Business statistics
A student did buy and submit the following assignment, they will get a very low mark because it is all wrong , refer to the comments Ans 1. a i) Total of number of complaints? Total of Median call time? original 6629.0 658.45.0 replacement 5174.0 516.410 Total 11803.0 1174.86 Comment: you need to summarize the dataset the same way you have summarized in weeks 1,2,3,4,5,6,7,8,9,10,11,12 ii) Difference between ?̅1−?̅2= 658.45-516.41= 142.04 Comment: you have totals not averages Difference between p ̂_1 -p ̂_2= 6629-5174= 1455 Comment: these are not proportions In relation with coefficient r= 1455/11803= +0.12 perfect positive correlation Comment: you cannot calculate correlation r this way, It is inappropriate to find correlation when you don’t have 2 quantitive variables it is wrong to say 0.12 is a perfect correlation, 1 is a perfect correlation b i) Values Row Labels Total of Median call time? Total of number of complaints? original 651.49 6539 replacement 512.12 5160 Grand Total 1163.61 11699 ii) Difference between ?̅1−?̅2= 651.49-512.12= 139.37 Difference between p ̂_1 -p ̂_2= 6539-5160= 1379 In relation with coefficient r= 1379/11699= +0.11 perfect positive correlation c) Introducing the dataset 1 the median was 142.04 introducing the dataset 2 the median was 139.37 It could be seen that the original and replacement staff in call center 1 have been having more complaints. Comment: you should not talk about differences here. Ans 2. a i) Row Labels Total of number of complaints? no 4130 original 1292 replacement 2838 yes 7673 original 5337 replacement 2336 Grand Total 11803 ii) Difference between ?̅1−?̅2= 5337-2336= 3001 Difference between p ̂_1 -p ̂_2= 7673-4130= 3543 In relation with coefficient r= 3543/11803= +0.300 a weak uphill Comment: uphill? It is better to say positive correlation positive linear correlation b i) Row Labels Total of number of complaints? no 4695 original 1220 replacement 3475 yes 7004 original 5319 replacement 1685 Grand Total 11699 ii) Difference between ?̅1−?̅2= 5319-1685= 3634 Comment: these are not means Difference between p ̂_1 -p ̂_2= 7004-4695= 2309 Comment: these are not proportions In relation with coefficient r= 2309/11699= +0.197 perfect positive correlation It is inappropriate to find correlation when you don’t have 2 quantitive variables it is wrong to say 0.12 is a perfect correlation, 1 is a perfect correlation c) Introducing the dataset 1 the median was 3001 introducing the dataset 2 the median was 3543 It can be seen that call center 2 had a lot more complaints for being above 3 minutes on an average however the call center 1 had more complaints from the replacement staff Ans 3. a i) Values Row Labels Total of number of complaints? Total of Median call time? original 6629 658.45 replacement 5174 516.41 Grand Total 11803 1174.86 ii) Difference between ?̅1−?̅2= 658.45-516.41= 142.04 Difference between p ̂_1 -p ̂_2= 6629-5174= 1455 In relation with coefficient r= 1174.86/11803= +0.099 small negative relation b i) Values Row Labels Total of Median call time? Total of number of complaints? original 651.49 6539 replacement 512.12 5160 Grand Total 1163.61 11699 ii) Difference between ?̅1−?̅2= 6539-5160= 1379 Difference between p ̂_1 -p ̂_2= 651.49-512.12= 139.37 In relation with coefficient r= 1163.61/11699= +0.099 small negative relation c) It can be seen that the results of data set 1 and 2 that the results are not very far off and that the complaints have been more from call center 1. Comment: what does this mean Ans 4. a i) The sample size is 243 and sample mean is 2.70 ii) The Zscore of sample mean would be -0.27273 b i) sample size is 221 and sample mean is 2.33 ii) The Zscore of sample mean would be -0.60909 Ans 5 a i) The sample size is 364 ii) 0.0111 proportion is way to low iii) [0.00434394 , 0.0280675] b i) 331 ii) 0.0112 iii) [0.00421809 , 0.0293974]. Ans 6. a i) Comment: pvalue missing so there is not discussion of population so you are not answering the question Values Row Labels Total of Median call time? Total of number of complaints? original 658.45 6629 replacement 516.41 5174 Grand Total 1174.86 11803 ii) This shows that the number of complaints of original staff are a lot more than the replacement staff and thus the median call time is increasing because of this reason overall. b i) Values Row Labels Total of Median call time? Total of number of complaints? original 651.49 6539 replacement 512.12 5160 Grand Total 1163.61 11699 ii) It could be seen that the in this case, original staff has more complaints and therefore the median call time has increased too. c) It has been seen that the median call time and the original and replacement data highlight that in both call centers, the original staff is more inefficient. Ans 7. a i) Comment: pvalue missing so there is not discussion of population so you are not answering the question Row Labels Total of number of complaints? no 4130 original 1292 replacement 2838 yes 7673 original 5337 replacement 2336 Grand Total 11803 ii) It can be seen that the data represents that in call center 1 the original staff had a lot more complaints all of them being more than 3 minutes. b i) Row Labels Total of number of complaints? no 4695 original 1220 replacement 3475 yes 7004 original 5319 replacement 1685 Grand Total 11699 ii) It can be seen that the data shows that in call center 2 the original staff had a lot more complaints as compared to the replacement and the frequency of 3 min call time is more for original staff. c) It can be seen that though the original staff has a lot more complaints for the original staff, it can however be seen that call center 2 has a lot less complaints. Ans 8 a i) Comment: pvalue missing so there is not discussion of population so you are not answering the question Values Total of Median call time? Total of number of complaints? 1174.86 11803 Column 1 Variance Median call time 0.19416 Number of complaints 1.956462 23.85469156 ii) This relation shows that as the data of the complaints has been increasing, the median call time is increasing too at the variance of 23.85469156. Values Total of number of complaints? Total of Median call time? 11699 1163.61 Column 1 Variance Median call time 0.191148 Number of complaints 1.876298 25.50257 ii) This relation shows that as the data of the complaints has been increasing, the median call time is increasing too at the variance of 25.50257. c) It could be seen that the data of call center 2 has been varying more correlationally as compared to call center 1 Ans 9. “The report *describes a dataset, the dataset is a record of each person reviewing new and old product *Has a statistical analysis, two major findings are 1). There is strong evidence the results above also apply to the whole population of country 2) There is stronger evidence the results above also apply to the whole population of country 2 *Has a conclusion that summarizes the results and proposes new variables The report would benefit from an executive summary that explains the major finding that both beneficial for country 1 and country 2. A student did buy and submit the following assignment, they will get a very low mark because it is all wrong , refer to the comments Ans 1. a i) Total of number of complaints? Total of Median call time? original 6629.0 658.45.0 replacement 5174.0 516.410 Total 11803.0 1174.86 Comment: you need to summarize the dataset the same way you have summarized in weeks 1,2,3,4,5,6,7,8,9,10,11,12 ii) Difference between ?̅1−?̅2= 658.45-516.41= 142.04 Comment: you have totals not averages Difference between p ̂_1 -p ̂_2= 6629-5174= 1455 Comment: these are not proportions In relation with coefficient r= 1455/11803= +0.12 perfect positive correlation Comment: you cannot calculate correlation r this way, It is inappropriate to find correlation when you don’t have 2 quantitive variables it is wrong to say 0.12 is a perfect correlation, 1 is a perfect correlation b i) Values Row Labels Total of Median call time? Total of number of complaints? original 651.49 6539 replacement 512.12 5160 Grand Total 1163.61 11699 ii) Difference between ?̅1−?̅2= 651.49-512.12= 139.37 Difference between p ̂_1 -p ̂_2= 6539-5160= 1379 In relation with coefficient r= 1379/11699= +0.11 perfect positive correlation c) Introducing the dataset 1 the median was 142.04 introducing the dataset 2 the median was 139.37 It could be seen that the original and replacement staff in call center 1 have been having more complaints. Comment: you should not talk about differences here. Ans 2. a i) Row Labels Total of number of complaints? no 4130 original 1292 replacement 2838 yes 7673 original 5337 replacement 2336 Grand Total 11803 ii) Difference between ?̅1−?̅2= 5337-2336= 3001 Difference between p ̂_1 -p ̂_2= 7673-4130= 3543 In relation with coefficient r= 3543/11803= +0.300 a weak uphill Comment: uphill? It is better to say positive correlation positive linear correlation b i) Row Labels Total of number of complaints? no 4695 original 1220 replacement 3475 yes 7004 original 5319 replacement 1685 Grand Total 11699 ii) Difference between ?̅1−?̅2= 5319-1685= 3634 Comment: these are not means Difference between p ̂_1 -p ̂_2= 7004-4695= 2309 Comment: these are not proportions In relation with coefficient r= 2309/11699= +0.197 perfect positive correlation It is inappropriate to find correlation when you don’t have 2 quantitive variables it is wrong to say 0.12 is a perfect correlation, 1 is a perfect correlation c) Introducing the dataset 1 the median was 3001 introducing the dataset 2 the median was 3543 It can be seen that call center 2 had a lot more complaints for being above 3 minutes on an average however the call center 1 had more complaints from the replacement staff Ans