Business Analytics, End Term
In an ideal scenario, if a distribution has “Negative Skewness” then,
In an ideal scenario, if a distribution has “Negative Skewness” then, A company produces a car in two locations “A” and “B” using the same manufacturing process. A total of 20 cars in each location were taken and the number of defects in each car was computed. It has been established that the maximum number of defects per car is 5. Given this data in Table- 1, answer the given sub-questions. | Number of Defects | Number of cars produced at Location-A with the specified number of defects | Number of cars produced at Location-B with the specified number of defects | |---|---|---| | 1 | 5 | 1 | | 2 | 5 | 5 | | 3 | 3 | 2 | | 4 | 3 | 2 | | 5 | 2 | 5 | **Table- 1** If the focus is on seeing the distribution of defects (presented in Table-1) across the two locations, then which among the following graphs will be best suited? (*Note: While choosing an answer to this* *question, please do not worry about colour reproduction or other aesthetics. Make a choice only based* *on the concepts of visualization theory*) A company produces a car in two locations “A” and “B” using the same manufacturing process. A total of 20 cars in each location were taken and the number of defects in each car was computed. It has been established that the maximum number of defects per car is 5. Given this data in Table- 1, answer the given sub-questions. | Number of Defects | Number of cars produced at Location-A with the specified number of defects | Number of cars produced at Location-B with the specified number of defects | |---|---|---| | 1 | 5 | 1 | | 2 | 5 | 5 | | 3 | 3 | 2 | | 4 | 3 | 2 | | 5 | 2 | 5 | **Table- 1** If the aim is to determine if the defect occurrence is independent of location, then how many cars would you expect to have “4” defects in Location A? *(Round your answer to two decimal places. Eg: If* *your answer is 10.256, then round it to 10.26)*