Applied probability to predict the chance of employees whopass the mt program to officially become the brand manager

Application of Probability in this article + Sex Male/Female + Age 18-21/22-25/26-29/30-33/34+ + Academic Level High-school graduation/ Undergraduate/ Undergraduate + awareness of candid

HANOI MINISTRY OF EDUCATION AND TRAINING

National Economics University

School of Advanced Educational Programs

***

ASSIGNMENT REPORT International Business Administration Intake 62B

Group 3

Authors: Nguyễn Thảo Linh

Nguyễn Thùy Dung Trần Lê Thu Hà Nguyễn Thị Tâm Giang

Hồ Minh Huyền

Đỗ Thị Ngọc Ánh Course: BUSINESS STATISTICS

Report title: Application of probability in evaluating the potential of candidates for the Management Trainee program: Research

the case of PwC

Date of completion: October 13th, 2022

Table of Content

Part 1: Article summarizing 3

I Introduction 3

1 What is PwC? 3

2 What is Management Trainee Program? 3

II Summarizing the ariticle 3

1 What is the issue of interest? 3

2 Why do you care about the technique as the organization manager? 3

3 Additional Source 4

4 Application of Probability in this article 4

5 Hypotheses: 5

Part 2: Data Analyzing 5

1 Gender 5

2) Age 6

3 Academic level 7

4 The awareness of candidates about the management trainee program since their high school 8

5 Experiment 9

6 Skills 11

III Extra part: Applied probability to predict the chance of employees who pass the MT program to officially become the brand manager 12

1 The sample space 12

2 Probability distribution 13

3 Describing the probability distribution 13

4 Bivariate Distribution 14

5 Bivariate Probability of Distribution 15

IV Discussion 16

V Conclusion 18

1 Summary 18

2 Limitation 19

VI Reference 20

Part 1: Article summarizing

I Introduction

1 What is PwC?

Formed in 1998 when Price Waterhouse merged with Coopers and Lybrand, PwC, also known as PricewaterhouseCoopers, offers clients various professional business services, including accounting, auditing, human resources consulting, and strategy management It is among the “Big Four” professional services firms, alongside Deloitte, Ernst & Young, and KPMG

2 What is Management Trainee Program?

Management trainees, sometimes referred to as "MTs," are often hired to work and train alongside managers and executives with the intention that one day they will become a manager within the organization Current managers and other experienced, senior personnel in various departments supervise the instruction and development of these trainees, teaching them the techniques and systems necessary to keep the company running efficiently and effectively This type of position is most often found in particular industries, such as operations, finance, sales or marketing

II Summarizing the ariticle

1 What is the issue of interest?

Purpose: Predicting the probability of passing or failing through the characteristics of candidates participating in the PwC management program entrance exam

2 Why do you care about the technique as the organization manager?

- For the company: get an overview of common characteristics commonly found

in candidates with a high probability of passing the MT program

=> Therefore, it helps the company to evaluate and make decisions to choose suitable candidates

- For candidates who are or will be taking the MT exam: Understanding the probability of passing MT through common characteristics in a potential candidate will help them make adjustments and change themselves to increase their chances of being recruited

3 Additional Source

+ Predicting customer consumption trends

Source: (Mahajan, 2015):

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4456898/

+ Predicting risks in finance, business, insurance

Source: (G Shafer, V Vovk , 2005) https://books.google.com.vn/books?

hl=vi&lr=&id=dYxsZzMmvHoC&oi=fnd&pg=PR5&dq=probability+finance& ots=CR0SJI72Mg&sig=o4mfOKuAtqlSURZedHOuz5SBeQM&redir_esc=y#v

=onepage&q=probability%20finance&f=false

Source: (B Lipstein - Journal of Marketing Research, 1965)

https://journals.sagepub.com/doi/abs/10.1177/002224376500200305

4 Application of Probability in this article

+ Sex (Male/Female)

+ Age (18-21/22-25/26-29/30-33/34+)

+ Academic Level (High-school graduation/ Undergraduate/ Undergraduate) + awareness of candidates about the management trainee program since theirA high school Don’t know/ Know but not research about it/ Know and research (

about it)

+ Candidates experiment (No experiment/ Related course experiment/ Part-time job experiment/ Full-time job experiment/ Extra-curricular activities)

+ Candidates skills (Communication, Leadership/ Problem solving/ Teamwork/ Self awareness/ Critical thinking)

5 Hypotheses:

Theory of Planned Behavior: The theory of planned behavior is a theory used

to understand and predict behaviors, which posits that behaviors are immediately determined by behavioral intentions and under certain circumstances, perceived behavioral control Behavioral intentions are determined by a combination of three factors: attitudes toward the behavior, subjective norms, and perceived behavioral control

Part 2: Data Analyzing

1 Gender

This table illustrates the common Gender of the candidates who participate in the management trainee program (analyzing the actual case of PwC Company) Therefore predicting the probability of each category of age helps the company,

as well as the wannabe candidates, have the overall view of the probability of each age to fail/pass the management trainee program

For more details, we can easily see the Joint probability (P(x and y) - x is the gender category, y is the probability to pass/fail) through the table More specifically, the joint probability of Female and Pass (abbreviate as P(Female and Pass) is 0.25, and P(Female and Fail) is 0,25 Do the same for others, we have P(Male and Pass) is 0.25, P(Male and Fail) is 0.25

When it comes to the Marginal probability, we can also calculate the marginal probability of event Female (abbreviated as P(female)) = P(Female and Pass) + P(female and Fail) = 0.5 In addition, we can calculate P(male) = P(male and

Pass) + P(female and Fail) = 0.5 Besides that, we can calculate the marginal probability of Pass (abbreviated as P(pass)) = P(Pass and Female) + P(Pass and Male) = 0.5, P(fail) = P(Fail and Male) + P(Fail and Female) = 0.5

In this gender category table, use the formula P(A/B) = P(A and B)/P(B) to take the conditional probability, then compare it with P(A) We can see that P(Pass/Female) = 0.25 / 0.5 = 0.5 and it is equal to P(Pass), P(Pass/Male) = 0.25 / 0.5 =0.5 and it also equal to P(Pass) too Therefore these are independent events Doing the same with the other events, we can conclude that event Female and event Male are independent with event Pass

2) Age

This table illustrates the common age of the candidates who participate in the management trainee program (analysing the actual case of PwC Company) Therefore, predicting the probability of each category of age helps the company and the wannabe candidates have the overall view of the probability of each age

to fail/pass the management trainee program

For more details, we can easily see the Joint probability (P(x and y) - x is the age category, y is the probability to pass/fail) through the table More specifically, the joint probability of age 18-21 and Pass (abbreviate as P(18-21 and Pass)) is 0.05 Do the same for others, we have P(22-25 and Pass) is 0.06 Besides, we have P(26-29 and Pass) is 0.03 In addition, P(30-33 and Pass) is 0.01 and lastly we have P(34+ and Pass) is 0.05

When it comes to the Marginal probability, we can also calculate the marginal probability of age cat 18-21 (abbreviated as P(18-21)) = P(18-21 and Pass) + P(18-21 and Fail) = 0.2 Doing the same for the others, we can calculate P(22-25) = 0,16; P(26-29) = 0,19; P(Pass) = 0,2; P(Fail) = 0,8

In this age category table, use the formula P(A/B) = P(A and B)/P(B) to take the conditional probability, then compare it with P(A) We can see that P(Pass/34+)

= 0.05/0.25=0.2 and it equal to P(Pass) Therefore these are independent events Doing the same with the other events, we can conclude that except 34+, others are dependent with event Pass

3 Academic level

High-school

graduation

This table illustrates the academic level of the candidates who participate in the management trainee program (analyzing the actual case of PwC Company) Therefore, predicting the probability of each category of academic level helps the company and the wannabe candidates have the overall view of the probability of each academic level to fail/pass the management trainee program For more details, we can easily see the Joint probability (P(x and y) - x is the academic level category, y is the probability to pass/fail) through the table More specifically, the joint probability of High-school graduation and Pass (abbreviate as P(High graduation and Pass) is 0.03, P(Undergraduate and Pass)

is 0,04 Do the same for others, we have P(Postgraduation and Pass) is 0.13 In addition, the joint probability of High-school graduation and Fail (abbreviate as P(High graduation and Fail) is 0.12, P(Undergraduate and Fail) is 0,33 and lastly P(Postgraduation and Fail) is 0.35

When it comes to the Marginal probability, we can also calculate the marginal probability of event High-school graduation (abbreviated as P(High-school

graduation)) = P(High-school graduation and Pass) + P(High-school graduation and Fail) = 0.15 In addition, we can calculate P(Undergraduate) = P(Undergraduate and Pass) + P(Undergraduate and Fail) = 0.37 Also, we can calculate P(Postgraduation) = P(Postgraduation and Pass) + P(Postgraduation and Fail) = 0.48; P(pass) = P(Pass and High-school graduation) + P(Pass and Undergraduate)+P(Pass and Postgraduation) = 0.2 In addition, we can calculate P(Fail) = P(Fail and High-school graduation) + P(Fail and Undergraduate) + P(Fail and Postgraduation) = 0.8

In this academic level table, use the formula P(A/B) = P(A and B)/P(B) to take the conditional probability, then compare it with P(A) We can see that P(Pass/High-school graduation) = 0.03/0.15=0.2 and it equal to P(Pass) Therefore these are independent events Doing the same with the other events,

we can conclude that except event High-school graduation, others are dependent with event Pass

4 The awareness of candidates about the management trainee program

since their high school

Know but not

research about it

Know and

research about it

This table illustrates the awareness of candidates about the management trainee program since their high school (analyzing the actual case of PwC Company) Therefore predicting the probability of each category of information helps the company, as well as the wannabe candidates, have the overall view of the probability of each information to fail/pass the management trainee program For more details, we can easily see the Joint probability (P(x and y) - x is the information category, y is the probability to pass/fail) through the table More

specifically, the joint probability of event Don’t know and event Pass (abbreviated as P(Don’t know and Pass) is 0.03 Do the same for others, we have P(Know but not research about it and Pass) is 0.1; P(Know and research about it and Pass) is 0.19

When it comes to the Marginal probability, we can also calculate P(Don’t know)

= P(Don’t know and Pass) + P(Don’t know and Fail) = 0.34 In addition, we can calculate P(Know but not research about it)) = P(Know but not research about it and Pass) + P(Know but not research about it and Fail) = 0.35 Also, we can calculate P(Know and research about it) = P(Know and research about it and Pass) + P(Know and research about it and Fail) = 0.40 Besides, calculate the marginal probability of Pass (abbreviated as P(pass)) = P(Pass and Don’t know) + P(Pass and Know but not research about it)+P(Pass and Know and research about it) = 0.23 In addition, we can calculate P(fail) = 0.77

In this experiment at the table, use the formula P(A/B) = P(A and B)/P(B) to take the conditional probability, then compare it with P(A) We can see that P(Pass/Don’t know) = 0.03/0.34=0.08 and it is not equal to P(Pass) Therefore, these are dependent events Doing the same with the other events, we can conclude that all are dependent events

5 Experiment

Related course

experiment

Part-time job

experiment

Full-time job

experiment

This table illustrates the common experiment of the candidates who participate

in the management trainee program (analyzing the actual case of PwC Company) Therefore predicting the probability of each category of experiment helps the company, as well as the wannabe candidates, have the overall view of the probability of each experiment to fail/pass the management trainee program For more details, we can easily see the Joint probability (P(x and y) - x is the experiment category, y is the probability to pass/fail) through the table More specifically, the joint probability of no experiment and Pass (abbreviated as P(no experiment and Pass) is 0.01 Do the same for others, we have P(related course experiment and Pass) is 0.24 Besides, we have P(part-time job experiment and Pass) is 0.09 and P(full-time job experiment and Pass) is 0.19 Lastly, we have P(extra-curricular activities and Pass) is 0.07

When it comes to the Marginal probability, we can also calculate P(no experiment) = P(no experiment and Pass) + P(no experiment and Fail) = 0.19

In addition, we can calculate P(related course experiment)) = P(related course experiment and Pass) + P(related course experiment and Fail) = 0.27 Also, we can calculate P(part-time job experiment) = P(part-time job experiment and Pass) + P(part-time job experiment and Fail) = 0.15 Moreover, we can calculate P(full-time job experiment) = P(full-time job experiment and Pass) + P(full-time job experiment and Fail) = 0.23 Besides, calculate the marginal probability of Pass (abbreviated as P(pass)) = P(Pass and no experiment) + P(Pass and related course experiment)+P(Pass and part-time job experiment) + P(Pass and full-time job experiment) + P(Pass and extra-curricular activities) = 0.62 In addition, we can calculate P(fail) = 0.38

In this experiment at the table, use the formula P(A/B) = P(A and B)/P(B) to take the conditional probability, then compare it with P(A) We can see that P(Pass/no experiment) = 0.01/019=0.05 and it is not equal to P(Pass) Therefore these are dependent events Doing the same with the other events, we can conclude that all are dependent events