Oro Assignement

Business Finance

broward college

Question Description

Attached below are the instructions for the assignment. Also attached is examples of how the assignment should be done. PLEASE DO NOT TAKE THE ASSIGNMENT IF YOU CANT NOR WON'T MEET THE DUE TIME. NO EXTRA TIME WILL BE GIVEN. Any questions or concerns please ask. DO NOT ASSUME. PLEASE READ THE INSTRUCTIONS CAREFULLY AND REVIEW THE EXAMPLE PROVIDED. THANK YOU

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toc s10 References: flo dd1 End of worksheet. This workbook contains worksheets for use in Census Data Case Projects. Main working template. Contains raw data and recommended variables. Start here. Inventory of all Census files included in the ACS extract worksheet. (This is a very small subset of all census data available). Data Dictionary. Official census data definitions, shortened descriptions, and ID name for every (over 5000) variables in the ACS extract worksheet. d of worksheet. … … s10. Transform raw data into area analysis. (step 1.0) Step 0. Click link to jump to step 0 at bottom of page. There, you be shown which zip c Step 1, s10b. Work area where you compute variables for your analysis (leaving raw da You must develop at least one variable of your own design (in addition to s10c. Once done computing key variables, list ONLY those few considered 'key' s10d. (To the right, at top). Delete columns relating to unused zip codes (the on s10e. Use cell references to create mini-table expressly for generating graphs (in s10f. Use cell references to manually create mini-table expressly for generating Step 2. Step 3. Step 4. Step 4. s10c. Key Variables Area➔➔➔ Variable Zip code➔➔➔ General variables Per capita income % with health insurance Language-compatible households (% of total) Housing tenure Other ideas (from below): Transportation availability, Household size Specialty related variables Expected # births Average age of children 17 and under Average years of education Other ideas (from below): Disability status Employment occupation Employment industry Age 65+ Custom variable of your own design % HH with children <5 and male-parent only (demonstration - do not use) s10b. Work area for computing key variables Variable.ID Variable.Description GENERAL DEMOGRAPHICS Population b01001_e001 # Total: b01001b_e001 # Total: b01001h_e001 # Total: b01001i_e001 # Total: % population white Households dp02_e020 dp02_e003 :ALL :BLK :WHT :HSP # HH Type- Avg HH size # HH Type- Total HH :ALL :ALL Weighted HH size # HH Type- Avg HH size :ALL Median age b01002_e002 b01002_e003 b01002_e004 # Medn age - Total: # Medn age - Male # Medn age - Female Household income b19013_e001 b19013b_e001 b19013i_e001 # Medn HH income # Medn HH income # Medn HH income :ALL :BLK :HSP Family income b19113_e001 b19113b_e001 b19113h_e001 b19113i_e001 # Medn family income # Medn family income # Medn family income # Medn family income :ALL :BLK :WHT :HSP Per capita income b19301_e001 b19301b_e001 b19301i_e001 Weighted PCI # Per capita income # Per capita income # Per capita income :ALL :ALL :ALL :ALL :BLK :HSP Health Insurance dp03_p127 dp03_p128 dp03_p129 dp03_p130 dp03_p131 # Per capita income # Per capita income # Per capita income :ALL :BLK :HSP % H-Ins- Civln NonInstPopn % H-Ins- With H-Ins % H-Ins- With H-Ins- Private % H-Ins- With H-Ins- Public % H-Ins- No H-Ins :ALL :ALL :ALL :ALL :ALL Weighted: # with health insurance Housing Tenure dp04_e071 dp04_e072 dp04_e073 dp04_e074 dp04_e075 dp04_e076 dp04_e077 # yr HHer Moved-in- Occupied Units :ALL # yr HHer Moved-in- Moved in 2010 or later :ALL # yr HHer Moved-in- Moved in 2000 to 2009 :ALL # yr HHer Moved-in- Moved in 1990 to 1999 :ALL # yr HHer Moved-in- Moved in 1980 to 1989 :ALL # yr HHer Moved-in- Moved in 1970 to 1979 :ALL # yr HHer Moved-in- Moved in 1969 or earlier :ALL Weighing - years per bracket 1 # yr HHer Moved-in- Moved in 2010 or later :ALL 7 # yr HHer Moved-in- Moved in 2000 to 2009 :ALL 17 # yr HHer Moved-in- Moved in 1990 to 1999 :ALL 27 # yr HHer Moved-in- Moved in 1980 to 1989 :ALL 37 # yr HHer Moved-in- Moved in 1970 to 1979 :ALL 47 # yr HHer Moved-in- Moved in 1969 or earlier :ALL Subtotal of weighted years Average tenure Do not use. Just to demonstrate average of all zip codes is NOT area average Transportation Availability dp04_e081 # VEHICLES AVAILABLE- Occupied Units :ALL dp04_e082 # VEHICLES AVAILABLE- No vehicles available :ALL # Homes with at least one car % Homes with at least one car Language (all people age 5+) - English or Spanish b16005_e001 # Total: :ALL b16005_e003 b16005_e004 b16005_e010 b16005_e011 b16005_e015 b16005_e016 b16005_e020 b16005_e021 # Native- Speak only English :ALL # Native- Speak Spanish: :ALL # Native- Speak Euro Lang- Eng very well :ALL # Native- Speak Euro Lang- Eng well :ALL # Native- Speak Asian Lang- Eng very well :ALL # Native- Speak Asian Lang- Eng well :ALL # Native- Speak Other Lang- Eng very well :ALL # Native- Speak Other Lang- Eng well :ALL b16005_e025 b16005_e026 b16005_e030 b16005_e032 b16005_e033 b16005_e037 b16005_e038 b16005_e042 b16005_e043 # Forgn born- Speak only English :ALL # Forgn born- Speak Spanish: :ALL # Forgn born- Speak Spanish- Eng not at all :ALL # Forgn born- Speak Euro Lang- Eng very well :ALL # Forgn born- Speak Euro Lang- Eng well :ALL # Forgn born- Speak Asian Lang- Eng very well :ALL # Forgn born- Speak Asian Lang- Eng well :ALL # Forgn born- Speak Other Lang- Eng very well :ALL # Forgn born- Speak Other Lang- Eng well :ALL Total English and/or Spanish % of all people aged 5 and over Language (all people age 5+) - English only b16005_e003 # Native- Speak only English :ALL b16005_e005 # Native- Speak Spanish- Eng very well :ALL b16005_e006 # Native- Speak Spanish- Eng well :ALL b16005_e010 # Native- Speak Euro Lang- Eng very well :ALL b16005_e011 # Native- Speak Euro Lang- Eng well :ALL b16005_e015 # Native- Speak Asian Lang- Eng very well :ALL b16005_e016 # Native- Speak Asian Lang- Eng well :ALL b16005_e020 b16005_e021 b16005_e025 b16005_e027 b16005_e028 b16005_e032 b16005_e033 b16005_e037 b16005_e038 b16005_e042 b16005_e043 # Native- Speak Other Lang- Eng very well :ALL # Native- Speak Other Lang- Eng well :ALL # Forgn born- Speak only English :ALL # Forgn born- Speak Spanish- Eng very well :ALL # Forgn born- Speak Spanish- Eng well :ALL # Forgn born- Speak Euro Lang- Eng very well :ALL # Forgn born- Speak Euro Lang- Eng well :ALL # Forgn born- Speak Asian Lang- Eng very well :ALL # Forgn born- Speak Asian Lang- Eng well :ALL # Forgn born- Speak Other Lang- Eng very well :ALL # Forgn born- Speak Other Lang- Eng well :ALL Total English only % of all people aged 5 and over Population by age (for pediatrics volume estimates b01001_e001 # Total: b01001_e003 # Male- Under 5 yr b01001_e004 # Male- 5 to 9 yr b01001_e005 # Male- 10 to 14 yr b01001_e006 # Male- 15 to 17 yr b01001_e027 # Female- Under 5 yr b01001_e028 # Female- 5 to 9 yr b01001_e029 # Female- 10 to 14 yr b01001_e030 # Female- 15 to 17 yr Estimated age braket age 2,50 # Male- Under 5 yr 7,00 # Male- 5 to 9 yr 12,00 # Male- 10 to 14 yr 16,00 # Male- 15 to 17 yr Total years :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL Pediatrics aged children # Pediatrics aged children % :ALL :ALL :ALL :ALL Average age Disability Status (to estimate PT volume) dp02_e103 # Disable-Civln NonInst Popn- Total :ALL dp02_e104 # Disable-Civln NonInst Popn- With a disabilit:ALL dp02_e106 # Disable-Civln NonInst Popn- <18 :ALL dp02_e107 # Disable-Civln NonInst Popn- With a disabilit:ALL dp02_e109 # Disable-Civln NonInst Popn- 18 to 64 yr :ALL dp02_e110 # Disable-Civln NonInst Popn- With a disabilit:ALL dp02_e112 # Disable-Civln NonInst Popn- >65 :ALL dp02_e113 # Disable-Civln NonInst Popn- With a disabilit:ALL Disabled: total # Disabled: total % Disabled: Age 0-17: # Disabled: Age 0-17: % Disabled: Age 18-64: # Disabled: Age 18-64: % Disabled: Age 65+: # Disabled: Age 65+: % Expected # patients Probability of using our particular services 0,10 Disabled: Age 0-17: # 0,75 Disabled: Age 18-64: # 0,10 Disabled: Age 65+: # total estimated patients Share of all disabled Share of population Fertility estimates (to estimate OB/GYN volume) dp02_e003 # HH Type- Total HH :ALL dp02_e004 # HH Type- Family HH :ALL dp02_e006 # HH Type- Family HH- Kids<18 :ALL dp02_e007 # HH Type- Family HH- Married Fam :ALL dp02_e008 # HH Type- Family HH- Married Fam- Kids<18 :ALL dp02_e009 # HH Type- Family HH- Male HHer, Singl :ALL dp02_e010 # HH Type- Family HH- Male HHer, Singl- Kids<1:ALL dp02_e011 dp02_e012 # HH Type- Family HH- Female HHer, Singl :ALL # HH Type- Family HH- Female HHer, Singl- Kids:ALL dp02_e020 dp02_e021 # HH Type- Avg HH size # HH Type- Avg family size dp02_e051 dp02_e052 dp02_e053 dp02_e054 dp02_e055 dp02_e056 dp02_e057 # Fertlty- #Women 15-50 recent birth :ALL # Fertlty- Unmarried :ALL # Fertlty- Unmarried - Per 1k unmarried women :ALL # Fertlty- Per 1k women 15-50 :ALL # Fertlty- Per 1k women 15-50- 15-19 :ALL # Fertlty- Per 1k women 15-50- 20-34 :ALL # Fertlty- Per 1k women 15-50- 35-50 :ALL :ALL :ALL Inferred women aged 15-50 # Fertlty- Per 1k women 15-50 :ALL Fertility estimate adjustment factor (net of assumed target family size of 3,00 Adjusted fertility rate Estimated upcoming births Employment - Occupation (to estimate PT / Rehab patient count) c24010_e001 # Total: :ALL c24010_e020 c24010_e022 c24010_e023 c24010_e024 c24010_e025 c24010_e026 c24010_e030 c24010_e034 # Male- Svc Occup- Health Supp :ALL # Male- Svc Occup- Protect Svc Occup- Fire :ALL # Male- Svc Occup- Protect Svc Occup- Cops :ALL # Male- Svc Occup- Food Prep :ALL # Male- Svc Occup- Cleaning :ALL # Male- Svc Occup- Pers Care :ALL # Male- Construct Occup: :ALL # Male- Product Trans Occup: :ALL c24010_e056 c24010_e058 c24010_e059 # Female- Svc Occup- Health Supp :ALL # Female- Svc Occup- Protect Svc Occup- Fire :ALL # Female- Svc Occup- Protect Svc Occup- Cops :ALL c24010_e060 c24010_e061 c24010_e062 c24010_e066 c24010_e070 # Female- Svc Occup- Food Prep # Female- Svc Occup- Cleaning # Female- Svc Occup- Pers Care # Female- Construct Occup: # Female- Product Trans Occup: :ALL :ALL :ALL :ALL :ALL Expected # patients Probability of needing services (all genders) 0,10 # Male- Svc Occup- Health Supp :ALL 0,20 # Male- Svc Occup- Protect Svc Occup- Fire :ALL 0,10 # Male- Svc Occup- Protect Svc Occup- Cops :ALL 0,05 # Male- Svc Occup- Food Prep :ALL 0,05 # Male- Svc Occup- Cleaning :ALL 0,07 # Male- Svc Occup- Pers Care :ALL 0,15 # Male- Construct Occup: :ALL 0,12 # Male- Product Trans Occup: :ALL Total expected # patients % of workforce. Employment - Industry (to estimate #/% insured) c24010_e001 # Total: :ALL c24010_e002 c24010_e003 c24010_e019 c24010_e027 c24010_e030 c24010_e034 # Male: :ALL # Male- Mgt Bus Sci Occup:ALL # Male- Svc Occup: :ALL # Male- Sales Offc Occup: :ALL # Male- Construct Occup: :ALL # Male- Product Trans Occup: :ALL c24010_e038 c24010_e039 c24010_e055 c24010_e063 c24010_e066 c24010_e070 # Female: :ALL # Female- Mgt Bus Sci Occup:ALL # Female- Svc Occup: :ALL # Female- Sales Offc Occup: :ALL # Female- Construct Occup: :ALL # Female- Product Trans Occup: :ALL Male-Female subtotal # Female- Mgt Bus Sci Occup# Female- Svc Occup: # Female- Sales Offc Occup: # Female- Construct Occup: # Female- Product Trans Occup: :ALL :ALL :ALL :ALL :ALL Expected % insured Probability of being insured 0,95 # Female- Mgt Bus Sci Occup0,75 # Female- Svc Occup: 0,80 # Female- Sales Offc Occup: 0,55 # Female- Construct Occup: 0,75 # Female- Product Trans Occup: :ALL :ALL :ALL :ALL :ALL # expected insured % expected insured Graph / Map export area . . . zip code # expected insured Employment - Industry (to estimate #/% potential staff) c24010_e001 # Total: :ALL c24010_e016 c24010_e017 c24010_e018 c24010_e020 # Male- Mgt Bus Sci Occup- Health : :ALL # Male- Mgt Bus Sci Occup- Health - Diag :ALL # Male- Mgt Bus Sci Occup- Health - Tech :ALL # Male- Svc Occup- Health Supp :ALL c24010_e052 c24010_e053 c24010_e054 c24010_e056 # Female- Mgt Bus Sci Occup- Health :ALL # Female- Mgt Bus Sci Occup- Health Diag-Tech :ALL # Female- Mgt Bus Sci Occup- Health Tech :ALL # Female- Svc Occup- Health Supp :ALL Total - All health industry workers (Male & Female) % of workforce Education dp02_e084 dp02_e085 dp02_e086 dp02_e087 dp02_e088 dp02_e089 dp02_e090 dp02_e091 dp02_e093 dp02_e094 # Educ Attain- Popn 25 yr and over :ALL # Educ Attain- Less than 9th grade :ALL # Educ Attain- 9th to 12th grade, no diploma :ALL # Educ Attain- High school graduate or equiv :ALL # Educ Attain- Some college, no degree :ALL # Educ Attain- Associate's degree :ALL # Educ Attain- Bachelor's degree :ALL # Educ Attain- Graduate or professional degree:ALL # Educ Attain- % high school graduate or highe:ALL # Educ Attain- % bachelor's degree or higher :ALL Work Count # per class # Educ Attain- Popn 25 yr and over # Less than HS # HS, Some College # Assoc # Bach # Grad :ALL Accumulate weighted school years Weight (est # # Educ Attain- Popn 25 yr and over school years) 10,00 # Less than HS 12,00 # HS, Some College 14,00 # Assoc 16,00 # Bach 18,00 # Grad :ALL Average years of education Naïve average (incorrect - demonstration only) Age 65+ Population b01001_e001 b01001_e020 b01001_e021 b01001_e022 # Total: # Male- 65 and 66 yr # Male- 67 to 69 yr # Male- 70 to 74 yr :ALL :ALL :ALL :ALL b01001_e023 b01001_e024 b01001_e025 b01001_e044 b01001_e045 b01001_e046 b01001_e047 b01001_e048 b01001_e049 # Male- 75 to 79 yr :ALL # Male- 80 to 84 yr :ALL # Male- 85 yr and over :ALL # Female- 65 and 66 yr :ALL # Female- 67 to 69 yr :ALL # Female- 70 to 74 yr :ALL # Female- 75 to 79 yr :ALL # Female- 80 to 84 yr :ALL # Female- 85 yr and over :ALL Total age 65+ (Male and Female) % age 65+ (Male and Female) Custom design (You must develop a % variable of your own design.) s10a. Raw data direct from Census files Do not delete or modify anything in this area. b01001_geo3 Geography b01001_geo1 Id b01001_geo2 Id2 b01001_e001 # Total: b01001_e002 # Male: b01001_e003 # Male- Under 5 yr b01001_e004 # Male- 5 to 9 yr b01001_e005 # Male- 10 to 14 yr b01001_e006 # Male- 15 to 17 yr b01001_e007 # Male- 18 and 19 yr b01001_e008 # Male- 20 yr b01001_e009 # Male- 21 yr b01001_e010 # Male- 22 to 24 yr :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL b01001_e011 b01001_e012 b01001_e013 b01001_e014 b01001_e015 b01001_e016 b01001_e017 b01001_e018 b01001_e019 b01001_e020 b01001_e021 b01001_e022 b01001_e023 b01001_e024 b01001_e025 b01001_e026 b01001_e027 b01001_e028 b01001_e029 b01001_e030 b01001_e031 b01001_e032 b01001_e033 b01001_e034 b01001_e035 b01001_e036 b01001_e037 b01001_e038 b01001_e039 b01001_e040 b01001_e041 b01001_e042 b01001_e043 b01001_e044 b01001_e045 b01001_e046 b01001_e047 b01001_e048 # Male- 25 to 29 yr # Male- 30 to 34 yr # Male- 35 to 39 yr # Male- 40 to 44 yr # Male- 45 to 49 yr # Male- 50 to 54 yr # Male- 55 to 59 yr # Male- 60 and 61 yr # Male- 62 to 64 yr # Male- 65 and 66 yr # Male- 67 to 69 yr # Male- 70 to 74 yr # Male- 75 to 79 yr # Male- 80 to 84 yr # Male- 85 yr and over # Female: # Female- Under 5 yr # Female- 5 to 9 yr # Female- 10 to 14 yr # Female- 15 to 17 yr # Female- 18 and 19 yr # Female- 20 yr # Female- 21 yr # Female- 22 to 24 yr # Female- 25 to 29 yr # Female- 30 to 34 yr # Female- 35 to 39 yr # Female- 40 to 44 yr # Female- 45 to 49 yr # Female- 50 to 54 yr # Female- 55 to 59 yr # Female- 60 and 61 yr # Female- 62 to 64 yr # Female- 65 and 66 yr # Female- 67 to 69 yr # Female- 70 to 74 yr # Female- 75 to 79 yr # Female- 80 to 84 yr :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL b01001_e049 b01001_m001 b01001_m002 b01001_m003 b01001_m004 b01001_m005 b01001_m006 b01001_m007 b01001_m008 b01001_m009 b01001_m010 b01001_m011 b01001_m012 b01001_m013 b01001_m014 b01001_m015 b01001_m016 b01001_m017 b01001_m018 b01001_m019 b01001_m020 b01001_m021 b01001_m022 b01001_m023 b01001_m024 b01001_m025 b01001_m026 b01001_m027 b01001_m028 b01001_m029 b01001_m030 b01001_m031 b01001_m032 b01001_m033 b01001_m034 b01001_m035 b01001_m036 b01001_m037 # Female- 85 yr and over #moE; Total: #moE; Male: #moE; Male- Under 5 yr #moE; Male- 5 to 9 yr #moE; Male- 10 to 14 yr #moE; Male- 15 to 17 yr #moE; Male- 18 and 19 yr #moE; Male- 20 yr #moE; Male- 21 yr #moE; Male- 22 to 24 yr #moE; Male- 25 to 29 yr #moE; Male- 30 to 34 yr #moE; Male- 35 to 39 yr #moE; Male- 40 to 44 yr #moE; Male- 45 to 49 yr #moE; Male- 50 to 54 yr #moE; Male- 55 to 59 yr #moE; Male- 60 and 61 yr #moE; Male- 62 to 64 yr #moE; Male- 65 and 66 yr #moE; Male- 67 to 69 yr #moE; Male- 70 to 74 yr #moE; Male- 75 to 79 yr #moE; Male- 80 to 84 yr #moE; Male- 85 yr and over #moE; Female: #moE; Female- Under 5 yr #moE; Female- 5 to 9 yr #moE; Female- 10 to 14 yr #moE; Female- 15 to 17 yr #moE; Female- 18 and 19 yr #moE; Female- 20 yr #moE; Female- 21 yr #moE; Female- 22 to 24 yr #moE; Female- 25 to 29 yr #moE; Female- 30 to 34 yr #moE; Female- 35 to 39 yr :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL :ALL b01001_m038 b01001_m039 b01001_m040 b01001_m041 b01001_m042 b01001_m043 b01001_m044 b01001_m045 b01001_m046 b01001_m047 b01001_m048 b01001_m049 b01001b_geo3 b01001b_geo1 b01001b_geo2 b01001b_e001 b01001b_e002 b01001b_e003 b01001b_e004 b01001b_e005 b01001b_e006 b01001b_e007 b01001b_e008 b01001b_e009 b01001b_e010 b01001b_e011 b01001b_e012 b01001b_e013 b01001b_e014 b01001b_e015 b01001b_e016 b01001b_e017 b01001b_e018 b01001b_e019 b01001b_e020 b01001b_e021 b01001b_e022 b01001b_e023 #moE; Female- 40 to 44 yr :ALL #moE; Female- 45 to 49 yr :ALL #moE; Female- 50 to 54 yr :ALL #moE; Female- 55 to 59 yr :ALL #moE; Female- 60 and 61 yr :ALL #moE; Female- 62 to 64 yr :ALL #moE; Female- 65 and 66 yr :ALL #moE; Female- 67 to 69 yr :ALL #moE; Female- 70 to 74 yr :ALL #moE; Female- 75 to 79 yr :ALL #moE; Female- 80 to 84 yr :ALL #moE; Female- 85 yr and over :ALL Geography Id Id # Total: :BLK # Male: :BLK # Male- Under 5 yr :BLK # Male- 5 to 9 yr :BLK # Male- 10 to 14 yr :BLK # Male- 15 to 17 yr :BLK # Male- 18 and 19 yr :BLK # Male- 20 to 24 yr :BLK # Male- 25 to 29 yr :BLK # Male- 30 to 34 yr :BLK # Male- 35 to 44 yr :BLK # Male- 45 to 54 yr :BLK # Male- 55 to 64 yr :BLK # Male- 65 to 74 yr :BLK # Male- 75 to 84 yr :BLK # Male- 85 yr and over :BLK # Female: :BLK # Female- Under 5 yr :BLK # Female- 5 to 9 yr :BLK # Female- 10 to 14 yr :BLK # Female- 15 to 17 yr :BLK # Female- 18 and 19 yr :BLK # Female- 20 to 24 yr :BLK b01001b_e024 b01001b_e025 b01001b_e026 b01001b_e027 b01001b_e028 b01001b_e029 b01001b_e030 b01001b_e031 b01001b_m001 b01001b_m002 b01001b_m003 b01001b_m004 b01001b_m005 b01001b_m006 b01001b_m007 b01001b_m008 b01001b_m009 b01001b_m010 b01001b_m011 b01001b_m012 b01001b_m013 b01001b_m014 b01001b_m015 b01001b_m016 b01001b_m017 b01001b_m018 b01001b_m019 b01001b_m020 b01001b_m021 b01001b_m022 b01001b_m023 b01001b_m024 b01001b_m025 b01001b_m026 b01001b_m027 b01001b_m028 b01001b_m029 b01001b_m030 # Female- 25 to 29 yr # Female- 30 to 34 yr # Female- 35 to 44 yr # Female- 45 to 54 yr # Female- 55 to 64 yr # Female- 65 to 74 yr # Female- 75 to 84 yr # Female- 85 yr and over #moE; Total: #moE; Male: #moE; Male- Under 5 yr #moE; Male- 5 to 9 yr #moE; Male- 10 to 14 yr #moE; Male- 15 to 17 yr #moE; Male- 18 and 19 yr #moE; Male- 20 to 24 yr #moE; Male- 25 to 29 yr #moE; Male- 30 to 34 yr #moE; Male- 35 to 44 yr #moE; Male- 45 to 54 yr #moE; Male- 55 to 64 yr #moE; Male- 65 to 74 yr #moE; Male- 75 to 84 yr #moE; Male- 85 yr and over #moE; Female: #moE; Female- Under 5 yr #moE; Female- 5 to 9 yr #moE; Female- 10 to 14 yr #moE; Female- 15 to 17 yr #moE; Female- 18 and 19 yr #moE; Female- 20 to 24 yr #moE; Female- 25 to 29 yr #moE; Female- 30 to 34 yr #moE; Female- 35 to 44 yr #moE; Female- 45 to 54 yr #moE; Female- 55 to 64 yr #moE; Female- 65 to 74 yr #moE; Female- 75 to 84 yr :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK :BLK b01001b_m031 b01001h_geo3 b01001h_geo1 b01001h_geo2 b ...
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