Business Analytics Quiz 2: 24 March 2024 (January 2024 term)

Say a demand response curve is modelled as a constant elasticity curve. If Q1 is 2400 units, Q2 is 1500 units, P1 is Rs. 100 and P2 is Rs. 200, then what is the elasticity of the curve?
(Note: Enter the answer rounded to two decimal places. For example, if the answer is “1.234”, then enter it as “1.23”)

A parts supplier must assign supply from 5 warehouses (W1, W2, W3, W4 and W5) to 3 customers (C1, C2 and C3), such that the total demand of 300 units for the three customers is satisfied. The warehouse capacities are 85, 55, 75, 65 and 40, for W1, W2, W3, W4 and W5 respectively. A warehouse can supply any number of customers. The cost to supply a customer from a given warehouse is provided in Table-1.Given this information, answer the subquestions.

How many decision variables are present in the standard primal formulation of the given problem?

A parts supplier must assign supply from 5 warehouses (W1, W2, W3, W4 and W5) to 3 customers (C1, C2 and C3), such that the total demand of 300 units for the three customers is satisfied. The warehouse capacities are 85, 55, 75, 65 and 40, for W1, W2, W3, W4 and W5 respectively. A warehouse can supply any number of customers. The cost to supply a customer from a given warehouse is provided in Table-1.Given this information, answer the subquestions.

How many constraints are present in the standard primal formulation of the given problem? (Note: Exclude the count of non-negativity constraints when you input your answer)

A parts supplier must assign supply from 5 warehouses (W1, W2, W3, W4 and W5) to 3 customers (C1, C2 and C3), such that the total demand of 300 units for the three customers is satisfied. The warehouse capacities are 85, 55, 75, 65 and 40, for W1, W2, W3, W4 and W5 respectively. A warehouse can supply any number of customers. The cost to supply a customer from a given warehouse is provided in Table-1.Given this information, answer the subquestions.

To reduce risk, it is proposed to split the total demand evenly across all 5 warehouses. Then is it a feasible solution?

A parts supplier must assign supply from 5 warehouses (W1, W2, W3, W4 and W5) to 3 customers (C1, C2 and C3), such that the total demand of 300 units for the three customers is satisfied. The warehouse capacities are 85, 55, 75, 65 and 40, for W1, W2, W3, W4 and W5 respectively. A warehouse can supply any number of customers. The cost to supply a customer from a given warehouse is provided in Table-1.Given this information, answer the subquestions.

If W1 supplies 75 units, W2 supplies 55 units, W3 supplies 75 units, W4 supplies 60 units and W5 supplies 35 units, then how many decision variables in the dual will have a non-zero value? (Note: Enter your answer after you formulate the dual based on the standard form of the primal)

Milo’s Motors (MM) is a motorcycle brand that manufactures electric two wheelers. MM wants to understand the relationship between “Mileage”, “Storage space” and “Charging Time in Minutes” on “Sales volume”. The owner of the company, Dr. Milo, only has half-baked knowledge of regression. Hence, several regression models (Model-1, Model-2, Model-3, Model-4, Model-5, Model-6, Model-7, Model-8, Model-9 and Model-10) which are given below were built on appropriate available data by Dr. Milo.Given this information, answer the subquestions.

ANOVA

Model-1: Partial ANOVA output when regressing “Sales volume” and “Mileage”

Model-2: Partial output when regressing “Mileage” and “Storage Space”

Model-3: Partial output when regressing “Storage Space” and “Charging Time in minutes”

Model-4: Partial output when regressing “Charging Time in minutes” and “Mileage”

Model-5: Partial output when regressing “Mileage” and “Charging Time in minutes”

Model-6: Partial output when regressing “Storage Space” and “Mileage”

Model-7: Partial output when regressing “Charging time in minutes” and “Storage Space”

Model-8: Partial output when regressing “Sales” and “Storage Space”

Model-9: Partial output when regressing “Sales” and “Charging Time in minutes”

ANOVA

Model-10: Partial output when regression “Sales” with all the three explanatory variables

How many observations (rows) are present in the data set used to build Model-1?

Milo’s Motors (MM) is a motorcycle brand that manufactures electric two wheelers. MM wants to understand the relationship between “Mileage”, “Storage space” and “Charging Time in Minutes” on “Sales volume”. The owner of the company, Dr. Milo, only has half-baked knowledge of regression. Hence, several regression models (Model-1, Model-2, Model-3, Model-4, Model-5, Model-6, Model-7, Model-8, Model-9 and Model-10) which are given below were built on appropriate available data by Dr. Milo.Given this information, answer the subquestions.

ANOVA

Model-1: Partial ANOVA output when regressing “Sales volume” and “Mileage”

Model-2: Partial output when regressing “Mileage” and “Storage Space”

Model-3: Partial output when regressing “Storage Space” and “Charging Time in minutes”

Model-4: Partial output when regressing “Charging Time in minutes” and “Mileage”

Model-5: Partial output when regressing “Mileage” and “Charging Time in minutes”

Model-6: Partial output when regressing “Storage Space” and “Mileage”

Model-7: Partial output when regressing “Charging time in minutes” and “Storage Space”

Model-8: Partial output when regressing “Sales” and “Storage Space”

Model-9: Partial output when regressing “Sales” and “Charging Time in minutes”

ANOVA

Model-10: Partial output when regression “Sales” with all the three explanatory variables

What is the total indirect effect of “Mileage” on “Sales Volume”?
(Note: Enter the answer rounded to two decimal places. For example, if the answer is “1.234”, then enter it as “1.23”)

Milo’s Motors (MM) is a motorcycle brand that manufactures electric two wheelers. MM wants to understand the relationship between “Mileage”, “Storage space” and “Charging Time in Minutes” on “Sales volume”. The owner of the company, Dr. Milo, only has half-baked knowledge of regression. Hence, several regression models (Model-1, Model-2, Model-3, Model-4, Model-5, Model-6, Model-7, Model-8, Model-9 and Model-10) which are given below were built on appropriate available data by Dr. Milo.Given this information, answer the subquestions.

ANOVA

Model-1: Partial ANOVA output when regressing “Sales volume” and “Mileage”

Model-2: Partial output when regressing “Mileage” and “Storage Space”

Model-3: Partial output when regressing “Storage Space” and “Charging Time in minutes”

Model-4: Partial output when regressing “Charging Time in minutes” and “Mileage”

Model-5: Partial output when regressing “Mileage” and “Charging Time in minutes”

Model-6: Partial output when regressing “Storage Space” and “Mileage”

Model-7: Partial output when regressing “Charging time in minutes” and “Storage Space”

Model-8: Partial output when regressing “Sales” and “Storage Space”

Model-9: Partial output when regressing “Sales” and “Charging Time in minutes”

ANOVA

Model-10: Partial output when regression “Sales” with all the three explanatory variables

What is the value of the “T-statistic” associated with the intercept in Model-3?
(Note: Enter the answer rounded to two decimal places. For example, if the answer is “1.234”, then enter it as “1.23”)

Milo’s Motors (MM) is a motorcycle brand that manufactures electric two wheelers. MM wants to understand the relationship between “Mileage”, “Storage space” and “Charging Time in Minutes” on “Sales volume”. The owner of the company, Dr. Milo, only has half-baked knowledge of regression. Hence, several regression models (Model-1, Model-2, Model-3, Model-4, Model-5, Model-6, Model-7, Model-8, Model-9 and Model-10) which are given below were built on appropriate available data by Dr. Milo.Given this information, answer the subquestions.

ANOVA

Model-1: Partial ANOVA output when regressing “Sales volume” and “Mileage”

Model-2: Partial output when regressing “Mileage” and “Storage Space”

Model-3: Partial output when regressing “Storage Space” and “Charging Time in minutes”

Model-4: Partial output when regressing “Charging Time in minutes” and “Mileage”

Model-5: Partial output when regressing “Mileage” and “Charging Time in minutes”

Model-6: Partial output when regressing “Storage Space” and “Mileage”

Model-7: Partial output when regressing “Charging time in minutes” and “Storage Space”

Model-8: Partial output when regressing “Sales” and “Storage Space”

Model-9: Partial output when regressing “Sales” and “Charging Time in minutes”

ANOVA

Model-10: Partial output when regression “Sales” with all the three explanatory variables

What is the adjusted R-Square value for Model-10?
(Note: Enter the answer rounded to two decimal places. For example, if the answer is “1.234”, then enter it as “1.23”)

Milo’s Motors (MM) is a motorcycle brand that manufactures electric two wheelers. MM wants to understand the relationship between “Mileage”, “Storage space” and “Charging Time in Minutes” on “Sales volume”. The owner of the company, Dr. Milo, only has half-baked knowledge of regression. Hence, several regression models (Model-1, Model-2, Model-3, Model-4, Model-5, Model-6, Model-7, Model-8, Model-9 and Model-10) which are given below were built on appropriate available data by Dr. Milo.Given this information, answer the subquestions.

ANOVA

Model-1: Partial ANOVA output when regressing “Sales volume” and “Mileage”

Model-2: Partial output when regressing “Mileage” and “Storage Space”

Model-3: Partial output when regressing “Storage Space” and “Charging Time in minutes”

Model-4: Partial output when regressing “Charging Time in minutes” and “Mileage”

Model-5: Partial output when regressing “Mileage” and “Charging Time in minutes”

Model-6: Partial output when regressing “Storage Space” and “Mileage”

Model-7: Partial output when regressing “Charging time in minutes” and “Storage Space”

Model-8: Partial output when regressing “Sales” and “Storage Space”

Model-9: Partial output when regressing “Sales” and “Charging Time in minutes”

ANOVA

Model-10: Partial output when regression “Sales” with all the three explanatory variables

What is F-statistic for Model-10?
(Note: Enter the answer rounded to two decimal places. For example, if the answer is “1.234”, then enter it as “1.23”)

Milo’s Motors (MM) is a motorcycle brand that manufactures electric two wheelers. MM wants to understand the relationship between “Mileage”, “Storage space” and “Charging Time in Minutes” on “Sales volume”. The owner of the company, Dr. Milo, only has half-baked knowledge of regression. Hence, several regression models (Model-1, Model-2, Model-3, Model-4, Model-5, Model-6, Model-7, Model-8, Model-9 and Model-10) which are given below were built on appropriate available data by Dr. Milo.Given this information, answer the subquestions.

ANOVA

Model-1: Partial ANOVA output when regressing “Sales volume” and “Mileage”

Model-2: Partial output when regressing “Mileage” and “Storage Space”

Model-3: Partial output when regressing “Storage Space” and “Charging Time in minutes”

Model-4: Partial output when regressing “Charging Time in minutes” and “Mileage”

Model-5: Partial output when regressing “Mileage” and “Charging Time in minutes”

Model-6: Partial output when regressing “Storage Space” and “Mileage”

Model-7: Partial output when regressing “Charging time in minutes” and “Storage Space”

Model-8: Partial output when regressing “Sales” and “Storage Space”

Model-9: Partial output when regressing “Sales” and “Charging Time in minutes”

ANOVA

Model-10: Partial output when regression “Sales” with all the three explanatory variables

For which of the following “Tabulated F” values, will the Null Hypothesis NOT BE REJECTED for Model-10?(Choose all that is applicable)

An AI engine to scrutinize applications for the BS program is being developed. The aim of the AI engine is to shortlist applicants who have the highest chance of completing the program. Hence, the AI engine classifies every applicant as either “Selected” or “Not Selected” based on the “Probability of Completion” which is computed using an applicant’s previous academic and professional records (X mark, XII mark, work experience, conduct and number of extracurricular certificates)
To test the model, past student data was captured. Using the past data, the AI model predicted the probability for completion. This is provided in Table-2. The table also provides the information of whether the student actually completed the course.Given this information, answer the subquestions.

Table-2

At a threshold of 0.7, what is the accuracy of the AI engine?
(Note: Enter the answer in “%” rounded to two decimal places without the “%” symbol. For example, if the answer is “1.234%”, then enter it as “1.23”)

An AI engine to scrutinize applications for the BS program is being developed. The aim of the AI engine is to shortlist applicants who have the highest chance of completing the program. Hence, the AI engine classifies every applicant as either “Selected” or “Not Selected” based on the “Probability of Completion” which is computed using an applicant’s previous academic and professional records (X mark, XII mark, work experience, conduct and number of extracurricular certificates)
To test the model, past student data was captured. Using the past data, the AI model predicted the probability for completion. This is provided in Table-2. The table also provides the information of whether the student actually completed the course.Given this information, answer the subquestions.

Table-2

At a threshold of 0.7, what is the precision for “Not Selected” category for the AI engine? (Note: Enter the answer in “%” rounded to two decimal places without the “%” symbol. For example, if the answer is “1.234%”, then enter it as “1.23”)

An AI engine to scrutinize applications for the BS program is being developed. The aim of the AI engine is to shortlist applicants who have the highest chance of completing the program. Hence, the AI engine classifies every applicant as either “Selected” or “Not Selected” based on the “Probability of Completion” which is computed using an applicant’s previous academic and professional records (X mark, XII mark, work experience, conduct and number of extracurricular certificates)
To test the model, past student data was captured. Using the past data, the AI model predicted the probability for completion. This is provided in Table-2. The table also provides the information of whether the student actually completed the course.Given this information, answer the subquestions.

Table-2

At a threshold of 0.7, what is the recall for “Selected” category for the AI engine?
(Note: Enter the answer in “%” rounded to two decimal places without the “%” symbol. For example, if the answer is “1.234%”, then enter it as “1.23”)

An AI engine to scrutinize applications for the BS program is being developed. The aim of the AI engine is to shortlist applicants who have the highest chance of completing the program. Hence, the AI engine classifies every applicant as either “Selected” or “Not Selected” based on the “Probability of Completion” which is computed using an applicant’s previous academic and professional records (X mark, XII mark, work experience, conduct and number of extracurricular certificates)
To test the model, past student data was captured. Using the past data, the AI model predicted the probability for completion. This is provided in Table-2. The table also provides the information of whether the student actually completed the course.Given this information, answer the subquestions.

Table-2

At a threshold of 0.4, how many “False Positives” is the AI engine predicting?
(Note: Enter the answer rounded to two decimal places. For example, if the answer is “1.234”, then enter it as “1.23”)

An AI engine to scrutinize applications for the BS program is being developed. The aim of the AI engine is to shortlist applicants who have the highest chance of completing the program. Hence, the AI engine classifies every applicant as either “Selected” or “Not Selected” based on the “Probability of Completion” which is computed using an applicant’s previous academic and professional records (X mark, XII mark, work experience, conduct and number of extracurricular certificates)
To test the model, past student data was captured. Using the past data, the AI model predicted the probability for completion. This is provided in Table-2. The table also provides the information of whether the student actually completed the course.Given this information, answer the subquestions.

Table-2

At a threshold of 0.4, how many “False Negatives” is the AI engine predicting?
(Note: Enter the answer rounded to two decimal places. For example, if the answer is “1.234”, then enter it as “1.23”)

An AI engine to scrutinize applications for the BS program is being developed. The aim of the AI engine is to shortlist applicants who have the highest chance of completing the program. Hence, the AI engine classifies every applicant as either “Selected” or “Not Selected” based on the “Probability of Completion” which is computed using an applicant’s previous academic and professional records (X mark, XII mark, work experience, conduct and number of extracurricular certificates)
To test the model, past student data was captured. Using the past data, the AI model predicted the probability for completion. This is provided in Table-2. The table also provides the information of whether the student actually completed the course.Given this information, answer the subquestions.

Table-2

At a threshold of 0.4, how many “True Negatives” is the AI engine predicting?
(Note: Enter the answer rounded to two decimal places. For example, if the answer is “1.234”, then enter it as “1.23”)