Excel Data USERS PEDESTRIANS CARS IN BANK DF BANK DF SAME ATM DF DIFF ATM POP AVG INC VIOLENT LOCATION 3038 461 3124 1 0 4 4 4 8 4000 54000 2 RESIDENTIAL 1531 1123 5186 0 0 7 0 4 0 13000 58000 19 MALL 4500 2751 14392 0 1 1 1 7 14000 82000 6 CAMPUS 3296 1206 5730 0 6 5 3 7 0 4 10000 68000 19 BUSINESS 4759 2442 13473 0 4 7 2 9 0 13000 86000 7 MALL 387 104 722 0 2 3 2 3 0 4000 43000 29 MALL 2418 1701 11095 0 6 7 2 1 0 1 15000 65000 26 RESIDENTIAL 2677 438 2430 0 6 4 6 0 5000 62000 24 MALL 258 239 1878 0 0 8 0 8 0 7 5000 44000 22 CAMPUS 2857 1395 7088 1 0 3 2 3 5 6000 67000 12 RESIDENTIAL 1688 731 6245 0 4 2 3 5 0 6 6000 61000 33 CAMPUS 600 453 2686 0 5 3 2 2 1 9 5000 78000 24 CAMPUS 2516 502 4223 0 5 7 3 8 0 10000 37000 16 MALL 4919 2185 10623 0 5 3 3 9 1 3 18000 75000 3 CAMPUS 2402 1712 8104 0 1 4 1 4 0 10000 62000 20 MALL 869 805 4372 0 6 2 0 9 0 16000 74000 21 MALL 1196 657 4241 0 4 1 0 4 1 3 7000 60000 18 BUSINESS 2699 899 5801 1 0 3 2 4 6 13000 70000 14 CAMPUS 724 203 2238 0 3 5 3 5 0 3000 64000 36 MALL 1118 224 1604 0 2 4 2 4 1 2 11000 65000 16 BUSINESS 1393 609 3677 0 2 6 2 3 0 1 12000 47000 24 BUSINESS 3609 819 5696 0 6 9 5 0 5 20000 64000 13 CAMPUS 497 484 4775 0 4 1 1 3 2 2 18000 73000 13 RESIDENTIAL 3695 1440 7501 0 6 9 4 2 1 4 10000 71000 6 RESIDENTIAL 718 578 5664 0 6 9 0 8 0 14000 33000 37 MALL 3532 2573 10718 0 0 4 0 4 1 5 11000 77000 7 RESIDENTIAL 98 551 4095 0 2 6 0 5 0 4 11000 71000 34 BUSINESS 3059 1099 5685 0 3 3 3 3 2 1 7000 63000 8 CAMPUS 4351 2245 10071 0 3 2 3 2 0 15000 71000 14 MALL 1909 693 6412 1 0 0 4 1 5 18000 58000 7 BUSINESS 3925 2436 10656 1 0 1 3 2 9 16000 72000 15 CAMPUS 951 745 5704 1 0 1 5 1 2 3000 63000 13 RESIDENTIAL 2367 1341 8017 0 6 2 1 6 0 16000 66000 9 MALL 870 781 4769 1 0 1 9 0 12000 55000 25 MALL 225 259 2985 0 1 9 0 9 2 6 11000 84000 9 RESIDENTIAL 772 670 3904 0 0 9 0 9 2 5 9000 51000 31 CAMPUS 1263 737 3783 0 1 3 1 3 1 6 8000 77000 12 CAMPUS 2105 626 3832 0 3 3 3 3 1 5 10000 49000 26 BUSINESS 678 327 4360 1 0 1 7 1 3 4000 73000 7 RESIDENTIAL 1067 594 6350 0 5 4 2 5 0 5 6000 59000 18 RESIDENTIAL 2242 642 6107 0 5 4 4 4 2 19000 61000 51 BUSINESS 2663 1819 8277 1 0 1 5 1 7 17000 68000 23 BUSINESS 956 435 4186 0 6 5 2 5 0 8 4000 65000 28 BUSINESS 4304 2158 9589 1 0 3 3 1 2 13000 51000 6 CAMPUS 1200 1242 5562 0 0 3 0 3 2 10000 64000 26 CAMPUS 2571 1052 6063 0 4 9 2 7 0 5 20000 84000 2 BUSINESS 2310 1569 7198 0 4 9 1 2 0 13000 59000 11 MALL 172 138 1075 0 0 5 0 5 0 4000 60000 12 MALL 2232 1054 6707 0 1 1 1 1 2 3 17000 66000 5 BUSINESS 3462 746 7441 1 0 3 9 0 1 3000 68000 6 BUSINESS 1703 257 4224 1 0 3 4 9 15000 68000 14 CAMPUS 2143 1186 5287 0 5 9 2 1 0 9000 79000 15 MALL 1178 1061 5793 1 0 0 5 2 7 18000 58000 20 RESIDENTIAL 2426 803 4886 0 3 3 3 2 1 10000 44000 12 CAMPUS 1643 891 7584 0 1 7 1 7 3 2 12000 58000 9 RESIDENTIAL 494 793 5968 0 0 1 0 1 1 5 10000 60000 33 CAMPUS 3024 1577 7855 0 2 3 0 5 0 5 20000 50000 16 BUSINESS 2123 716 5455 1 0 2 1 1 5 12000 54000 7 BUSINESS 2437 1008 4672 1 0 4 1 1 7 12000 57000 25 RESIDENTIAL 1497 600 3311 1 0 4 4 5 13000 60000 35 RESIDENTIAL 5395 2773 15424 0 4 8 3 3 0 20000 55000 10 MALL 3800 434 2407 0 6 7 5 1 4 3000 76000 4 CAMPUS 4070 1636 9700 0 5 7 4 2 1 1 16000 67000 20 BUSINESS 1390 928 5100 0 1 1 0 9 7000 55000 29 BUSINESS 891 378 2106 0 5 1 2 1 4 3000 82000 12 BUSINESS 418 377 4736 1 0 0 3 0 5000 66000 17 MALL 2166 1554 6613 0 1 2 1 2 1 7 17000 50000 17 CAMPUS 2644 1462 7002 0 6 5 2 8 0 13000 66000 15 MALL 3315 2017 9322 0 5 6 2 5 4 2 13000 64000 22 CAMPUS 4051 1507 7042 0 4 7 3 7 3 9 15000 62000 6 CAMPUS 1101 315 2052 0 3 6 1 4 3 6 3000 60000 8 CAMPUS 2280 1429 7907 0 2 7 0 1 2 2 8000 64000 16 CAMPUS 3218 1436 9274 1 0 2 6 3 8 14000 80000 5 CAMPUS 2617 214 2167 0 6 6 4 6 0 4000 64000 9 MALL 2766 1055 6892 0 4 2 1 6 1 6 7000 74000 3 BUSINESS 5488 2243 10437 0 5 6 4 6 3 2 16000 46000 16 RESIDENTIAL 3863 1249 6794 1 0 4 5 3 9 14000 65000 11 CAMPUS QUESTION Scenario You are working for a bank, planning ATM locations To inform your planning, you are analyzing data for current ATMs, in hopes of learning the factors that make an ATM successful (i e , highly used) You have been provided with a file (ATM csv) with data for a number of existing ATMs The variables in the file are USERS the number of weekly users of the ATM This is the variable that we are trying to predict PEDESTRIANS and CARS The number of pedestrians and cars, respectively, that pass by the ATM on a weekly basis IN BANK 1 if the ATM is located in a bank branch, 0 if not DF BANK, DF SAME ATM and DF DIFF ATM the distances (in km) from the closest branch of your bank, the next closest ATM for your bank, and the next closest ATM of a different bank, respectively POP The population in the immediate neighbourhood of the ATM AVG INC The average income of people in the neighbourhood VIOLENT The number of violent crimes committed in the neighbourhood in the last 6 months Page 1 of 3 LOCATION One of BUSINESS (business district of the town city), CAMPUS, MALL, or RESIDENTIAL Question 3 You suspect that there may be a non linear relationship with distance for any given distance, the area covered is a function of the square of distance (Remember r ) Run another regression, keeping the degree 1 variables (i e , the model from Question 1), and adding quadratic (degree 2) terms for each of the 'DF ' variables a Is there evidence for any that any of these variables have a quadratic relationship with USERS Explain your answer Scenario You are working for a bank, planning ATM locations To inform your planning, you are analyzing data for current ATMs, in hopes of learning the factors that make an ATM successful (i e , highly used) You have been provided with a file (ATM csv) with data for a number of existing ATMs The variables in the file are USERS the number of weekly users of the ATM This is the variable that we are trying to predict PEDESTRIANS and CARS The number of pedestrians and cars, respectively, that pass by the ATM on a weekly basis IN BANK 1 if the ATM is located in a bank branch, 0 if not DF BANK, DF SAME ATM and DF DIFF ATM the distances (in km) from the closest branch of your bank, the next closest ATM for your bank, and the next closest ATM of a different bank, respectively POP The population in the immediate neighbourhood of the ATM AVG INC The average income of people in the neighbourhood VIOLENT The number of violent crimes committed in the neighbourhood in the last 6 months Page 1 of 3 LOCATION One of BUSINESS (business district of the town city), CAMPUS, MALL, or RESIDENTIAL Question 3 You suspect that there may be a non linear relationship with distance for any given distance, the area covered is a function of the square of distance (Remember r ) Run another regression, keeping the degree 1 variables (i e , the model from Question 1), and adding quadratic (degree 2) terms for each of the 'DF ' variables a Is there evidence for any that any of these variables have a quadratic relationship with USERS Explain your

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Excel Data: USERS PEDESTRIANS CARS IN_BANK DF_BANK DF_SAME_ATM DF_DIFF_ATM POP AVG_INC VIOLENT LOCATION 3038 461 3124 1 0 4.4 4.8 4000 54000 2 RESIDENTIAL 1531

Excel Data:

USERS	PEDESTRIANS	CARS	IN_BANK	DF_BANK	DF_SAME_ATM	DF_DIFF_ATM	POP	AVG_INC	VIOLENT	LOCATION
3038	461	3124	1	0	4.4	4.8	4000	54000	2	RESIDENTIAL
1531	1123	5186	0	0.7	0.4	0	13000	58000	19	MALL
4500	2751	14392	0	1	1	1.7	14000	82000	6	CAMPUS
3296	1206	5730	0	6.5	3.7	0.4	10000	68000	19	BUSINESS
4759	2442	13473	0	4.7	2.9	0	13000	86000	7	MALL
387	104	722	0	2.3	2.3	0	4000	43000	29	MALL
2418	1701	11095	0	6.7	2.1	0.1	15000	65000	26	RESIDENTIAL
2677	438	2430	0	6	4.6	0	5000	62000	24	MALL
258	239	1878	0	0.8	0.8	0.7	5000	44000	22	CAMPUS
2857	1395	7088	1	0	3.2	3.5	6000	67000	12	RESIDENTIAL
1688	731	6245	0	4.2	3.5	0.6	6000	61000	33	CAMPUS
600	453	2686	0	5.3	2.2	1.9	5000	78000	24	CAMPUS
2516	502	4223	0	5.7	3.8	0	10000	37000	16	MALL
4919	2185	10623	0	5.3	3.9	1.3	18000	75000	3	CAMPUS
2402	1712	8104	0	1.4	1.4	0	10000	62000	20	MALL
869	805	4372	0	6.2	0.9	0	16000	74000	21	MALL
1196	657	4241	0	4.1	0.4	1.3	7000	60000	18	BUSINESS
2699	899	5801	1	0	3.2	4.6	13000	70000	14	CAMPUS
724	203	2238	0	3.5	3.5	0	3000	64000	36	MALL
1118	224	1604	0	2.4	2.4	1.2	11000	65000	16	BUSINESS
1393	609	3677	0	2.6	2.3	0.1	12000	47000	24	BUSINESS
3609	819	5696	0	6.9	5	0.5	20000	64000	13	CAMPUS
497	484	4775	0	4.1	1.3	2.2	18000	73000	13	RESIDENTIAL
3695	1440	7501	0	6.9	4.2	1.4	10000	71000	6	RESIDENTIAL
718	578	5664	0	6.9	0.8	0	14000	33000	37	MALL
3532	2573	10718	0	0.4	0.4	1.5	11000	77000	7	RESIDENTIAL
98	551	4095	0	2.6	0.5	0.4	11000	71000	34	BUSINESS
3059	1099	5685	0	3.3	3.3	2.1	7000	63000	8	CAMPUS
4351	2245	10071	0	3.2	3.2	0	15000	71000	14	MALL
1909	693	6412	1	0	0.4	1.5	18000	58000	7	BUSINESS
3925	2436	10656	1	0	1.3	2.9	16000	72000	15	CAMPUS
951	745	5704	1	0	1.5	1.2	3000	63000	13	RESIDENTIAL
2367	1341	8017	0	6.2	1.6	0	16000	66000	9	MALL
870	781	4769	1	0	1.9	0	12000	55000	25	MALL
225	259	2985	0	1.9	0.9	2.6	11000	84000	9	RESIDENTIAL
772	670	3904	0	0.9	0.9	2.5	9000	51000	31	CAMPUS
1263	737	3783	0	1.3	1.3	1.6	8000	77000	12	CAMPUS
2105	626	3832	0	3.3	3.3	1.5	10000	49000	26	BUSINESS
678	327	4360	1	0	1.7	1.3	4000	73000	7	RESIDENTIAL
1067	594	6350	0	5.4	2.5	0.5	6000	59000	18	RESIDENTIAL
2242	642	6107	0	5.4	4.4	2	19000	61000	51	BUSINESS
2663	1819	8277	1	0	1.5	1.7	17000	68000	23	BUSINESS
956	435	4186	0	6.5	2.5	0.8	4000	65000	28	BUSINESS
4304	2158	9589	1	0	3.3	1.2	13000	51000	6	CAMPUS
1200	1242	5562	0	0.3	0.3	2	10000	64000	26	CAMPUS
2571	1052	6063	0	4.9	2.7	0.5	20000	84000	2	BUSINESS
2310	1569	7198	0	4.9	1.2	0	13000	59000	11	MALL
172	138	1075	0	0.5	0.5	0	4000	60000	12	MALL
2232	1054	6707	0	1.1	1.1	2.3	17000	66000	5	BUSINESS
3462	746	7441	1	0	3.9	0.1	3000	68000	6	BUSINESS
1703	257	4224	1	0	3	4.9	15000	68000	14	CAMPUS
2143	1186	5287	0	5.9	2.1	0	9000	79000	15	MALL
1178	1061	5793	1	0	0.5	2.7	18000	58000	20	RESIDENTIAL
2426	803	4886	0	3.3	3.2	1	10000	44000	12	CAMPUS
1643	891	7584	0	1.7	1.7	3.2	12000	58000	9	RESIDENTIAL
494	793	5968	0	0.1	0.1	1.5	10000	60000	33	CAMPUS
3024	1577	7855	0	2.3	0.5	0.5	20000	50000	16	BUSINESS
2123	716	5455	1	0	2.1	1.5	12000	54000	7	BUSINESS
2437	1008	4672	1	0	4.1	1.7	12000	57000	25	RESIDENTIAL
1497	600	3311	1	0	4	4.5	13000	60000	35	RESIDENTIAL
5395	2773	15424	0	4.8	3.3	0	20000	55000	10	MALL
3800	434	2407	0	6.7	5	1.4	3000	76000	4	CAMPUS
4070	1636	9700	0	5.7	4.2	1.1	16000	67000	20	BUSINESS
1390	928	5100	0	1	1	0.9	7000	55000	29	BUSINESS
891	378	2106	0	5	1.2	1.4	3000	82000	12	BUSINESS
418	377	4736	1	0	0.3	0	5000	66000	17	MALL
2166	1554	6613	0	1.2	1.2	1.7	17000	50000	17	CAMPUS
2644	1462	7002	0	6.5	2.8	0	13000	66000	15	MALL
3315	2017	9322	0	5.6	2.5	4.2	13000	64000	22	CAMPUS
4051	1507	7042	0	4.7	3.7	3.9	15000	62000	6	CAMPUS
1101	315	2052	0	3.6	1.4	3.6	3000	60000	8	CAMPUS
2280	1429	7907	0	2.7	0.1	2.2	8000	64000	16	CAMPUS
3218	1436	9274	1	0	2.6	3.8	14000	80000	5	CAMPUS
2617	214	2167	0	6.6	4.6	0	4000	64000	9	MALL
2766	1055	6892	0	4.2	1.6	1.6	7000	74000	3	BUSINESS
5488	2243	10437	0	5.6	4.6	3.2	16000	46000	16	RESIDENTIAL
3863	1249	6794	1	0	4.5	3.9	14000	65000	11	CAMPUS

image text in transcribed

QUESTION:

image text in transcribed

Scenario You are working for a bank, planning ATM locations. To inform your planning, you are analyzing data for current ATMs, in hopes of learning the factors that make an ATM successful (i.e., highly used). You have been provided with a file (ATM.csv) with data for a number of existing ATMs. The variables in the file are: . USERS - the number of weekly users of the ATM. This is the variable that we are trying to predict. PEDESTRIANS and CARS The number of pedestrians and cars, respectively, that pass by the ATM on a weekly basis. IN_BANK - 1 if the ATM is located in a bank branch, 0 if not. DF_BANK, DF_SAME_ATM and DF_DIFF_ATM the distances (in km) from the closest branch of your bank, the next closest ATM for your bank, and the next closest ATM of a different bank, respectively. POP - The population in the immediate neighbourhood of the ATM. AVG_INC The average income of people in the neighbourhood. VIOLENT - The number of violent crimes committed in the neighbourhood in the last 6 months. . . Page 1 of 3 LOCATION - One of BUSINESS (business district of the town/city), CAMPUS, MALL, or RESIDENTIAL. Question 3 You suspect that there may be a non-linear relationship with distance-for any given distance, the area covered is a function of the square of distance. (Remember r??) Run another regression, keeping the degree 1 variables (i.e., the model from Question 1), and adding quadratic (degree 2) terms for each of the 'DF_' variables. a Is there evidence for any that any of these variables have a quadratic relationship with USERS? Explain your answer. Scenario You are working for a bank, planning ATM locations. To inform your planning, you are analyzing data for current ATMs, in hopes of learning the factors that make an ATM successful (i.e., highly used). You have been provided with a file (ATM.csv) with data for a number of existing ATMs. The variables in the file are: . USERS - the number of weekly users of the ATM. This is the variable that we are trying to predict. PEDESTRIANS and CARS The number of pedestrians and cars, respectively, that pass by the ATM on a weekly basis. IN_BANK - 1 if the ATM is located in a bank branch, 0 if not. DF_BANK, DF_SAME_ATM and DF_DIFF_ATM the distances (in km) from the closest branch of your bank, the next closest ATM for your bank, and the next closest ATM of a different bank, respectively. POP - The population in the immediate neighbourhood of the ATM. AVG_INC The average income of people in the neighbourhood. VIOLENT - The number of violent crimes committed in the neighbourhood in the last 6 months. . . Page 1 of 3 LOCATION - One of BUSINESS (business district of the town/city), CAMPUS, MALL, or RESIDENTIAL. Question 3 You suspect that there may be a non-linear relationship with distance-for any given distance, the area covered is a function of the square of distance. (Remember r??) Run another regression, keeping the degree 1 variables (i.e., the model from Question 1), and adding quadratic (degree 2) terms for each of the 'DF_' variables. a Is there evidence for any that any of these variables have a quadratic relationship with USERS? Explain your