ok here can you help me please!

p2.2-submission template (this is the assignment that I need you to answer)

p2.2 solved but need fixing ( I did the assignment but needs requirements so I am attaching it so if you can fix it, its the same assignment)

\fProject 2.2: Recommend a City Note that this project is a continuation from Project 2.1: Data Cleanup. You must meet specifications for Project 2.1 before you can continue on with this Project 2.2 Step 1: Linear Regression Create a linear regression model off your training set and present your model. Visualizations are highly encouraged in this section. Important: Make sure you have dealt with outliers and removed one city from your training set. You should have 10 rows of data before you begin modeling the dataset. Build a linear regression model to help you predict total sales. At the minimum, answer these questions: 1. How and why did you select the predictor variables (see supplementary text) in your model? You must show that each predictor variable has a linear relationship with your target variable with a scatterplot. 2. Explain why you believe your linear model is a good model. You must justify your reasoning using the statistical results that your regression model created. . For each variable you selected, please justify how each variable is a good fit for your model by using the p-values and Rsquared values that your model produced. 3. What is the best linear regression equation based on the available data? Each coefficient should have no more than 2 digits after the decimal (ex: 1.28) Step 2: Analysis Use your model results to provide a recommendation. At the minimum, answer this question: 1. Which city would you recommend and why did you recommend this city? Project 2.1: Data Cleanup Step 1: Business and Data Understanding Provide an explanation of the key decisions that need to be made. (250 word limit) Key Decisions: Answer these questions 1. What decisions needs to be made? Answer: The decision that we are making is related to expanding Pawdacity's stores and building a 14th pet store in Wyoming for Pawdacity. One of the main issues I am trying to uncover is where the best location is to build the store based on projected target revenue of $200,000 in their first year. 2. What data is needed to inform those decisions? Answer: The data I need to collect to inform my decision are as follows: 1. The sales data from 2010 of Pawdacity stores and their respective cities. 2. 2010 Census population data for cities in which the stores were located. 3. Total households with people under 18. 4. Land Area 5. Population Density of those cities. 6. Total Families living in those cities. 7. Number of pets owned by families in the area. 8. Types of pets owned in the area we want to build a store. 9. Number of parks and communal pet parks. 10. Data based on current and local marketing budget spent per city on current stores. 11. Data for the amount of people projected to be living a city we are planning on opening the store. 12. Competitors market data. Step 2: Building the Training Set Build your training set given the data provided to you. Your column sums of your dataset should match the sums in the table below. In addition provide the averages on your data set here to help reviewers check your work. You should round up to two decimal places, ex: 1.24 Column Sum Average Census Population 233,862 Total Pawdacity Sales 21,260.18 3,773,304 343,027.64 Households with Under 18 34,064 3,096.73 Land Area 33,071 3,006.49 Population Density 63 5.71 Total Families 69,653 6,332.07 Step 3: Dealing with Outliers Answer these questions Are there any cities that are outliers in the training set? Which outlier have you chosen to remove or impute? Because this dataset is a small data set (11 cities), you should only remove or impute one outlier. Please explain your reasoning. Answer: Land_Area - There does not appear to be outliers in this data set based on the interquartile analysis. Households_Under_18 - There does not appear to be outliers in this data set, all data seems to follow that for Pawdacity, the more 18 year olds you have in your house, the higher the likelihood that you will purchase items from the store. Population_Density - With population density, there is one outlier for the largest city (Cheyenne) in the state which has a population density of 20.34, however it seems to follow the relationship between population_density and sales based on the scatter plot, and therefore does not appear to skew the data. Additionally, by keeping the this in the dataset we have a more robust model for modelling big cities in the future. Total_Families - There is one outlier in total_families (Casper), however the city of Casper does not skew the data in terms of sales and due to the limited number of data points I have decided to keep it. 2010_Census - Within the 2010 Census data, using the interquartile range analysis, there appears to be one outlier than we can exclude, and that is the city of (Gillette). While the population data does not seem to skew the model, the sum_sales for this city appears to be the clearest outlier. Due to the fact that the sales is an outlier in such a small population relatively, leaving this data point in the model has the potential to go against our logic of larger city/larger revenue Project Overview This project is a continuation of Project 2.1 regarding trying to find the best city to expand for Pawdacity's newest pet store. Scenario Pawdacity is a leading pet store chain in Wyoming with 13 stores throughout the state. This year, Pawdacity would like to expand and open a 14th store. Your manager has asked you to perform an analysis to recommend the city for Pawdacity's newest store, based on predicted yearly sales. How Do I Complete this Project? This project uses skills learned throughout the "Multivariable Linear Regression\" lesson. To complete this project: Go through the course Apply the skills learned in the course to solve the business problem given in the project details section. Use our guidelines and rubric to help build your project. When you're ready, submit it to us for review using the submission template found in the supporting materials section. Skills Required In order to complete this project, you must be able to: Choose appropriate predictor variables Analyze for correlations between predictor variables Build a linear model The Business Problem Pawdacity is a leading pet store chain in Wyoming with 13 stores throughout the state. This year, Pawdacity would like to expand and open a 14th store. Your manager has asked you to perform an analysis to recommend the city for Pawdacity's newest store, based on predicted yearly sales. In the first part, you've already cleaned up the dataset and dealt with outliers. In this project, you will take this dataset that you cleaned up and use this dataset to train a linear regression model in order to predict sales Here are the criterias given to you in choosing the right city: 1 2 3 4 5 The new store should be located in a new city. That means there should be no existing stores in the new city. The total sales for the entire competition in the new city should be less than $500,000 The new city where you want to build your new store must have a population over 4,000 people (based upon the 2014 US Census estimate). The predicted yearly sales must be over $200,000. The city chosen has the highest predicted sales from the predicted set. Steps to Success Step 1: Build a Linear Regression Model Analyze the dataset you created in Project 2.1 and look at the distribution of your data. You can create histograms to look at each of your continuous and categorical data to determine the nature of the data you're working with. Important: Make sure you have dealt with outliers and removed one city from your training set. You should have 10 rows of data before you begin modeling the dataset. Build a linear regression model to help you predict total sales. Step 2: Perform the Analysis Use your regression model to calculate predicted sales for all of the cities and use the criteria given to you to make a recommendation. Data Please refer to the Supporting Materials section in Project 2.1 to access the data you need to complete this project. qattachments_7c86bbcff33abc7dbf9b1f88b0564ea5512d4aee NAME Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity Pawdacity ADDRESS 509 Fort St # A 601 SE Wyoming Blvd Unit 252 1400 Dell Range Blvd 3769 E Lincolnway 2625 Big Horn Ave 123 S 2nd St 932 Main St 900 Camel Dr 200 E Lakeway Rd 180 S Bent St 512 E Main St 2706 Commercial Way 1842 Sugarland Dr Ste 113 CITY Buffalo Casper Cheyenne Cheyenne Cody Douglas Evanston Gillette Gillette Powell Riverton Rock Springs Sheridan Page 1 STATE ZIP January February March WY 82834 16200 13392 14688 WY 82609 29160 21600 27000 WY 82009 47520 44280 47088 WY 82001 32400 26352 31968 WY 82414 19440 15984 19008 WY 82633 16200 13392 14688 WY 82930 24840 21168 21600 WY 82716 23760 20952 24192 WY 82718 23760 20844 24192 WY 82435 20520 17928 20304 WY 82501 27000 22032 28512 WY 82901 21600 19872 22248 WY 82801 27000 26352 28080 qattachments_7c86bbcff33abc7dbf9b1f88b0564ea5512d4aee April May June July August September October November December ### 18360 14040 12960 19224 15984 13392 13176 16848 ### 29160 27216 25488 25704 22896 25272 28944 27648 ### 43200 45144 44064 45360 47736 42984 44712 47304 ### 30456 32832 29808 32184 30780 31536 30024 32616 ### 16632 17496 18792 20304 19224 18144 18576 16632 ### 18360 14040 12960 19224 15984 29808 17496 18792 ### 24192 24624 25488 25704 22032 21168 25920 24840 ### 21168 20520 20304 24624 24192 23760 22032 22464 ### 21168 21384 21816 22464 24840 24408 20952 22248 ### 21600 17928 18144 18576 20304 21168 17496 18792 ### 25920 24192 25056 22896 25488 26352 26784 22248 ### 17496 24840 22464 21816 21384 20304 22032 18576 ### 21168 29376 25920 20304 33696 23760 25056 25488 Page 2 qattachments_79381a2323a77687321b3a8f24b1bde504a26f19 City|County Afton|Lincoln Albin|Laramie Alpine|Lincoln Baggs|Carbon Bairoil|Sweetwater Bar Nunn|Natrona Basin ?|Big Horn Bear River|Uinta Big Piney|Sublette Buffalo ?|Johnson Burlington|Big Horn Burns|Laramie Byron|Big Horn Casper ?|Natrona Cheyenne ??|Laramie Chugwater|Platte Clearmont|Sheridan Cody ?|Park Cokeville|Lincoln Cowley|Big Horn Dayton|Sheridan Deaver|Big Horn Diamondville|Lincoln Dixon|Carbon Douglas ?|Converse Dubois|Fremont East Thermopolis|Hot Springs Edgerton|Natrona Elk Mountain|Carbon Evanston ?|Uinta Evansville|Natrona Fort Laramie|Goshen Frannie|Park Gillette ?|Campbell Glendo|Platte Glenrock|Converse Grand Encampment|Carbon Granger|Sweetwater Green River ?|Sweetwater Greybull|Big Horn Guernsey|Platte Hanna|Carbon Hartville|Platte Hudson|Fremont Hulett|Crook Jackson ?|Teton Kaycee|Johnson Kemmerer ?|Lincoln Kirby|Hot Springs La Barge|Lincoln La Grange|Goshen Lander ?|Fremont Page 1 qattachments_79381a2323a77687321b3a8f24b1bde504a26f19 Laramie ?|Albany Lingle|Goshen Lost Springs|Converse Lovell|Big Horn Lusk ?|Niobrara Lyman|Uinta Manderson|Big Horn Manville|Niobrara Marbleton|Sublette Medicine Bow|Carbon Meeteetse|Park Midwest|Natrona Mills|Natrona Moorcroft|Crook Mountain View|Uinta Newcastle ?|Weston Opal|Lincoln Pavillion|Fremont Pine Bluffs|Laramie Pine Haven|Crook Pinedale ?|Sublette Powell|Park Ranchester|Sheridan Rawlins ?|Carbon Riverside|Carbon Riverton|Fremont Rock River|Albany Rock Springs|Sweetwater Rolling Hills|Converse Saratoga|Carbon Sheridan ?|Sheridan Shoshoni|Fremont Sinclair|Carbon Star Valley Ranch|Lincoln Sundance ?|Crook Superior|Sweetwater Ten Sleep|Washakie Thayne|Lincoln Thermopolis ?|Hot Springs Torrington ?|Goshen Upton|Weston Van Tassell|Niobrara Wamsutter|Sweetwater Wheatland ?|Platte Worland ?|Washakie Wright|Campbell Yoder|Goshen Page 2 qattachments_79381a2323a77687321b3a8f24b1bde504a26f19 2014 Estimate 1,968 185 845 439 107 2,735 1,312 521 538 4,615 332 305 609 40,086 62,845 216 142 9,740 542 718 794 184 740 97 6,423 998 252 199 196 12,190 2,831 227 162 31,971 201 2,583 443 139 12,630 1,868 1,193 831 62 462 400 10,449 260 2,732 93 553 455 7,642 Page 3 qattachments_79381a2323a77687321b3a8f24b1bde504a26f19 32,081 467 4 2,404 1,578 2,077 117 93 1,114 277 327 412 3,690 1,036 1,304 3,513 99 240 1,146 498 1,958 6,407 943 9,227 53 10,953 245 24,045 439 1,692 17,916 655 424 1,541 1,239 332 253 364 3,020 6,736 1,104 15 503 3,659 5,366 1,847 161 Page 4 qattachments_79381a2323a77687321b3a8f24b1bde504a26f19 2010 Census 1,911 181 828 440 106 2,213 1,285^[4] 518 552 4,585 288 301 593 35,316 59,466 212 142 9,520 535 655 757 178 737 97 6,120 971 254 195 191 12,359 2,544 230 157 29,087 205 2,576 450 139 12,515 1,847 1,147 841 62 458 383 9,577 263 2,656 92 551 448 7,487 Page 5 qattachments_79381a2323a77687321b3a8f24b1bde504a26f19 30,816 468 4 2,360 1,567 2,115 114 95 1,094 284 327 404 3,461 1,009 1,286 3,532 96 231 1,129 490 2,030 6,314 855 9,259 52 10,615 245 23,036 440 1,690 17,444 649 433 1,503 1,182 336 260 366^[5] 3,009 6,501 1,100 15 451 3,627 5,487 1,807 151 Page 8 qattachments_d0b6b0856dcc04073c8b78bafe5712c957a9ca45 BUSINESS NAME Mile High Mobile Pet LLC Pets City Inc Petco Animal Sups Stores Inc Pet-A-Care Muddy Paws Pet Salon Prossers Feed and Seed L L C Pet Pals Inc of Goshen County Don Bruner Sales LLC Pals For Pets Inc Pet Barn Comfy Critters LLC Pampered Pets Lander Pet & Ranch Supply Frontier Feathers Inc Tails of City K & M Pet Products and Svcs Dog World Petco Animal Supplies Inc Edna Grabow L and C Pets and Gifts LLC All Gods Creatures Camelot Pet Castle Joes Pet Depot Pet Food Outlet Joes Pet Depot Precious Pets LLC Zoobecks Inc Summit Pets Jackson Hole Feed and Pet Thunderpaws Pet Containment Big Als Pet Stop Riverbend Pets LLC PHYSICAL CITY NAME SALES VOLUME Cheyenne 300000 Cheyenne 640000 Cheyenne 0 Cheyenne 81000 Laramie 76000 Wheatland 77000 Torrington 126991 Torrington 750000 Saratoga 83000 Worland 69000 Worland 100000 Dubois 55000 Lander 108197 Lander 44000 Casper 110000 Casper 51000 Casper 49000 Casper 0 Douglas 96000 Evansville 210000 Gillette 450000 Gillette 230000 Gillette 0 Gillette 450000 Rock Springs 0 Rock Springs 100000 Rock Springs 890000 Evanston 89000 Jackson 72000 Jackson 110000 Kemmerer 69000 Alpine 70000 Page 1 qattachments_d0b6b0856dcc04073c8b78bafe5712c957a9ca45 CASS_LastLine Cheyenne, WY 82007-3528 Cheyenne, WY 82009-4851 Cheyenne, WY 82009-4945 Cheyenne, WY 82009-1009 Laramie, WY 82070-8979 Wheatland, WY 82201-2901 Torrington, WY 82240-3516 Torrington, WY 82240-3516 Saratoga, WY 82331 Worland, WY 82401-2715 Worland, WY 82401-3319 Dubois, WY 82513 Lander, WY 82520-3105 Lander, WY 82520-3033 Casper, WY 82601-2539 Casper, WY 82601-3035 Casper, WY 82601-2442 Casper, WY 82609-2319 Douglas, WY 82633-9231 Evansville, WY 82636 Gillette, WY 82716-2919 Gillette, WY 82716-1704 Gillette, WY 82716-3742 Gillette, WY 82718-6330 Rock Springs, WY 82901-6738 Rock Springs, WY 82901-4574 Rock Springs, WY 82901-5105 Evanston, WY 82930-3533 Jackson, WY 83001-8698 Jackson, WY 83001-8809 Kemmerer, WY 83101-2900 Alpine, WY 83128 Page 2 qattachments_03f060a58b11f7275d94645d3d6fbbddcbec32f9 City County Land Area Households with Under 18 Population Density Laramie Albany 2513.745235 2075 5.19 Rock River Albany 200.444 165 0.41 Basin Big Horn 543.9513043 250 0.66 Burlington Big Horn 137.6462142 63 0.17 Byron Big Horn 252.4895917 116 0.31 Cowley Big Horn 297.6806681 137 0.36 Deaver Big Horn 76.28585366 35 0.09 Greybull Big Horn 691.22612 318 0.84 Lovell Big Horn 809.453936 372 0.98 Manderson Big Horn 48.5078526 22 0.06 Gillette Campbell 2748.8529 4052 5.8 Wright Campbell 262.0087853 386 0.55 Baggs Carbon 253.2403224 62 0.06 Dixon Carbon 55.95515096 14 0.01 Elk Mountain Carbon 113.0640164 28 0.03 Grand EncampmentCarbon 255.5477513 63 0.06 Hanna Carbon 479.3683551 118 0.12 Medicine Bow Carbon 159.7894517 39 0.04 Rawlins Carbon 5322.661628 1307 1.32 Riverside Carbon 30.573433 8 0.01 Saratoga Carbon 976.0424271 240 0.24 Sinclair Carbon 244.587464 60 0.06 Douglas Converse 1829.4651 832 1.46 Glenrock Converse 838.818 381 0.67 Lost Springs Converse 89.244 41 0.07 Rolling Hills Converse 197.6670791 90 0.16 Hulett Crook 359.8361172 106 0.32 Moorcroft Crook 931.9755436 273 0.82 Pine Haven Crook 447.995966 131 0.39 Sundance Crook 1114.592373 327 0.98 Dubois Fremont 437.0735045 244 0.21 Hudson Fremont 202.3326243 113 0.1 Lander Fremont 3346.80934 1870 1.63 Pavillion Fremont 105.1078568 59 0.05 Riverton Fremont 4796.859815 2680 2.34 Shoshoni Fremont 286.8568592 160 0.14 Fort Laramie Goshen 62.78386652 41 0.17 La Grange Goshen 125.8443139 81 0.35 Lingle Goshen 129.1632849 84 0.35 Torrington Goshen 1599.818493 1034 4.39 Yoder Goshen 44.52952647 29 0.12 East Thermopolis Hot Springs 150.0841605 38 0.18 Kirby Hot Springs 55.38820208 14 0.07 Thermopolis Hot Springs 919.68149 233 1.1 Buffalo Johnson 3115.5075 746 1.55 Kaycee Johnson 221.5472 53 0.11 Albin Laramie 7.685902049 37 0.1 Burns Laramie 12.67135203 60 0.17 Cheyenne Laramie 1500.1784 7158 20.34 Pine Bluffs Laramie 47.61104729 227 0.65 Afton Lincoln 853.8066496 497 0.93 Alpine Lincoln 366.5988917 213 0.4 Page 1 qattachments_03f060a58b11f7275d94645d3d6fbbddcbec32f9 Cokeville Diamondville Kemmerer La Barge Opal Star Valley Ranch Thayne Bar Nunn Casper Edgerton Evansville Midwest Mills Lusk Manville Van Tassell Cody Frannie Meeteetse Powell Chugwater Glendo Guernsey Hartville Wheatland Clearmont Dayton Ranchester Sheridan Big Piney Marbleton Pinedale Bairoil Granger Green River Rock Springs Superior Wamsutter Jackson Bear River Evanston Lyman Mountain View Ten Sleep Worland Newcastle Upton Lincoln Lincoln Lincoln Lincoln Lincoln Lincoln Lincoln Natrona Natrona Natrona Natrona Natrona Natrona Niobrara Niobrara Niobrara Park Park Park Park Platte Platte Platte Platte Platte Sheridan Sheridan Sheridan Sheridan Sublette Sublette Sublette Sweetwater Sweetwater Sweetwater Sweetwater Sweetwater Sweetwater Teton Uinta Uinta Uinta Uinta Washakie Washakie Weston Weston 235.1439045 321.0451833 1185.264109 239.9161978 42.95063939 668.554902 157.9195226 208.5732199 3894.3091 15.17589424 215.8942543 31.41943934 281.4022601 2211.10042 144.8510854 23.36307829 2998.95696 67.60091729 136.4537034 2673.57455 84.44664041 78.58229038 466.4113056 24.23931345 1430.51045 18.04795251 100.9160162 119.8536565 1893.977048 710.4893573 1210.92214 2585.758665 29.4598297 38.27024605 3477.361206 6620.201916 91.40806971 138.4887321 1757.6592 66.04092655 999.4971 263.2764001 165.2924534 100.7925165 1294.105755 1193.129216 572.6548668 137 187 690 140 25 389 92 417 7788 30 432 63 563 205 13 2 1403 32 64 1251 39 36 216 11 662 25 141 167 2646 186 316 676 18 23 2113 4022 56 84 1078 98 1486 391 246 46 595 386 185 Page 2 0.26 0.35 1.29 0.26 0.05 0.73 0.17 0.6 11.16 0.04 0.62 0.09 0.81 0.79 0.05 0.01 1.82 0.04 0.08 1.62 0.17 0.16 0.93 0.05 2.86 0.09 0.48 0.57 8.98 0.31 0.53 1.12 0.01 0.02 1.46 2.78 0.04 0.06 2.36 0.33 4.95 1.3 0.82 0.17 2.18 1.47 0.71 qattachments_03f060a58b11f7275d94645d3d6fbbddcbec32f9 Total Families 4668.93 372.3 566.43 143.34 262.93 309.98 79.44 719.8 842.91 50.51 7189.43 685.27 129.53 28.62 57.83 130.71 245.19 81.73 2722.43 15.64 499.23 125.1 1744.08 799.67 85.08 188.44 255.03 660.52 317.51 789.95 506.29 234.37 3876.81 121.75 5556.49 332.28 100.01 200.47 205.76 2548.5 70.94 101.85 37.59 624.1 1819.5 129.39 74.87 123.43 14612.64 463.76 1040.41 446.72 Page 3 qattachments_03f060a58b11f7275d94645d3d6fbbddcbec32f9 286.54 391.21 1444.32 292.35 52.34 814.67 192.43 843.88 8756.32 61.4 873.51 127.12 1138.55 548.14 35.91 5.79 3515.62 79.25 159.96 3134.18 103.56 96.37 572 29.73 1754.34 57.55 321.81 382.2 6039.71 384.8 655.83 1400.43 33.7 43.77 3977.4 7572.18 104.55 158.4 2313.08 179.24 2712.64 714.53 448.6 106.26 1364.32 957.04 459.34 Page 4