Fluent Essays

Doc1 is instructions. Doc2 is the dataset. doc3 is supplemental info. Use R scripts for coding. Introduction As a data analyst, you will assess continuous data sources for their relevance to specific research questions throughout your career. In your previous coursework, you have performed data cleaning and exploratory data analysis on your data. You have seen basic trends and patterns and now can start building more sophisticated statistical models. In this course, you will use and explore both multiple regression and logistic regression models and their assumptions. For this task, you will use the churn dataset. You will then review the data dictionary related to the raw data file you have chosen, and prepare the data set file for multiple regression modeling. The organizations connected with the given data sets for this task seek to analyze their operations and have collected variables of possible use to support decision-making processes. You will analyze your chosen data set using multiple regression modeling, create visualizations, and deliver the results of your analysis. It is recommended that you use the cleaned data set from your previous course. Requirements You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course. Part I: Research Question A.  Describe the purpose of this data analysis by doing the following: 1.  Summarize one research question that is relevant to a real-world organizational situation captured in the data set you have selected and that you will answer using multiple regression. 2.  Define the objectives or goals of the data analysis. Ensure that your objectives or goals are reasonable within the scope of the data dictionary and are represented in the available data. Part II: Method Justification B.  Describe multiple regression methods by doing the following: 1.  Summarize the assumptions of a multiple regression model. 2.  Describe the benefits of using the tool(s) you have chosen (i.e., Python, R, or both) in support of various phases of the analysis. 3.  Explain why multiple regression is an appropriate technique to analyze the research question summarized in Part I. Part III: Data Preparation C.  Summarize the data preparation process for multiple regression analysis by doing the following: 1.  Describe your data preparation goals and the data manipulations that will be used to achieve the goals. 2.  Discuss the summary statistics, including the target variable and all predictor variables that you will need to gather from the data set to answer the research question. 3.  Explain the steps used to prepare the data for the analysis, including the annotated code. 4.  Generate univariate and bivariate visualizations of the distributions of variables in the cleaned data set. Include the target variable in your bivariate visualizations. 5.  Provide a copy of the prepared data set. Part IV: Model Comparison and Analysis D.  Compare an initial and a reduced multiple regression model by doing the following: 1.  Construct an initial multiple regression model from all predictors that were identified in Part C2. 2.  Justify a statistically based variable selection procedure and a model evaluation metric to reduce the initial model in a way that aligns with the research question. 3.  Provide a reduced multiple regression model that includes both categorical and continuous variables. Note: The output should include a screenshot of each model. E.  Analyze the data set using your reduced multiple regression model by doing the following: 1.  Explain your data analysis process by comparing the initial and reduced multiple regression models, including the following elements: •  the logic of the variable selection technique •  the model evaluation metric •  a residual plot 2.  Provide the output and any calculations of the analysis you performed, including the model’s residual error. Note: The output should include the predictions from the refined model you used to perform the analysis.  3.  Provide the code used to support the implementation of the multiple regression models. Part V: Data Summary and Implications F.  Summarize your findings and assumptions by doing the following: 1.  Discuss the results of your data analysis, including the following elements: •  a regression equation for the reduced model •  an interpretation of coefficients of the statistically significant variables of the model •  the statistical and practical significance of the model •  the limitations of the data analysis 2.  Recommend a course of action based on your results. Scenario: Telecommunications Churn In the telecommunications industry, customers can choose from multiple service providers and actively switch from one provider to another. Customer “churn” is defined as the percentage of customers who stopped using a provider’s product or service during a certain time frame. In this highly competitive market, some telecommunications industries can experience average annual churn rates as high as 25 percent. Given that it costs 10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many providers, retaining highly profitable customers is the number one business goal. To reduce customer churn, telecommunications companies need to predict which customers are at high risk of churn. You are an analyst on a team of analysts in a popular telecommunications company, which serves customers in all regions of the United States. You have been asked to analyze the data set to explore the data, identify trends, and compare key metrics. Data File Being Used: churn_clean.csv Data Dictionary: The data set includes the following information: • customers who left within the last month (the “Churn” column) • services that each customer signed up for (phone, multiple lines, internet, online security, online backup, device protection, technical support, and streaming TV and movies) • customer account information (how long they’ve been a customer, contracts, payment methods, paperless billing, monthly charges, GB usage over a year, etc.) • customer demographics (gender, age, job, income, etc.) The data set consists of 10,000 customers and 50 columns/variables: • CaseOrder: A placeholder variable to preserve the original order of the raw data file • Customer_id: Unique customer ID • Interaction, UID: Unique IDs related to customer transactions, technical support, and sign-ups The following variables represent customer demographic data: o City: Customer city of residence as listed on the billing statement o State: Customer state of residence as listed on the billing statement o County: Customer county of residence as listed on the billing statement o Zip: Customer zip code of residence as listed on the billing statement o Lat, Lng: GPS coordinates of customer residence as listed on the billing statement o Population: Population within a mile radius of customer, based on census data o Area: Area type (rural, urban, suburban), based on census data o TimeZone: Time zone of customer residence based on customer’s sign-up information o Job: Job of the customer (or invoiced person) as reported in sign-up information o Children: Number of children in customer’s household as reported in sign-up information (may not be children of customer) o Age: Age of customer as reported in sign-up information o Income: Annual income of customer (or invoiced person) as reported at time of sign-up o Marital: Marital status of customer as reported in sign-up information o Gender: Customer self-identification as male, female, or nonbinary • Churn: Whether the customer discontinued service within the last month (yes, no) • Outage_sec_perweek: Average number of seconds per week of system outages in the customer’s neighborhood • Email: Number of emails sent to the customer in the last year (marketing or correspondence) • Contacts: Number of times customer contacted technical support (or if a new customer, similar customer contacts in the new customer’s profile) • Yearly_equip_failure: The number of times customer’s equipment failed and had to be reset/replaced in the past year (or if new customer, similar failures as seen in the new customer’s profile) • Techie: Whether the customer considers themselves technically inclined (based on customer questionnaire when they signed up for services) (yes, no) • Contract: The contract term of the customer (month-to-month, one year, two year) • Port_modem: Whether the customer has a portable modem (yes, no) • Tablet: Whether the customer owns a tablet such as iPad, Surface, etc. (yes, no) • InternetService: Customer’s internet service provider (DSL, fiber optic, None) • Phone: Whether the customer has a phone service (yes, no) • Multiple: Whether the customer has multiple lines (yes, no) • OnlineSecurity: Whether the customer has an online security add-on (yes, no) • OnlineBackup: Whether the customer has an online backup add-on (yes, no) • DeviceProtection: Whether the customer has device protection add-on (yes, no) • TechSupport: Whether the customer has a technical support add-on (yes, no) • StreamingTV: Whether the customer has streaming TV (yes, no) • StreamingMovies: Whether the customer has streaming movies (yes, no) • PaperlessBilling: Whether the customer has paperless billing (yes, no) • PaymentMethod: The customer’s payment method (electronic check, mailed check, bank (automatic bank transfer), credit card (automatic)) • Tenure: Number of months the customer has stayed with the provider • MonthlyCharge: The amount charged to the customer monthly. This value reflects an average per customer. For brand new customers, this value is the average for other customers who fit the new customer’s profile. • Bandwidth_GB_Year: The average amount of data used, in GB, in a year by the customer (if the customer is newer than a year, this value is approximated based on initial use or of average usage for a typical customer in their demographic profile) The following variables represent responses to an eight-question survey asking customers to rate the importance of various factors/surfaces on a scale of 1 to 8 (1 = most important, 8 = least important) o Item1: Timely response o Item2: Timely fixes o Item3: Timely replacements o Item4: Reliability o Item5: Options o Item6: Respectful response o Item7: Courteous exchange o Item8: Evidence of active listening CaseOrder Customer_id Interaction UID City State County Zip Lat Lng Population Area TimeZone Job Children Age Income Marital Gender Churn Outage_sec_perweek Email Contacts Yearly_equip_failure Techie Contract Port_modem Tablet InternetService Phone Multiple OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies PaperlessBilling PaymentMethod Tenure MonthlyCharge Bandwidth_GB_Year Item1 Item2 Item3 Item4 Item5 Item6 Item7 Item8 1 K409198 aa90260b-4141-4a24-8e36-b04ce1f4f77b e885b299883d4f9fb18e39c75155d990 Point Baker AK Prince of Wales-Hyder 99927 56.251 -133.37571 38 Urban America/Sitka Environmental health practitioner 0 68 28561.99 Widowed Male No 7.978322947 10 0 1 No One year Yes Yes Fiber Optic Yes No Yes Yes No No No Yes Yes Credit Card (automatic) 6.795512947 172.455519 904.5361102 5 5 5 3 4 4 3 4 2 S120509 fb76459f-c047-4a9d-8af9-e0f7d4ac2524 f2de8bef964785f41a2959829830fb8a West Branch MI Ogemaw 48661 44.32893 -84.2408 10446 Urban America/Detroit Programmer, multimedia 1 27 21704.77 Married Female Yes 11.69907956 12 0 1 Yes Month-to-month No Yes Fiber Optic Yes Yes Yes No No No Yes Yes Yes Bank Transfer(automatic) 1.156680997 242.632554 800.9827661 3 4 3 3 4 3 4 4 3 K191035 344d114c-3736-4be5-98f7-c72c281e2d35 f1784cfa9f6d92ae816197eb175d3c71 Yamhill OR Yamhill 97148 45.35589 -123.24657 3735 Urban America/Los_Angeles Chief Financial Officer 4 50 9609.57 Widowed Female No 10.75280028 9 0 1 Yes Two Year Yes No DSL Yes Yes No No No No No Yes Yes Credit Card (automatic) 15.75414408 159.947583 2054.706961 4 4 2 4 4 3 3 3 4 D90850 abfa2b40-2d43-4994-b15a-989b8c79e311 dc8a365077241bb5cd5ccd305136b05e Del Mar CA San Diego 92014 32.96687 -117.24798 13863 Suburban America/Los_Angeles Solicitor 1 48 18925.23 Married Male No 14.91353964 15 2 0 Yes Two Year No No DSL Yes No Yes No No No Yes No Yes Mailed Check 17.08722662 119.95684 2164.579412 4 4 4 2 5 4 3 3 5 K662701 68a861fd-0d20-4e51-a587-8a90407ee574 aabb64a116e83fdc4befc1fbab1663f9 Needville TX Fort Bend 77461 29.38012 -95.80673 11352 Suburban America/Chicago Medical illustrator 0 83 40074.19 Separated Male Yes 8.147416533 16 2 1 No Month-to-month Yes No Fiber Optic No No No No No Yes Yes No No Mailed Check 1.670971726 149.948316 271.4934362 4 4 4 3 4 4 4 5 6 W303516 2b451d12-6c2b-4cea-a295-ba1d6bced078 97598fd95658c80500546bc1dd312994 Fort Valley GA Peach 31030 32.57032 -83.8904 17701 Urban America/New_York Chief Technology Officer 3 83 22660.2 Never Married Female No 8.420992898 15 3 1 No One year Yes No None Yes Yes Yes Yes Yes No No Yes No Electronic Check 7.000993555 185.007692 1039.357983 3 3 3 2 4 3 3 3 7 U335188 6630d501-838c-4be4-a59c-6f58c814ed6a 87d1c4223e49156020564c01a88973b9 Pioneer TN Scott 37847 36.4342 -84.27892 2535 Suburban America/New_York Surveyor, hydrographic 0 79 11467.5 Widowed Male Yes 11.18272453 10 0 1 Yes Month-to-month No No DSL Yes No No No No Yes Yes Yes No Electronic Check 13.23677381 200.118516 1907.242972 6 5 6 4 1 5 5 5 8 V538685 70ddaa89-b726-49dc-9022-2d655e4c7936 fce3f21888317907de42e298d718ccce Oklahoma City OK Oklahoma 73109 35.43313 -97.52463 23144 Suburban America/Chicago Sales promotion account executive 2 30 26759.64 Married Female Yes 7.791632265 16 0 0 Yes Month-to-month No No DSL No No No Yes No No No No Yes Mailed Check 4.26425515 114.950905 979.6127078 2 2 2 5 2 3 4 5 9 M716771 05a49ee3-8fd5-453a-a5f3-82b6cd986856 6c7043ced703b84de29766af3d53c976 Saint Cloud FL Osceola 34771 28.27646 -81.16273 17351 Suburban America/New_York Teaching laboratory technician 2 49 58634.51 Separated Nonbinary No 5.739005915 20 2 3 No Month-to-month Yes No DSL Yes No Yes Yes No No No No Yes Bank Transfer(automatic) 8.220686373 117.468591 1312.874964 5 4 4 3 4 3 4 4 10 I676080 86f17e4d-2c24-4b70-a6ec-dddf0609dbaa 6ffe183271258a039e122ced8750b2a0 Cincinnati OH Hamilton 45237 39.19296 -84.4523 20193 Rural America/New_York Museum education officer 1 86 50231.4 Married Female No 8.707823904 18 1 0 No Two Year Yes No Fiber Optic Yes No Yes No Yes No No Yes Yes Mailed Check 3.422086139 162.482694 508.7637913 2 2 2 2 5 2 3 3 11 J980369 89490f4b-765f-431a-b302-580aae7db71a 6271cceddef89e353881b8c38b5674b1 Little Meadows PA Susquehanna 18830 41.95142 -76.10744 555 Urban America/New_York Teacher, special educational needs 7 23 22580.7 Separated Female No 9.341631685 9 0 2 No Month-to-month No No DSL Yes Yes Yes Yes No No Yes No No Mailed Check 19.26726194 174.958118 2728.767869 4 4 4 7 3 3 3 4 12 E243720 c32b8522-d62a-49b1-a77a-e0534cb1378b 073356b840a6f10f65bebf6c9c168b3a Corozal PR Corozal 783 18.3041 -66.32847 33372 Rural America/Puerto_Rico Maintenance engineer 2 56 18342.12 Married Female No 6.68082638 17 1 1 No Month-to-month Yes No Fiber Optic Yes No No No Yes No Yes No No Electronic Check 10.52199781 149.962093 1180.588788 4 4 3 4 4 4 3 4 13 F139569 4828f66e-5592-4c61-bbf4-313cf8731f1c fe004e8605f165c2c59064fca86389d5 Conesville IA Muscatine 52739 41.37287 -91.36865 556 Rural America/Chicago Engineer, broadcasting (operations) 0 83 83671.08 Divorced Male No 8.273875452 9 0 0 No Month-to-month Yes No Fiber Optic Yes No No No No No Yes No No Bank Transfer(automatic) 13.01149229 137.439154 1196.388018 1 2 1 4 3 2 3 3 14 X44200 780992d3-d758-4152-aceb-5d59edda3d15 2a93472e2def0e9ea2ead2f8c15c80c9 East Livermore ME Androscoggin 4228 44.43256 -70.11501 0 Urban America/New_York Learning disability nurse 5 29 115114.57 Separated Female No 5.880565591 14 1 0 No Two Year Yes Yes Fiber Optic Yes Yes Yes No No Yes Yes No Yes Bank Transfer(automatic) 16.87922024 184.971516 1948.694497 5 6 5 2 4 5 4 4 15 H68068 8dc7ad15-2f59-4c77-9640-6f2c0000b3fc 82e2317aca04b63397b31395029f50bf Hillside IL Cook 60162 41.86752 -87.90222 8165 Urban America/Chicago Automotive engineer 1 30 64256.81 Separated Male Yes 11.79073041 10 3 0 No Month-to-month No No DSL No Yes No Yes No Yes No No Yes Bank Transfer(automatic) 10.06019902 159.965581 1582.295235 3 3 4 2 3 4 4 2 16 A403906 df437bd4-bd8a-4c8f-8110-11ed1a7723ea d43e8bc69c074903d9c618596562747e Saint Germain WI Vilas 54558 45.92342 -89.50138 2093 Suburban America/Chicago Amenity horticulturist 3 39 89061.45 Divorced Male Yes 10.79846973 13 1 0 No One year No No None Yes No Yes No No No Yes Yes Yes Electronic Check 13.87001293 177.65076 1840.014467 3 3 3 2 4 3 5 2 17 V847470 bbf5ca9f-cb89-44ea-ac42-3f5862ebdbd0 c2b766b12c3780537d74b858ae602a93 Modesto CA Stanislaus 95351 37.62326 -120.99637 50079 Urban America/Los_Angeles Applications developer 0 63 31659.3 Married Female Yes 13.52284721 13 1 0 No Month-to-month Yes No DSL Yes Yes No Yes Yes No No Yes Yes Bank Transfer(automatic) 15.78214958 194.966286 2070.376729 3 4 4 3 5 4 4 3 18 F721878 5c3548de-4744-4167-9412-3f018262bab2 66c10f561f108fd744c68c6c5b388c53 Parkton NC Robeson 28371 34.90636 -78.98944 7249 Rural America/New_York Immunologist 2 60 44142.81 Separated Male Yes 9.831166695 16 3 0 No Two Year No No None Yes No Yes No Yes Yes Yes Yes No Mailed Check 2.303331273 202.682861 882.0985933 2 2 4 3 3 4 3 3 19 F487435 200fad69-1e40-4de6-9222-df84d8925000 90c4427b652ec11afec04bd3c73c88d2 Oakland TN Fayette 38060 35.2091 -89.50332 9463 Rural America/Chicago Engineer, electrical 3 61 39262.14 Widowed Female No 9.57470568 11 1 0 Yes Two Year Yes No None Yes No No Yes Yes Yes Yes No Yes Bank Transfer(automatic) 17.10995632 152.490739 1833.0967 3 4 3 4 3 2 3 5 20 B561228 5ff2d3a8-6e9f-4bfa-af2b-04e5cdc0a1e0 34cb26f446dde7d77a6ecc5d7e106942 Trafford AL Blount 35172 33.84469 -86.6974 3177 Rural America/Chicago Broadcast presenter 3 23 19494.75 Divorced Male Yes 10.92246098 10 1 0 No Month-to-month Yes No DSL Yes Yes No No No No Yes No Yes Credit Card (automatic) 12.80615845 149.944668 1954.080809 5 5 5 4 4 4 3 3 21 X325271 ebf7fbd7-9f65-48d8-8f82-b7b7fd4f3412 21877fa7a1ff6e02e419246b1211b228 Kaneville IL Kane 60144 41.83594 -88.5206 69 Rural America/Chicago Counsellor 4 38 39624.21 Never Married Male No 12.06889188 14 0 3 No Month-to-month No No Fiber Optic Yes No Yes Yes Yes No No Yes Yes Bank Transfer(automatic) 20.45390977 184.978458 2330.319383 2 3 3 2 4 2 3 3 22 I282896 69fbc82f-1ea2-47a2-a056-d6f8d428b687 994c0d689a45958a51dde0312f52258b Columbus OH Franklin 43215 39.96636 -83.01286 14440 Suburban America/New_York Geophysical data processor 3 30 45714.47 Divorced Male No 6.984819996 18 1 0 No Two Year No Yes Fiber Optic Yes No Yes No Yes No No No No Bank Transfer(automatic) 2.415991977 127.495766 594.1054279 5 5 4 4 3 4 3 5 23 I367206 1937a759-91b7-4390-80f1-6f8e7d81e058 3bc3a28af4d353a18ab1173dabdc6496 Mallard IA Palo Alto 50562 42.96153 -94.64179 381 Urban America/Chicago Designer, multimedia 3 52 27442.03 Widowed Male No 9.372975594 16 1 1 No Month-to-month No No Fiber Optic Yes No No No No Yes No No Yes Electronic Check 6.652298992 124.964303 713.0633088 3 3 3 2 2 3 5 5 24 N37182 ab3bfaf3-94f1-4a14-b577-0e57178a7645 adab17955634cf8ec0cd47d8d89c352c Baileyville ME Washington 4694 45.10333 -67.47223 2193 Urban America/New_York Event organiser 2 68 79699.62 Divorced Male No 7.109002592 9 0 0 No One year Yes No Fiber Optic Yes No No No No Yes Yes No Yes Electronic Check 8.543717478 149.948316 945.3802928 4 4 3 5 3 4 4 3 25 U644626 30a2f575-54cc-4894-96b8-358317896f43 c741e189e7837dca1ae8e031402b447d Williamsburg OH Clermont 45176 39.08339 -84.02314 9434 Rural America/New_York Equality and diversity officer 3 75 28520.32 Widowed Female Yes 13.74777955 15 1 0 No Month-to-month No Yes None Yes Yes Yes No Yes No No Yes No Credit Card (automatic) 5.770390256 162.511928 870.76398 3 3 4 2 4 4 3 4 26 A766555 44c6b2bd-74ee-4c80-9b9f-67b544d1e06a 6cab409a353f4b1e618308bb6383f4ae Saint Augustine FL St. Johns 32086 29.75675 -81.30311 29568 Suburban America/New_York Counsellor 1 77 12558.83 Separated Male Yes 9.767668044 12 1 0 No Month-to-month No Yes DSL Yes No No No No No No No No Bank Transfer(automatic) 4.157658837 92.455141 774.2937069 4 4 4 4 2 4 5 5 27 V54032 eba9d80f-e4e6-4905-81ab-5f5f2a49cc88 f7184e21cba24f672130af4181ace3fc Tohatchi NM McKinley 87325 36.00209 -108.62285 3089 Rural America/Denver Psychiatrist 1 47 8762.23 Married Female Yes 15.8626095 15 0 1 No Month-to-month Yes No Fiber Optic Yes No Yes No Yes No Yes Yes No Mailed Check 10.40566091 222.649979 1506.446527 5 3 5 3 3 5 4 4 28 Z301326 f4ba5aef-80af-43fe-a8cb-0592f3013d6b 8f35529b5d78c0c900635ceebf6b84cb Erie MI Monroe 48133 41.7812 -83.48594 5652 Urban America/Detroit Surveyor, commercial/residential 0 70 43882.72 Widowed Female Yes 14.73616645 12 0 0 No Month-to-month Yes No None Yes No No Yes Yes No Yes No Yes Electronic Check 1.392421597 139.981577 419.0508708 2 2 3 4 4 2 2 2 29 J887250 a49a3cbf-182c-49f5-b15f-e124582d2c8f 1e083de4b0dfeed53263515c8b8b26eb Moretown VT Washington 5660 44.24718 -72.74137 1716 Urban America/New_York Civil Service administrator 3 20 10482.52 Separated Female Yes 13.43558029 13 2 0 Yes One year No No DSL Yes No No No Yes No Yes Yes No Bank Transfer(automatic) 1.430386349 200.132293 1259.415493 3 4 3 3 2 4 4 2 30 Z686376 cddc4b08-7aae-4194-a75c-28bbcc8257f3 82bbe8f8a091353aad86806f5fabc658 Silver Spring MD Montgomery 20902 39.04373 -77.04224 52484 Suburban America/New_York Radiographer, diagnostic 4 69 33442.79 Never Married Male Yes 10.61771118 18 1 0 No Two Year No Yes DSL Yes Yes Yes Yes Yes No Yes Yes Yes Credit Card (automatic) 5.425864884 257.651257 1745.125956 4 4 4 3 2 5 5 4 31 L357432 79b827eb-46b9-4737-8484-0b670171bc4b 2024dab5fff54350716edea719cb3a9b Whitesboro NY Oneida 13492 43.11988 -75.32875 11268 Urban America/New_York Air traffic controller 9 45 43383.54 Married Male No 6.613070806 14 0 0 No Two Year Yes No Fiber Optic No No No Yes No No Yes Yes Yes Credit Card (automatic) 11.08287797 230.105118 1795.465729 3 2 1 5 3 3 3 2 32 V899967 5cffec4f-2316-4f97-b0d6-106888164492 e82cf64d7a0e5a2f0ef56b6dc68b08bb Sherwood OH Defiance 43556 41.30608 -84.56989 1765 Rural America/New_York Dietitian 0 40 21793.88 Never Married Female No 4.450171647 13 2 0 No Two Year No Yes None Yes Yes No Yes No Yes No No No Electronic Check 3.586184954 147.489301 492.4771472 2 3 4 3 3 2 4 3 33 G571225 c7c6fcb5-eb79-438e-bf66-179ac491274e f1641e36607077033891129802343682 Pe Ell WA Lewis 98572 46.5512 -123.32437 680 Rural America/Los_Angeles Therapist, occupational 6 82 51960.51 Divorced Female Yes 8.774646693 9 0 0 No Month-to-month Yes Yes Fiber Optic Yes No No Yes No No Yes Yes Yes Electronic Check 4.727388465 230.105118 1013.993166 4 4 4 5 2 5 3 5 34 S381541 a81aaffc-186f-46d1-8378-ad11bcedde9c 5377bc298c0d723478837a5d98bc738b Seney MI Schoolcraft 49883 46.43517 -86.01334 136 Rural America/Detroit Building services engineer 5 74 25942.1 Separated Male Yes 11.13156721 8 1 1 No Month-to-month No No DSL Yes Yes Yes Yes No No Yes No No Electronic Check 20.89923386 174.958118 2680.762173 5 5 4 3 5 3 4 1 35 R195593 fd1b0111-3d91-460d-b26b-d52008c383f8 6db59b17dc83e5b8af6d312100ea2965 Chatsworth IL Livingston 60921 40.73772 -88.28785 1517 Urban America/Chicago Information officer 4 69 11211.59 Widowed Male Yes 5.540788887 15 1 0 No One year Yes No Fiber Optic Yes Yes Yes No Yes Yes Yes Yes No Mailed Check 3.471329958 267.664655 1081.814362 6 5 5 5 2 6 5 5 36 I327596 c329889a-43ac-4d20-98f3-50d1c4c527fb d8fc79b5e3193a0dd891a4b6afac838d Albion MI Calhoun 49224 42.28109 -84.75184 14103 Suburban America/Detroit Outdoor activities/education manager 1 52 8283.29 Never Married Male Yes 13.80362698 11 0 1 No Month-to-month Yes No DSL Yes Yes Yes Yes No Yes No No Yes Mailed Check 5.478588204 162.483267 1209.961168 4 2 3 4 3 3 4 3 37 U790894 01fa4db5-6be5-47de-a3ac-53b95e5c44a8 f57314d251b601e0b1f18a432612c3ff Marlborough CT Hartford 6447 41.63306 -72.45472 6394 Suburban America/New_York Market researcher 2 26 10114.81 Never Married Female Yes 16.31017089 16 1 1 No Month-to-month Yes No Fiber Optic Yes Yes Yes Yes Yes Yes No Yes Yes Credit Card (automatic) 3.312991809 229.993134 897.5919144 4 3 5 5 2 4 5 5 38 O739489 1659c53c-7045-445c-a222-08c53851aac1 cdcabd9f398e96aa39936f87adde7dc0 Lafayette NJ Sussex 7848 41.10336 -74.68407 5059 Urban America/New_York Surveyor, insurance 1 25 36229.21 Never Married Female No 9.447065309 9 2 0 No One year Yes Yes DSL Yes Yes Yes No Yes No No No Yes Electronic Check 7.161561527 140.00128 1340.930156 5 6 4 2 5 3 3 3 39 I958127 9d8f0590-1da6-4b6f-ab72-ef78c8aaac24 7d59e47c0a72bb47918543996d0990ba Washington DC District of Columbia 20017 38.93845 -76.99317 20450 Suburban America/New_York Office manager 1 66 25397.44 Divorced Female No 16.84100845 12 1 0 No Two Year Yes No Fiber Optic Yes No Yes Yes No Yes No Yes No Credit Card (automatic) 9.847563364 184.964681 1113.993679 4 4 3 4 2 4 4 5 40 Z666770 e8e28d51-c371-4a39-a81c-0c08d2f1f6ba 7e5b41b7d2ad340e17143f4066c3ab33 Dublin PA Bucks 18917 40.37305 -75.2041 2123 Suburban America/New_York Editorial assistant 1 72 50336.5 Divorced Male No 7.972937227 10 1 1 No Two Year Yes No Fiber Optic Yes No No Yes Yes No No No No Electronic Check 16.04202196 147.473844 1530.10769 3 4 3 6 4 4 6 2 41 X711438 3c086e5c-e913-43b6-90d0-30b15b7cd37f bc2290835aee9167c09294399ef208c9 Mantoloking NJ Ocean 8738 40.02145 -74.06183 1164 Urban America/New_York Customer service manager 3 21 38211.37 Divorced Female No 11.42906608 12 1 0 No Month-to-month No No Fiber Optic Yes No No Yes Yes No Yes No Yes Mailed Check 9.179779682 172.457857 1310.137419 5 2 3 3 3 5 4 4 42 U369541 282e3c6b-40f1-4460-8afa-7f2a1e88f06c 72d20b3d5284a2af94ec2f39fba29c39 Sproul PA Blair 16682 40.26608 -78.45782 23 Suburban America/New_York Production designer, theatre/television/film 3 55 52887.26 Married Male No 8.06137096 16 2 0 No Month-to-month Yes No DSL Yes No Yes No No Yes No No Yes Electronic Check 3.002844144 107.481989 892.8964931 3 3 2 3 5 4 3 3 43 S896111 e677e4d4-13aa-4fde-a48e-e34b94f422cd f8f47993d1fa402f133393f681022a3d Springer OK Carter 73458 34.35204 -97.25336 895 Suburban America/Chicago Analytical chemist 4 41 12003.33 Divorced Male Yes 12.54780492 11 1 2 No Two Year Yes No Fiber Optic Yes Yes Yes No No Yes Yes Yes No Credit Card (automatic) 3.924254455 255.141716 1125.581944 3 3 3 5 1 4 3 4 44 K227200 d542a248-4059-4008-a6fb-b7bc6e897a1c cbbc7e4acd904ad82aff342295cad7a0 Homer City PA Indiana 15748 40.52449 -79.08432 6699 Suburban America/New_York Print production planner 4 32 24090.62 Never Married Male No 8.255565152 13 0 0 No Two Year Yes Yes Fiber Optic Yes Yes No No No Yes No Yes Yes Electronic Check 18.55959629 192.456745 2016.710031 3 3 3 3 4 3 3 4 45 E818912 7ac233f7-4a78-4b44-95cf-37cc5c87cc41 d5dce7dccb69ce4e7a244f4b9e2ec508 New York NY New York 10035 40.7955 -73.92968 35743 Rural America/New_York Psychiatrist 0 22 15137.93 Never Married Female Yes 13.156514 6 0 0 No Two Year Yes No Fiber Optic Yes Yes Yes No No No Yes Yes Yes Credit Card (automatic) 8.798394712 242.632554 1412.736809 4 4 4 4 3 4 3 4 46 Y490834 79eb6124-9f8f-4329-90d1-903bd8bbb43d 2cf02d65e777660bee29aabf8338d7a0 Sagola MI Dickinson 49881 46.07835 -88.03348 320 Urban America/Menominee Conservation officer, nature 2 44 54458.38 Divorced Female Yes 10.58349704 5 1 0 No Month-to-month Yes Yes None Yes Yes Yes Yes Yes No Yes Yes Yes Electronic Check 10.377366 245.174977 1710.689671 3 3 2 2 4 3 5 2 47 B609739 bb4a7c2d-6524-41d6-b2ed-f7511509ff5b e0f8186edebc6ed9650b5dce5496960d Peoria IL Peoria 61606 40.6998 -89.61143 7870 Suburban America/Chicago Broadcast presenter 2 23 132116.33 Divorced Male Yes 12.50184778 11 1 0 No Month-to-month Yes No Fiber Optic Yes No Yes Yes No Yes No Yes Yes Bank Transfer(automatic) 22.25746016 184.964681 2384.885909 4 4 5 2 2 4 3 3 48 E687638 a7cae1b4-3c31-4037-8755-ac692dba6d4c 96b1ca7a9b1642d44b22ac0e05fc47ec Acme WA Whatcom 98220 48.68497 -122.19692 861 Suburban America/Los_Angeles Applications developer 3 43 37358.13 Never Married Female Yes 9.750038902 11 1 0 Yes Month-to-month No No Fiber Optic Yes Yes Yes Yes Yes Yes No No Yes Credit Card (automatic) 9.427146079 195.006206 1213.940165 4 5 3 4 3 3 4 3 49 E492264 1df1db19-9bfc-407a-a72c-203fada5b5d3 0792f3196f67e1c2e518931ecba4ce51 Chattanooga TN Hamilton 37416 35.10444 -85.17661 15364 Rural America/New_York Librarian, public 1 86 26534.16 Separated Female No 7.868660851 11 2 2 No Two Year Yes No None Yes No No No No No Yes No No Bank Transfer(automatic) 7.760290062 104.962874 740.010722 3 4 3 2 3 3 3 3 50 W255321 7abea12e-f7b4-4e5a-be09-6f9fb061f923 3d5a7f23e552c62a0921e1fa051e015f Harvey ND Wells 58341 47.77527 -99.85 2589 Suburban America/Chicago Financial adviser 2 84 76430.83 Married Female No 12.43563329 14 0 1 No Month-to-month No Yes None Yes No No No No Yes No No Yes Electronic Check 11.56938439 92.488023 897.9716153 3 3 4 3 5 3 3 4 51 S72442 6711579b-29d7-460e-bc34-10d4d1110a06 eba011bb46de58c6e2cea23d0097d290 Goodwater AL Coosa 35072 33.08977 -86.06138 4901 Urban America/Chicago Surveyor, building 2 62 4662.37 Separated Female Yes 8.957166926 13 3 0 No Month-to-month Yes No DSL Yes Yes No No Yes Yes No Yes No Bank Transfer(automatic) 3.343811019 184.979684 1026.387172 2 2 3 4 3 5 2 4 52 O845020 91dc4423-1f3b-4ff3-bb12-6da4c1deabe8 1b7ff908a8870766a8b463e23757aec4 Lexington NC Davidson 27292 35.73451 -80.20925 39649 Urban America/New_York Horticulturist, amenity 5 68 43820.85 Divorced Female Yes 8.015021713 12 1 0 No Month-to-month Yes No None Yes No No No Yes No Yes Yes Yes Credit Card (automatic) 7.338869531 187.656013 1234.244324 2 3 3 5 4 2 3 2 53 D805597 e0ad6b64-9210-4998-aa82-05b6bc33873f 4cc1941f0eddeb8b3d2d94de19b2769c Transylvania LA East Carroll 71286 32.6644 -91.19645 456 Suburban America/Chicago Diagnostic radiographer 1 59 38391.55 Never Married Female No 4.210347704 8 0 0 No Month-to-month No No Fiber Optic Yes No No No Yes No Yes No Yes Electronic Check 5.486534225 149.962093 727.3731845 4 4 4 3 4 2 5 4 54 R189303 ccb06917-57e1-4595-94f7-4a54b1f0ec9d 7b4e1744bba082cf026b16878ed94b79 Jolley IA Calhoun 50551 42.47954 -94.75105 94 Urban America/Chicago Doctor, general practice 0 31 44137.6 Separated Female Yes 11.13960534 6 0 0 Yes One year No No Fiber Optic Yes Yes No No No No Yes No Yes Credit Card (automatic) 1.55278221 169.944668 452.8721504 4 4 3 4 4 4 2 3 55 N142854 d207e3a8-6ae6-4567-9b5a-7ae8d8530d0c d2b5848d8ef49e1b6b68415e597d46e7 Miller NE Buffalo 68858 40.95947 -99.37702 315 Urban America/Chicago Insurance risk surveyor 0 51 14364.65 Widowed Male Yes 9.03738856 11 2 0 No Month-to-month Yes No Fiber Optic Yes Yes Yes No Yes No No No Yes Mailed Check 12.07970325 160.00128 1296.674543 4 2 4 5 4 3 3 4 56 F522569 01051fb5-00df-4a7e-a284-b152a0b5ea47 3fc7c0a5a88aa3796474660502fbb05f Welch WV McDowell 24801 37.44262 -81.57762 4969 Suburban America/New_York Heritage manager 1 58 62359.91 Separated Female No 10.91594785 11 1 1 No One year No No DSL Yes No No No Yes No Yes No No Mailed Check 13.98666226 129.962093 1840.628269 3 4 3 6 3 4 5 3 57 L384793 79c4473e-fd88-46bc-a276-e21783d598eb 019a544457407fa24e77f7dfa224f2f7 Alvin TX Brazoria 77511 29.38147 -95.24205 46869 Suburban America/Chicago Legal executive 1 73 82634.86 Married Male Yes 8.387624096 11 0 1 No Month-to-month No No DSL Yes No No Yes No No No No Yes Electronic Check 9.563362997 114.950905 1324.330108 5 4 3 5 3 3 5 4 58 C101444 5ed9b266-29a0-489e-88e0-59f9a0b39e49 8518b3cdfe3bf58e2b262a154d64df01 Goleta CA Santa Barbara 93117 34.49127 -120.08222 58431 Rural America/Los_Angeles Professor Emeritus 1 33 15690.25 Widowed Female No 11.4487514 11 0 1 No Two Year No No None Yes No Yes No Yes Yes No Yes Yes Mailed Check 6.732948946 142.515576 958.1397059 4 5 5 5 4 4 4 5 59 E987442 626e918e-7e34-4b0a-a92c-bd60db4b6173 0091e1705b9d932e9ff7aaacac8738f6 Racine WI Racine 53403 42.68864 -87.82515 26182 Suburban America/Chicago Radio producer 1 58 14568.7 Separated Male Yes 10.22013704 15 2 0 No Two Year Yes No None Yes Yes No No No Yes Yes Yes Yes Credit Card (automatic) 15.72071086 220.14775 1868.864027 3 5 3 2 4 5 5 4 60 L54611 f16fa219-d489-4a93-b261-5dc4dc99e564 93c054f671b490bbe2c99b4c12337e92 Chauncey GA Dodge 31011 32.12693 -83.06218 694 Rural America/New_York Barrister's clerk 0 49 29207.46 Never Married Female No 8.425458142 9 1 0 No Two Year No No Fiber Optic Yes No No No Yes No No No Yes Electronic Check 7.233605493 124.97808 676.712908 5 5 3 4 4 5 5 4 61 L85018 97f617e1-f6f2-49c3-b317-542d7bb05d1c 97f71f48649e7ee23765f054867265a4 Porum OK Muskogee 74455 35.3619 -95.26371 3093 Suburban America/Chicago Engineer, automotive 1 26 55319.23 Never Married Female No 8.386280619 12 1 0 No One year No Yes DSL Yes No Yes Yes No Yes No Yes Yes Mailed Check 2.871017037 164.964681 1086.990308 5 5 4 4 3 4 4 4 62 O942919 176d7713-b919-41c3-badf-676dc1abbf07 5b5794d3dbb6f9fed1f9205863915cbf Delavan IL Tazewell 61734 40.37342 -89.52646 2765 Urban America/Chicago Recruitment consultant 1 25 81831.49 Widowed Male No 9.565924291 10 3 0 No Month-to-month Yes No Fiber Optic Yes Yes No No Yes Yes Yes No No Electronic Check 2.344875382 194.976769 704.7540406 3 4 3 2 3 4 2 4 63 I167253 d73797a9-ae43-4fe0-8f43-c7db6456eee8 4025058646046e514a7c7e4e6202226e Harveys Lake PA Luzerne 18618 41.37478 -76.03486 3757 Rural America/New_York Commercial horticulturist 0 42 100076.65 Separated Female Yes 10.79349982 14 3 0 No Month-to-month Yes No DSL Yes No Yes Yes No No No Yes Yes Mailed Check 7.960490246 152.455519 1416.444695 5 5 5 4 3 2 4 4 64 R110564 9b56af1d-ccae-418c-a7e3-f1bc3964d09c 2c8f07dff8efc7ab26276074824df9c7 Eagar AZ Apache 85925 34.088 -109.3232 4938 Rural America/Phoenix Pharmacist, community 1 81 69965.65 Widowed Male No 6.081975067 15 0 0 No Month-to-month No Yes None Yes No No No Yes No No No No Mailed Check 12.25062113 92.5018 1095.950634 5 5 5 3 4 3 2 5 65 M278354 7a1d7d58-f80c-47a6-933b-cf5e77d057a7 ceaf5a70e8500cb64025ddddebab1fab Aulander NC Hertford 27805 36.20782 -77.08433 3761 Suburban America/New_York Forest/woodland manager 1 43 34883.69 Divorced Female No 13.55065547 13 0 0 Yes Month-to-month No Yes None Yes No No No Yes No No Yes Yes Electronic Check 9.634946079 127.488728 1083.610268 4 4 4 3 4 4 3 3 66 W604879 65a865fb-9376-4428-b1ee-0049a7c55176 1d0e697f5bc6a63125b54fd3d61bbd99 Mokelumne Hill CA Calaveras 95245 38.29793 -120.62131 1851 Urban America/Los_Angeles Designer, graphic 2 63 45634.2 Never Married Male No 9.470419821 14 1 0 No Two Year Yes No DSL Yes Yes No Yes Yes No Yes No No Bank Transfer(automatic) 7.916533093 184.963371 1570.641337 3 4 3 3 4 4 4 3 67 X345797 71895f94-c853-4e94-85ef-3fab52301614 306759d64ba79dfc8b2134c0c716f2bd Wheeling IL Cook 60090 42.1295 -87.92197 38476 Suburban America/Chicago Civil engineer, consulting 3 70 10964.73 Married Female Yes 6.194985909 11 0 1 No Month-to-month Yes No Fiber Optic Yes Yes No No Yes Yes Yes Yes Yes Bank Transfer(automatic) 4.471254161 265.146969 1007.425553 3 3 2 4 4 3 3 3 68 N189653 941c7516-20c5-47d4-a063-5f3dc7bfc47a ee5479f4dca3d8278204dd1897e44553 Nevada MO Vernon 64772 37.82945 -94.33173 13547 Urban America/Chicago Science writer 1 78 22365.28 Widowed Male Yes 10.80393657 19 1 0 No Month-to-month No No Fiber Optic Yes No No No No No Yes Yes No Bank Transfer(automatic) 10.79028074 207.609354 1323.028419 4 4 3 6 2 5 7 4 69 M562102 df3742f5-e551-4ecc-90fe-eea710b6d9fc 5c597d4d20168d90ddf8f5abf63c999d Slaton TX Lubbock 79364 33.45204 -101.64305 7801 Urban America/Chicago Health and safety inspector 0 48 52636.6 Separated Male No 7.903964267 17 0 1 No Month-to-month Yes Yes DSL Yes Yes Yes No No No Yes No No Electronic Check 12.24190682 152.462354 1808.730555 3 1 2 2 5 2 2 1 70 X67911 14e03b7a-04b1-4dae-8c20-41ad7e3f69b1 46bd163d500568caa0e2de057b8ef4e3 Gillette WY Campbell 82716 44.48072 -105.68542 17823 Suburban America/Denver Administrator, Civil Service 1 63 19048.13 Separated Female Yes 11.27443693 18 2 1 Yes Month-to-month Yes No DSL Yes No No No Yes No Yes Yes Yes Credit Card (automatic) 2.203859443 200.132293 1120.241472 4 4 4 5 4 4 4 3 71 O834546 eeeb8da7-926a-44a7-8d86-68f1eecc7055 fffe3f210b76a8d6e7a1ec383b766260 Roseburg OR Douglas 97470 43.22975 -123.23433 20269 Urban America/Los_Angeles Technical sales engineer 4 60 42784.82 Married Female Yes 6.129509309 12 0 0 No One year Yes Yes None Yes No Yes Yes No Yes Yes Yes No Electronic Check 23.92132231 212.655686 2676.789803 5 5 6 5 3 7 5 5 72 I493092 819fd181-eecf-497c-a453-ada4517d9c06 f858b8ce89fb8cf960dabc54d3561c12 North Royalton OH Cuyahoga 44133 41.31414 -81.74522 30225 Rural America/New_York Special educational needs teacher 0 87 77996.88 Divorced Male Yes 7.969277912 17 0 0 Yes One year Yes No DSL Yes Yes No Yes No Yes Yes Yes Yes Bank Transfer(automatic) 3.7311787 255.119794 1268.086909 3 3 4 5 4 3 5 4 73 H787644 2456727b-d909-43a0-9f62-d2e57c9ef757 8969d2ad6edcb6f6b3c03903bbf6f842 Cadiz OH Harrison 43907 40.25609 -81.02123 5760 Suburban America/New_York Outdoor activities/education manager 3 54 47507.3 Married Female No 4.368120492 9 0 0 No Two Year Yes No Fiber Optic Yes No No Yes No No No No Yes Bank Transfer(automatic) 7.980015371 134.950905 822.6771666 2 3 3 3 5 2 2 3 74 N582710 867d0b20-ffdd-478b-b5e8-ad80ca7ff50e fea9671289fa6d8794f72bf3c1b3e55b Homestead MT Sheridan 59242 48.43505 -104.4389 61 Urban America/Denver Civil engineer, consulting 3 35 7724.73 Divorced Male Yes 6.258356398 9 0 1 No Month-to-month Yes No Fiber Optic Yes No No Yes Yes Yes Yes Yes Yes Electronic Check 9.190009376 255.137219 1577.915603 4 3 2 3 3 3 3 4 75 T689606 fd741cf9-5663-472f-94e8-5977c1499a74 b412a5cf1e6e60c5f8b9b3fac56083dd San Diego CA San Diego 92131 32.89431 -117.08013 34359 Urban America/Los_Angeles Maintenance engineer 1 70 1509.52 Never Married Male Yes 12.92303286 16 0 0 No Month-to-month No No Fiber Optic Yes Yes No Yes Yes No No Yes No Bank Transfer(automatic) 2.378630018 214.966286 648.7471776 4 4 4 5 2 3 4 4 76 E330599 99348a66-489f-42a7-894a-178bc1ba030c 2ffb137d5a923efa638cda02537eb25a Inglewood CA Los Angeles 90303 33.9381 -118.3323 24693 Rural America/Los_Angeles Sports therapist 2 67 81124.71 Married Female Yes 12.43301273 13 0 0 No One year Yes Yes DSL Yes Yes Yes Yes Yes Yes Yes Yes Yes Electronic Check 5.43464555 270.160419 1648.102673 3 3 4 3 5 3 3 4 77 C641592 77618d0f-b884-47ba-ad1b-7029b27512f8 9c7d828228996c8d9400cd17a3c355f5 Catlett VA Fauquier 20119 38.6184 -77.62493 4235 Rural America/New_York Engineer, communications 4 46 11795.57 Widowed Female Yes 7.607663559 11 0 0 No Two Year No No None No No No No Yes Yes Yes Yes Yes Electronic Check 1.092361222 200.165175 768.0874545 1 1 2 2 3 1 3 2 78 E24772 6f3cb471-a76a-480a-8042-cb64a5a43175 7b779f43153c7a475fdf4f75dad470aa Rockfield KY Warren 42274 36.94686 -86.60235 2193 Urban America/Chicago Oceanographer 1 50 74794.84 Never Married Male No 7.442789019 10 2 1 No Two Year No No DSL Yes No No Yes No No No Yes No Bank Transfer(automatic) 20.13981517 149.937833 2426.030626 2 2 3 4 3 3 4 4 79 A889769 10eabf96-a37f-457f-b30b-b14113ea9ef5 609f92fd8000baf6c8f016013fe60afb Concord IL Morgan 62631 39.82437 -90.36129 273 Rural America/Chicago Archaeologist 1 24 19038.78 Never Married Female Yes 13.04429298 18 1 0 No Month-to-month No Yes Fiber Optic Yes Yes No No No No Yes Yes Yes Mailed Check 24.64873861 240.114868 2660.503932 4 4 5 1 4 4 3 3 80 G391761 31eeb895-3c43-4aa5-91de-59b6782b4467 ec244815da702eef604eb33e0b3584d6 Little Falls MN Morrison 56345 45.98881 -94.37486 14557 Rural America/Chicago Personal assistant 0 20 79167.77 Divorced Male No 14.89762162 17 0 0 No Month-to-month No Yes Fiber Optic Yes No Yes No Yes No No No Yes Credit Card (automatic) 9.677739656 127.495766 1129.549713 3 4 3 4 5 4 3 3 81 A606860 67203a0f-8d33-4cae-b0af-f090e77b213a 7c3d23d2690d1d49f4dbaa9835f7857e Orlando FL Orange 32804 28.57725 -81.39726 17653 Urban America/New_York Animal nutritionist 1 29 37428.26 Married Female Yes 13.79387475 13 2 1 No One year Yes No DSL Yes No No No No No Yes Yes No Electronic Check 9.82814183 187.609354 1771.286466 3 4 3 2