.324386 -0.62655-0.76443-0.183120.061342-0.751460.624291 -0.07437-0.82514-0.45230.562301-1.495340.924195 2.079102-0.64302-0.362570.061342-0.00759-0.87523 0.256930.5711580.0860661.0632611.4801551.823908 1.08519-0.035930.8038890.5623011.1082190.924195 -0.4609-0.58231-0.362570.061342-0.37953-1.17514 -0.62655-0.70373-0.45230.061342-0.75146-0.27542 0.86432-0.5216-0.272850.5623010.7362820.624291 -1.068290.328323-0.36257-0.43962-1.1234-0.57533 0.091278-0.82514-0.362571.56422-0.007590.024482 -0.90263-0.70373-0.093391.56422-0.00759-0.27542 -0.516110.0854880.8038890.0613420.364346-0.27542 1.306059-0.82514-0.45230.5623010.3643460.624291 -0.957852.513837-0.4523-0.94058-0.75146-1.17514 -0.847420.3890310.534705-0.439620.364346-0.27542 -0.73698-0.035932.060082.06518-0.379533.023526 -1.068290.7532841.252529-0.940580.3643461.524004 -0.7922-0.643020.5347051.56422-0.751462.123813 -0.24003-0.64302-0.45230.061342-0.37953-0.57533 -1.12350.874701-0.36257-0.43962-1.49534-0.57533 -0.57133-0.70373-0.45230.562301-1.1234-0.27542 -0.57133-0.70373-0.18312-1.44154-0.37953-0.87523 Mean24.3469414.591846.04081631.877557.02040816320.918374.08E-17-4.1E-17-1.2E-172.22E-161.01E-16-2.5E-16 Std dev18.1102516.472111.14481.996172.6886319013.334396111111

4.  SUMXMY2(array_x, array_y) Distance^2 to 1Distance^2 to 2Distance^2 to 3Distance^2 to 4Min Distance 7.0168974.4467215.0860827.043724.44672 19.608659.5051673.26685330.1023.266853 11.688984.41140514.7822332.237954.411405 13.5257812.057181.21286126.962121.212861 9.7804383.322897.71785318.936723.32289 16.30331.6768126.60106823.980151.676812 5.0324867.1021063.87351819.481223.873518 9.2590883.5062721.36038724.979231.360387 9.38149912.752652.82725928.641252.827259 21.981677.54686714.7761349.614667.546867 3.5205365.6603164.75147924.715473.520536 6.4152433.8489439.53688413.078483.848943 17.9711814.655591.70023436.153151.700234 10.8807828.005132.5055362.5528710.88078 3.0788734.5373685.66268727.533523.078873 5.57779418.202918.3783346.093835.577794 49.8123947.6172758.3265919.60205 3.9724434.9789026.89887223.093713.972443 11.684070.351655.62364120.451810.35165 8.7538291.090393.41014224.018241.09039 10.67141.3640733.52469919.03421.364073 9.077950.7041718.97030218.315660.704171 2.5683095.3278269.66770520.789672.568309 011.0217312.277424.152010 12.27747.606484028.981150 10.9684925.4689222.6275138.5504810.96849 10.977942.3926945.43338623.782972.392694 11.205670.8856298.47737115.484880.885629 17.17172.5630279.79229215.284832.563027 15.590368.2453931.10373733.522141.103737 7.19855313.0868510.2649711.154497.198553 7.3884310.102474.39817613.777554.398176 10.190720.9980546.75899327.888130.998054 11.0217307.60648421.806970 8.6330515.5618931.87080217.502591.870802 9.3747231.74754812.7602425.890791.747548 12.788793.4399995.47415416.561693.439999 11.675253.0170319.69068916.383183.017031 3.9131363.4580997.42392817.093313.458099 12.391096.0551851.34817919.677261.348179 8.41520112.275822.2002939.663828.415201 2.7738843.7130759.28928720.229972.773884 24.1520121.8069728.9811500 5.41948910.7112617.4003813.221245.419489 18.260139.02028117.409843.897153 9.6075220.3813645.10311223.894690.381364 10.479433.64069816.1488827.318683.640698 13.569940.3923468.84941120.48050.392346 10.60712.8322599.07593833.002692.832259

5.  MIN  會傳回一組數 值 中的最小 值。  找出與四個候選城市距離最小 ( 最相似 ) 的城市 Distance^ 2 to 1 Distance^ 2 to 2 Distance^ 2 to 3 Distance^ 2 to 4 Min Distance 7.0168974.4467215.0860827.043724.44672 19.608659.5051673.26685330.1023.266853 11.688984.41140514.7822332.237954.411405 13.5257812.057181.21286126.962121.212861 9.7804383.322897.71785318.936723.32289 16.30331.6768126.60106823.980151.676812 5.0324867.1021063.87351819.481223.873518 9.2590883.5062721.36038724.979231.360387

6.  Decision variable(H5:H8)  Assume cluster =4  Objective Function(S8)  =SUM(S10:S58) CityCluster Los Angeles24 Omaha34 Memphis25 San Francisco43 Sum Dis^2165.3482 Min Distance 4.44672 3.266853 4.411405 1.212861 3.32289 1.676812 3.873518 1.360387 2.827259 7.546867 3.520536 3.848943 1.700234 10.88078 3.078873 5.577794 19.60205 3.972443 0.35165 1.09039 1.364073 0.704171 2.568309 0 0 10.96849 2.392694 0.885629 2.563027 1.103737 7.198553 4.398176 0.998054 0 1.870802 1.747548 3.439999 3.017031 3.458099 1.348179 8.415201 2.773884 0 5.419489 3.897153 0.381364 3.640698 0.392346 2.832259

7. Distance^2 to 1Distance^2 to 2Distance^2 to 3Distance^2 to 4Min DistanceAssigned toCity 7.0168974.4467215.0860827.043724.446722Albuquerque 19.608659.5051673.26685330.1023.2668533Atlanta 11.688984.41140514.7822332.237954.4114052Austin 13.5257812.057181.21286126.962121.2128613Baltimore 9.7804383.322897.71785318.936723.322892Boston 16.30331.6768126.60106823.980151.6768122Charlotte MATCH 函數會搜尋儲存格範圍中的指定項目 ， 並傳回該項目於該範圍中的相對位置 。 =MATCH(S10,O10:R10,0) 找出距離最近的城市為第二個城市