UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook

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2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 1/19 Lab Solution --- NOT FOR DISTRIBUTION! Lab CX: Weighted Average 1 Setup In [1]: 2 HMO Contracts 2.1 Business Decision A private doctor contracts with health maintenance organizations (HMOs) that provide health care benefits to employees of large firms. Each HMO pays the doctor a set amount per patient- in-care per month, regardless of whether the patients actually visit the doctor for treatment. Those patients that do visit the doctor for treatment pay the doctor a copay per visit, regardless of the duration of the visit. The doctor wants to know whether patients from one HMO are more likely to visit than those from another HMO. Also, she wants to know whether patients from one HMO take more of her time than those from another HMO. If so, the she might try to negotiate higher pay from the HMO with patients who are more likely to visit and/or visit longer. The doctor has the following patients-in-care and HMO contracts: # Import some useful functions from numpy import * from numpy.random import * from datascience import * from statsmodels.formula.api import * # Define some useful functions def correlation (array_1, array_2): return corrcoef(array_1, array_2).item( 1 ) # Customize look of graphics import matplotlib.pyplot as plt plt.style.use( 'fivethirtyeight' ) % matplotlib inline # Force display of all values from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all" # Handle some obnoxious warning messages import warnings warnings.filterwarnings( "ignore" )

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 2/19 There are 130 AARP patients-in-care. AARP pays the doctor $100 per patient-in-care per month. AAPA patients pay the doctor $10 per visit. There are 110 Aetna patients-in-care. Aetna pays the doctor $100 per patient-in-care per month. Aetna patients pay the doctor $10 per visit. There are 120 Blue Cross patients-in-care. Blue Cross pays the doctor $100 per patient-in- care per month Blue Cross patients pay the doctor $10 per visit 2.2 Data Retrieve the doctor visit dataset, show the number of observations and the first few observations. This dataset records visits for one month. Note that duration is recorded in units of minutes. In [2]: Set the pay per patient-in-care per month. Set the copay per visit. For each HMO, set the number of patients-in-care. Out[2]: 128 Out[2]: Patient ID HMO Plan Duration 149494695 Aetna 15 114484827 Blue Cross 20 90038933 Blue Cross 15 65338581 Aetna 25 196045149 Aetna 20 66286542 Blue Cross 20 162667323 AARP 30 171010175 AARP 25 217152916 Aetna 15 129287489 Blue Cross 20 ... (118 rows omitted) data = Table().read_table( 'doctor_visits.csv' ) data.num_rows data

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 3/19 In [3]: 2.3 Analysis Calculate the mean visit duration (minutes). In [4]: For each HMO, calculate the number of visits. Out[3]: 100 Out[3]: 10 Out[3]: 130 Out[3]: 110 Out[3]: 120 Out[3]: 100 Out[3]: 10 Out[3]: array([130, 110, 120]) Out[4]: 21.25 pay = 100 copay = 10 n_AARP = 130 n_Aetna = 110 n_blue = 120 pay copay n_AARP n_Aetna n_blue n = make_array( 130 , 110 , 120 ) pay copay n mean(data.column( 'Duration' ))

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2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 4/19 In [5]: For each HMO, calculate the mean visit duration (minutes). In [6]: For each HMO, calculate the sum total visit duration (minutes). ds = data.group('HMO Plan', sum).select('HMO Plan', 'Duration sum') ds For each HMO, calculate the revenue rate ($ per hour earned for treating patients). Hint: Remember to convert minutes to hours. In [ ]: Calculate the ratio of revenue rate for Blue Cross patients to revenue rate for Aetna patients. Calculate the ratio of revenue rate for AARP patients to revenue rate for Blue Cross patients. Out[5]: HMO Plan count AARP 32 Aetna 48 Blue Cross 48 Out[6]: HMO Plan Duration mean AARP 27.5 Aetna 20 Blue Cross 18.3333 dc = data.group( 'HMO Plan' ) dc dm = data.group( 'HMO Plan' , mean).select( 'HMO Plan' , 'Duration mean' ) dm sum_pay_aarp = n_AARP * pay sum_copay_treat_aarp = dc.column( 'count' ).item( 0 ) * copay sum_duration_treat_aarp = ds.column( 'Duration sum' ).item( 0 ) / 60 rate_aarp = (sum_pay_aarp + sum_copay_treat_aarp) / sum_duration_treat_aa sum_pay_aetna = n_Aetna + pay sum_copay_treat_aetna = dc.column( 'count' ).item( 1 ) * copay sum_duration_treat_aetna = ds.column( 'Duration sum' ).item( 1 ) / 60 rate_aetna = (sum_pay_aetna + sum_copay_treat_aetna) / sum_duration_treat sum_pay_blue = n_blue + pay sum_copay_treat_blue = dc.column( 'count' ).item( 2 ) * copay sum_duration_treat_blue = ds.column( 'Duration sum' ).item( 2 ) / 60 rate_blue = (sum_pay_blue + sum_copay_treat_blue) / sum_duration_treat_bl rate_aarp rate_aetna rate_blue

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 5/19 In [8]: 2.4 Quiz The dataset comprises ____ observations. The dataset comprises ____ variables. Patients that visited the doctor did so for ____ minutes, on average. ____ AARP patients visited the doctor. ____ Aetna patients visited the doctor. ____ Blue Cross patients visited the doctor. AARP patients that visited the doctor did so for ____ minutes, on average. Aetna Patients that visited the doctor did so for ____ minutes, on average. Blue Cross patients that visited the doctor did so for ____ minutes, on average. AARP patients that visited the doctor did so for a sum total of ____ minutes. Aetna patients that visited the doctor did so for a sum total of of ____ minutes. Blue Cross patients that visited the doctor did so for a sum total of ____ minutes. The doctor earns $ ____ per hour for treating AARP patients, on average. The doctor earns $ ____ per hour for treating Aetna patients, on average. The doctor earns $ ____ per hour for treating Blue Cross patients, on average. The doctor earns ____ times as much per hour for treating Blue Cross patients as for treating Aetna patients, on average. The doctor earns ____ times as much per hour for treating AARP patients as for treating Blue Cross patients, on average. Assuming that the doctor will keep the same patients-in-care, she should try to re-negotiate the pay from ____. AARP because AARP patients have longer visit durations than those from other HMOs do, on average. AARP or Blue Cross because these HMOs each correspond to the lowest sum total visit duration. Aetna because there are fewer patients from Aetna than from other HMOs. Aetna because Aetna patients produce less revenue per hour than those from other HMOs do, on average. ----------------------------------------------------------------------- ---- NameError Traceback (most recent call l ast) Cell In[8], line 1 ----> 1 rate_blue / rate_aetna 2 rate_aarp / rate_blue 4 rate . item( 2 ) / rate . item( 1 ) NameError : name 'rate_blue' is not defined rate_blue / rate_aetna rate_aarp / rate_blue rate.item( 2 ) / rate.item( 1 ) rate.item( 0 ) / rate.item( 2 )

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 6/19 3 Restaurant Reviews continued 3.1 Business Decision An independent marketing firm recently surveyed some of the city's restaurant patrons for their overall ratings of several restaurants. It is not clear how well the surveyed patrons represent the city's entire population of patrons. Restaurant #135085 competes with Restaurant #135076, and their owners are considering launching marketing campaigns that would tout their favorable customer ratings. 3.2 Data The dataset comprises several ratings. Each observation corresponds to a customer's review of a restaurant. Retrieve the restaurant survey dataset, show the number of observations and the first few observations In [13]: Out[13]: 1161 Out[13]: userID placeID rating food_rating service_rating latitude longitude smoker drink_level dress_ U1001 135045 1 1 1 22.14 -100.979 false abstemious U1001 135033 1 1 1 22.14 -100.979 false abstemious U1001 135025 2 2 2 22.14 -100.979 false abstemious U1001 135085 0 1 1 22.14 -100.979 false abstemious U1001 135039 1 1 1 22.14 -100.979 false abstemious U1001 132825 2 2 1 22.14 -100.979 false abstemious U1001 132830 1 1 1 22.14 -100.979 false abstemious U1001 135051 1 1 2 22.14 -100.979 false abstemious U1001 135040 1 1 1 22.14 -100.979 false abstemious U1002 135085 1 1 1 22.1501 -100.983 false abstemious ... (1151 rows omitted) data_all = Table.read_table( 'restaurant_reviews.csv' ) data_all.num_rows data_all

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2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 7/19 Show all the possible rating levels. In [14]: Build and show a table comprising reviews for restaurant #135085. Out[14]: array([0, 1, 2]) unique(data_all.column( 'rating' ))

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 9/19 userID placeID rating food_rating service_rating latitude longitude smoker drink_level dress_ U1001 135085 0 1 1 22.14 -100.979 false abstemious U1002 135085 1 1 1 22.1501 -100.983 false abstemious U1007 135085 1 2 0 22.1185 -100.938 false casual drinker U1013 135085 1 2 0 22.1746 -100.994 false abstemious no U1016 135085 2 2 0 22.1562 -100.977 false casual drinker U1027 135085 1 1 1 22.1652 -100.987 true social drinker no U1029 135085 1 1 1 22.1518 -100.989 true casual drinker U1032 135085 1 1 1 22.1692 -100.987 false abstemious U1033 135085 2 2 2 22.15 -100.983 false social drinker no U1036 135085 2 2 2 22.1606 -100.989 false social drinker U1045 135085 2 2 2 22.1567 -100.984 false casual drinker U1046 135085 1 2 1 22.1444 -100.933 false social drinker no U1049 135085 0 0 0 22.15 -100.983 false casual drinker U1056 135085 2 2 2 22.169 -100.974 false social drinker U1059 135085 2 2 2 18.9883 -99.097 false abstemious U1062 135085 0 0 0 22.1958 -101.006 false casual drinker U1077 135085 2 2 2 22.1565 -100.986 false social drinker U1081 135085 1 2 1 22.2077 -100.942 false casual drinker U1084 135085 2 2 2 22.1725 -101.006 true casual drinker U1086 135085 2 1 1 22.1573 -100.984 false social drinker no U1089 135085 1 1 1 22.1626 -100.993 true casual drinker U1090 135085 2 2 2 22.1585 -100.984 false social drinker no U1092 135085 0 0 0 22.1254 -100.948 false abstemious U1098 135085 1 2 1 22.1826 -100.963 false social drinker

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2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 10/19 Build and show a table comprising reviews for restaurant #135076. userID placeID rating food_rating service_rating latitude longitude smoker drink_level dress_ U1104 135085 2 2 1 22.149 -100.978 true social drinker no U1106 135085 2 2 2 18.9271 -99.1736 false casual drinker U1108 135085 1 2 1 22.1435 -100.988 false abstemious U1109 135085 2 2 2 22.3033 -101.055 false abstemious U1113 135085 1 2 1 22.1373 -100.914 false casual drinker U1116 135085 2 2 2 22.1565 -100.986 false social drinker U1120 135085 0 0 0 22.1219 -100.904 false abstemious U1122 135085 2 2 1 22.1696 -100.992 ? abstemious U1132 135085 2 2 2 22.15 -100.983 false casual drinker U1134 135085 2 1 2 22.1497 -100.999 false casual drinker no U1135 135085 0 0 0 22.1704 -100.95 false casual drinker U1137 135085 2 1 2 22.1448 -100.945 false social drinker

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 11/19 In [17]: 3.3 Analysis 3.3.1 Restaurant #135085, budget Calculate the number of customers for restaurant #135085. Calculate the mean rating for restaurant #135085. In [16]: Out[17]: 13 userID placeID rating food_rating service_rating latitude longitude smoker drink_level dress_ U1005 135076 2 2 1 22.1835 -100.96 false abstemious no U1010 135076 1 2 2 22.1909 -100.999 false social drinker no U1013 135076 1 1 0 22.1746 -100.994 false abstemious no U1014 135076 1 2 1 23.7516 -99.1701 false abstemious U1019 135076 0 0 0 22.1534 -100.975 false casual drinker no U1053 135076 1 1 1 22.1758 -100.987 false casual drinker U1056 135076 2 2 2 22.169 -100.974 false social drinker U1058 135076 2 2 2 22.2058 -100.986 false abstemious U1075 135076 2 2 0 22.1676 -100.96 false casual drinker U1081 135076 1 2 2 22.2077 -100.942 false casual drinker U1085 135076 2 2 2 22.1968 -100.936 false abstemious no U1099 135076 1 1 0 22.1849 -100.971 true social drinker U1125 135076 1 1 1 22.192 -100.957 false casual drinker Out[16]: 1.3333333333333333 data2 = data_all.where( 'placeID' , 135076 ) data2.num_rows data2.show() n = data1.num_rows avg1 = mean(data1.column( 'rating' )) avg1

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 12/19 Calculate the number of low budget customers for restaurant #135085. Calculate the number of medium budget customers for restaurant #135085. Calculate the number of high budget customers for restaurant #135085. Calculate the number of customers with known budget type for restaurant #135085. In [18]: Calculate the mean rating by low budget customers for restaurant #135085. Calculate the mean rating by medium budget customers for restaurant #135085. Calculate the mean rating by high budget customers for restaurant #135085. In [19]: Calculate the weighted average rating by budget type for restaurant #135085, assuming budget types for the population are equally distributed. In [21]: Out[18]: 11 Out[18]: 21 Out[18]: 1 Out[18]: 33 Out[19]: 1.0909090909090908 Out[19]: 1.4285714285714286 Out[19]: 2.0 Out[21]: 1.5064935064935066 n_low = data1.where( 'budget' , 'low' ).num_rows n_low n_medium = data1.where( 'budget' , 'medium' ).num_rows n_medium n_high = data1.where( 'budget' , 'high' ).num_rows n_high n = n_low + n_medium + n_high n mean_low = mean(data1.where( 'budget' , 'low' ).column( 'rating' )) mean_low mean_medium = mean(data1.where( 'budget' , 'medium' ).column( 'rating' )) mean_medium mean_high = mean(data1.where( 'budget' , 'high' ).column( 'rating' )) mean_high avg1_budget_equal = (mean_low + mean_medium + mean_high) / 3 avg1_budget_equal

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2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 13/19 Calculate the weighted average rating by budget type for restaurant #135085, assuming budget types for the population are distributed as they are for restaurant #135085. In [24]: Calculate the weighted average rating by budget type for restaurant #135085, assuming budget types for the population are distributed as in all the data. In [ ]: 3.3.2 Restaurant #135085, marital status Calculate the number of single customers for restaurant #135085. Calculate the number of widow customers for restaurant #135085. Calculate the number of married customers for restaurant #135085. Calculate the number of customers with known marital status for restaurant #135085. In [ ]: Calculate the mean rating by single customers for restaurant #135085. Calculate the mean rating by widow customers for restaurant #135085. Calculate the mean rating by married customers for restaurant #135085. Cell In[24], line 1 avg1_budget_sample x (n_low * mean_low + n_medium * mean_medium + n _high * mean_high) / (n_low + n_medium + n_high) ^ SyntaxError: invalid syntax avg1_budget_sample x (n_low * mean_low + n_medium * mean_medium + n_high avg1_budget_sample n_all_low = data_all.where( 'budget' , 'low' ).num_rows n_all_medium = data_all.where( 'budget' , 'medium' ).num_rows n_all_high = data_all.where( 'budget' , 'high' ).num_rows avg1_budget_sample = (n_all_low * mean_low + n_all_medium * mean_medium + avg1_budget_sample n_single = data1.where( 'marital_status' , 'single' ).num_rows n_single n_widow = data1.where( 'marital_status' , 'window' ).num_rows n_widow n_married = data1.where( 'marital_status' , 'married' ).num_rows n_married n = n_single + n_widow + n_married n

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 14/19 In [ ]: Calculate the weighted average rating by marital status for restaurant #135085, assuming marital statuses for the population are equally distributed. In [ ]: Calculate the weighted average rating by marital status for restaurant #135085, assuming marital statuses for the population are distributed as they are for restaurant #135085. In [ ]: Calculate the weighted average rating by marital status for restaurant #135085, assuming marital statuses for the population are distributed as in all the data. In [ ]: 3.3.3 Restaurant #135076, budget Calculate the number of customers for restaurant #135076. Calculate the mean rating for restaurant #135076. In [ ]: Calculate the number of low budget customers for restaurant #135076. Calculate the number of medium budget customers for restaurant #135076. Calculate the number of high budget customers for restaurant #135076. Calculate the number of customers with known budget type for restaurant #135076. mean_single = mean(data1.where( 'marital_status' , 'single' ).column( 'rating mean_single mean_widow = mean(data1.where( 'marital_status' , 'widow' ).column( 'rating' mean_widow mean_married = mean(data1.where( 'marital_status' , 'married' ).column( 'rati mean_married avg1_marital_equal = (mean_single + mean_widow + mean_married) / 3 avg1_marital_equal avg1_marital_sample = avg1_marital_sample = (n_single * mean_single + n_w avg1_marital_sample avg1_marital_sample n_all_single = data_all.where( 'marital_status' , 'single' ).num_rows n_all_widow = data_all.where( 'marital_status' , 'widow' ).num_rows n_all_married = data_all.where( 'marital_status' , 'married' ).num_rows avg1_marital_all = (n_all_single * mean_single + n_all_widow * mean_widow avg1_marital_all n = data2.num_rows avg2 = mean(data2.column( 'rating' )) avg2

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 15/19 In [ ]: Calculate the mean rating by low budget customers for restaurant #135076. Calculate the mean rating by medium budget customers for restaurant #135076. Calculate the mean rating by high budget customers for restaurant #135076 In [25]: Calculate the weighted average rating by budget type for restaurant #135076, assuming budget types for the population are equally distributed In [ ]: Calculate the weighted average rating by budget type for restaurant #135076, assuming budget types for the population are distributed as they are for restaurant #135076 In [ ]: Calculate the weighted average rating by budget type for restaurant #135076, assuming budget types for the population are distributed as in all data. Out[25]: 1.25 Out[25]: 1.2857142857142858 Out[25]: 1.0 n_low = data2.where( 'budget' , 'low' ).num_rows n_low n_medium = data2.where( 'budget' , 'medium' ).num_rows n_medium n_high = data2.where( 'budget' , 'high' ).num_rows n_high n = n_low + n_medium + n_high n mean_low = mean(data2.where( 'budget' , 'low' ).column( 'rating' )) mean_low mean_medium = mean(data2.where( 'budget' , 'medium' ).column( 'rating' )) mean_medium mean_high = mean(data2.where( 'budget' , 'high' ).column( 'rating' )) mean_high avg2_budget_equal = (mean_low + mean_medium + mean_high) / 3 avg2_budget_equal avg2_budget_sample = (n_low * mean_low + n_medium * mean_medium + n_high avg2_budget_sample

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2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 16/19 In [ ]: 3.3.4 Restaurant #135076, marital status Calculate the number of single customers for restaurant #135076. Calculate the number of widow customers for restaurant #135076. Calculate the number of married customers for restaurant #135076. Calculate the number of customers with known marital status for restaurant #135076. In [29]: Calculate the mean rating by single customers for restaurant #135076. Calculate the mean rating by widow customers for restaurant #135076. Calculate the mean rating by married customers for restaurant #135076. In [26]: Out[29]: 11 Out[29]: 1 Out[29]: 1 Out[29]: 13 Out[26]: 1.3636363636363635 Out[26]: 1.0 Out[26]: 1.0 n_all_low = data_all.where( 'budget' , 'low' ).num_rows n_all_medium = data_all.where( 'budget' , 'medium' ).num_rows n_all_high = data_all.where( 'budget' , 'high' ).num_rows avg2_budget_all = (n_all_low * mean_low + n_all_medium * mean_medium + n_ avg1_budget_all n_single = data2.where( 'marital_status' , 'single' ).num_rows n_single n_widow = data2.where( 'marital_status' , 'widow' ).num_rows n_widow n_married = data2.where( 'marital_status' , 'married' ).num_rows n_married n = n_single + n_widow + n_married n mean_single = mean(data2.where( 'marital_status' , 'single' ).column( 'rating mean_single mean_widow = mean(data2.where( 'marital_status' , 'widow' ).column( 'rating' mean_widow mean_married = mean(data2.where( 'marital_status' , 'married' ).column( 'rati mean_married

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 17/19 Calculate the weighted average rating by marital status for restaurant #135076, assuming marital status for the population are equally distributed. In [27]: Calculate the weighted average rating by marital status for restaurant #135076, assuming marital status for the population are distributed as they are for restaurant #135076. In [31]: Calculate the weighted average rating by marital status for restaurant #135076, assuming marital status for the population are distributed as in all the data. In [32]: 3.3.5 Comparison Build a table to compare rating averages calculated various ways like this: One column for calculation method name + one column for distribution assumption + two columns for restaurant ratings Seven rows for calculation methods First row is mean Second row is weighted average by budget type, assuming population distribution is equal Out[27]: 1.1212121212121211 Out[31]: 1.3076923076923077 ----------------------------------------------------------------------- ---- AttributeError Traceback (most recent call l ast) Cell In[32], line 1 ----> 1 n_all_single = data . all . where( 'marital_status' , 'single' ) . num_r ows 2 n_all_widow = data . all . where( 'marital_status' , 'widow' ) . num_row s 3 n_all_married = data . all . where( 'marital_status' , 'high' ) . num_ro ws AttributeError : 'Table' object has no attribute 'all' avg2_marital_equal = (mean_single + mean_widow + mean_married) / 3 avg2_marital_equal avg2_marital_sample = (n_single * mean_single + n_widow * mean_widow + n_ avg2_marital_sample n_all_single = data_all.where( 'marital_status' , 'single' ).num_rows n_all_widow = data_all.where( 'marital_status' , 'widow' ).num_rows n_all_married = data_all.where( 'marital_status' , 'high' ).num_rows avg2_budget_all = (n_all_single * mean_single + n_all_widow * mean_widow avg2_budget_all

2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 18/19 Third row is weighted average by marital status, assuming population distribution is equal Fourth row is weighted average by budget type, assuming population distribution is as in restaurant sample Fifth row is weighted average by marital status, assuming population distribution is as in restaurant sample Sixth row is weighted average by budget type, assuming population distribution is as in all the data Seventh row is weighted average by marital status, assuming population distribution is In [ ]: 3.4 Quiz The dataset comprises ____ observations. The dataset comprises ____ variables. There are ____ possible rating levels. The mean rating for restaurant #135085 is ____ . The weighted average rating by budget type for restaurant #135085 is ____ , assuming budget types for the population are equally distributed. The weighted average rating by budget type for restaurant #135085 is ____ , assuming budget types for the population are distributed as they are for restaurant #135085. The weighted average rating by budget type for restaurant #135085 is ____ , assuming budget types for the population are distributed as in all the data. The weighted average rating by marital status for restaurant #135085 is ____ , assuming marital statuses for the population are equally distributed. The weighted average rating by marital status for restaurant #135085 is ____ , assuming marital statuses for the population are distributed as they are for restaurant #135085. The weighted average rating by marital status for restaurant #135085 is ____ , assuming marital statuses for the population are distributed as in all the data. There are ____ reviews for restaurant #135076. The weighted average rating by budget type for restaurant #135076 is ____ , assuming budget types for the population are equally distributed. The weighted average rating by budget type for restaurant #135076 is ____ , assuming budget types for the population are distributed as they are for restaurant #135076. The weighted average rating by budget type for restaurant #135076 is ____ , assuming budget types for the population are distributed as in all the data. The weighted average rating by marital status for restaurant #135076 is ____ , assuming marital statuses for the population are equally distributed. The weighted average rating by marital status for restaurant #135076 is ____ , assuming marital statuses for the population are distributed as they are for restaurant #135076. The weighted average rating by marital status for restaurant #135076 is ____ , assuming marital statuses for the population are distributed as in all the data. Comparative analysis shows that mean or weighted average rating of restaurant #135085 is higher than that of restaurant #135076 ____ out of 7 times. Table().with_columns( 'Method' , make_array( 'mean' , 'weighted average by bu 'Distribution Assumption' , make_array( '' , 'equal' , 'Restaurant #135085' , make_array(avg1, avg1_budget_e 'Restaurant #135076' , make_array(avg2, avg2_budget_e

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2/5/24, 6:32 PM UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook https://ugba88.haas.berkeley.edu/user/darrinfan/notebooks/my-work/UGBA88 Lab CX assignment-Copy2.ipynb 19/19 Restaurant #135076 claims that its average rating is higher than restaurant #135085's. This claim is ____. true when weighted assuming distribution of marital statuses is as in the dataset of all reviews false when weighted assuming distribution of marital statuses is as in the dataset of all reviews true regardless of weight assumption false regardless of weight assumption The real proportion of low budget customers in the population of customers that do not write reviews is ____. about 10% about 33% about 50% about the same as the proportion of low budget customers in the sample of restaurant #135076's customers unknown Document revised September 1, 2023 Copyright (c) Huntsinger Associates, LLC

UGBA88 Lab CX assignment-Copy2 - Jupyter Notebook

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