I suspect maybe you're showing (X exits)/(N funded companies). You can't just compare binomial proportions like that, between proportions of different N. You'll have lots of random fluctuation. If Waltham had a couple of exits out of few tries, it will look better than Cambridge, even though the error bars on Cambridge would be much smaller. Maybe do a lower bound of a confidence interval? Sorry for the stats nitpicks.
Yeah, while you can solve it that way, you're not getting any insight from the analysis. Logistic regression assumes all data are known exactly, while error bars are important for this case. Take a look at my comment to the parent for a better way.
Using the "error bar" approach (Wilson score), the following ranking results (need at least 7 startups to make the list):
Good:
stddev Exits Total Place
3.17127075776 7 7 Chapel Hill, NC USA
3.01843089923 143 207 Mountain View, CA USA
2.94392395713 32 42 South San Francisco, CA USA
2.33101381278 15 20 Foster City, CA USA
2.31494411999 7 8 Itasca, IL USA
2.31494411999 7 8 Westford, MA USA
2.29564449793 539 966 San Francisco, CA USA
2.2599048739 102 171 Cambridge, MA USA
2.16920869781 37 58 Cupertino, CA USA
2.15675433868 60 99 Waltham, MA USA
2.14760333093 107 185 Santa Clara, CA USA
2.03695302614 13 18 San Bruno, CA USA
2.02879100247 8 10 Redwood Shores, CA USA
1.96889748562 50 85 Menlo Park, CA USA
1.92488214848 87 157 San Mateo, CA USA
1.88561243541 111 206 San Jose, CA USA
1.87088199283 12 17 Bedford, MA USA
1.863724487 9 12 Brisbane, CA USA
1.863724487 9 12 Alameda, CA USA
1.81215794651 31 52 Burlington, MA USA
1.80795794099 13 19 Los Gatos, CA USA
1.79841328745 398 807 New York, NY USA
1.79232968296 127 244 Palo Alto, CA USA
1.78670614594 86 161 Boston, MA USA
1.76603102228 130 252 Seattle, WA USA
1.76116206965 14 21 Arlington, VA USA
1.725668452 15 23 Aliso Viejo, CA USA
1.69835027929 16 25 Emeryville, CA USA
1.65992493077 115 228 San Diego, CA USA
1.6288650578 21 35 Milpitas, CA USA
1.62697011397 106 211 Sunnyvale, CA USA
1.61878237122 8 11 Chelmsford, MA USA
1.54045615814 9 13 Watertown, MA USA
1.54045615814 9 13 Lowell, MA USA
1.52765254628 23 40 Campbell, CA USA
1.52184678546 63 124 Redwood City, CA USA
1.45979095374 114 240 Austin, TX USA
1.44275765525 12 19 Burlingame, CA USA
1.43452996236 6 8 Calabasas, CA USA
1.42071168128 15 25 Berlin, 16 DEU
1.34271490531 7 10 Belmont, CA USA
1.34271490531 7 10 Venice, CA USA
1.34271490531 7 10 Louisville, CO USA
1.33560665511 20 36 Pasadena, CA USA
1.31683773572 18 32 Lexington, MA USA
1.3079978015 35 69 Portland, OR USA
1.29748756451 8 12 Sterling, VA USA
1.28502636002 37 74 Tel Aviv, 5 ISR
1.27560983396 9 14 El Segundo, CA USA
1.26780216551 12 20 Bothell, WA USA
1.26575403151 39 79 Fremont, CA USA
1.25989899302 42 86 Santa Monica, CA USA
1.16897845975 15 27 Marlborough, MA USA
1.15041829453 30 61 Bellevue, WA USA
1.14641742472 19 36 Morrisville, NC USA
1.12663799508 18 34 Pleasanton, CA USA
1.12663799508 18 34 Woburn, MA USA
1.11830928242 39 83 Boulder, CO USA
1.1055382851 17 32 Bethesda, MD USA
1.1055382851 17 32 Richardson, TX USA
1.0729130146 10 17 Malvern, PA USA
1.06468478349 5 7 Kitchener, ON CAN
1.06468478349 5 7 Surry Hills, 2 AUS
1.06468478349 5 7 Bridgewater, NJ USA
1.04813789306 27 56 Durham, NC USA
1.0299173951 6 9 Petah Tiqva, 2 ISR
1.02697161367 7 11 Tucson, AZ USA
1.02697161367 7 11 Kfar Saba, 2 ISR
1.00242961826 44 99 Vancouver, BC CAN
Bad:
stddev Exits Total Place
-3.03488442406 13 135 Moscow, 48 RUS
-2.24461906751 0 10 Saint Petersburg, 66 RUS
-2.24054924343 2 22 Edinburgh, U8 GBR
-2.1019848289 0 9 Porto Alegre, 23 BRA
-2.1019848289 0 9 Little Rock, AR USA
-2.1019848289 0 9 Jakarta, 4 IDN
-1.99941240406 1 14 Lexington, KY USA
-1.93525993713 0 8 Tallinn, 1 EST
-1.93525993713 0 8 Centennial, CO USA
-1.93525993713 0 8 Reno, NV USA
-1.86110616417 5 30 Columbus, OH USA
-1.74075145471 1 12 Glasgow, V2 GBR
-1.73777813442 0 7 São Paulo, 27 BRA
-1.73777813442 0 7 Taipei, 3 TWN
-1.73777813442 0 7 Livermore, CA USA
-1.73777813442 0 7 Brisbane, 4 AUS
-1.73777813442 0 7 Turku, 15 FIN
-1.73777813442 0 7 Lima, 15 PER
-1.69885420792 2 16 Quebec, QC CAN
-1.69885420792 2 16 Memphis, TN USA
-1.65383249608 9 41 Cleveland, OH USA
-1.62527946863 4 23 New Delhi, 7 IND
-1.61418876295 6 30 Buenos Aires, 7 ARG
-1.56634866608 55 178 Los Angeles, CA USA
-1.53427233432 4 22 Chennai, 25 IND
-1.48872091756 9 38 São Paulo, 2 BRA
-1.40898404732 1 10 Colorado Springs, CO USA
-1.40299548518 21 72 Dublin, 7 IRL
-1.33066279643 4 20 Melbourne, 7 AUS
-1.32829693893 130 356 London, H9 GBR
-1.30824843556 26 83 Pittsburgh, PA USA
-1.2681757003 13 46 Mumbai, 16 IND
-1.24409522273 56 159 Paris, A8 FRA
-1.21002433161 6 25 Hong Kong, HKG
-1.20538559097 1 9 Troy, MI USA
-1.20094804147 12 42 Raleigh, NC USA
-1.18989409055 24 74 Shanghai, 23 CHN
-1.18887014907 45 128 Beijing, 22 CHN
-1.11938393751 2 12 Fayetteville, AR USA
-1.11331820129 19 59 Bangalore, 19 IND
-1.10037104532 5 21 Phoenix, AZ USA
-1.09606612444 35 99 Houston, TX USA
-1.09541919563 29 84 Dallas, TX USA
-1.09414918118 3 15 Santa Ana, CA USA
-1.09414918118 3 15 Hyderabad, 2 IND
-1.05291108276 16 50 Stockholm, 26 SWE
-1.04636163325 59 155 Toronto, ON CAN
-1.0386757642 13 42 Miami, FL USA
-1.02924375409 10 34 Shenzhen, 30 CHN
-1.00129935812 9 31 Calgary, AB CAN
