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BU52018 BUSINESS ANALYTICS

创建日期：2024-04-09 17:19:03

所属分类：统计学statistics

标签： BU52018

BUSINESS ANALYTICS

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BUSINESS ANALYTICS

Module – BU52018
Level - 5
2 HOURS
ANSWER QUESTION 1 IN SECTION A AND
THREE QUESTIONS IN SECTION B
ALL QUESTIONS CARRY EQUAL MARKS
The use of approved calculators (FX83, FX85 or FX 115) is
permitted in this examination
BU52018 Page 2 of 7
SECTION A
ANSWER ALL PARTS OF THE QUESTION IN THIS SECTION
1. A travel agency would like to forecast the number of UK citizen visits abroad so that they can
develop a growth plan for their business. The agency’s statistician developed the following
model, to be estimated using quarterly data for the past 10 years (40 observations):

ܸܫܵܫܶܵ௧ ൌ ߚଵ ൅ ߚଶܴܶܧܰܦ௧ ൅ ߚଷܩܦܲ_ܲܥ௧ ൅ ߚସܳ2௧ ൅ ߚହܳ3௧ ൅ ߚ଺2ܳ4௧ ൅ ߝ௧
where:
• ܸܫܵܫܶܵ௧ is the number of UK citizen visits abroad during period ݐ, measured in millions,
• ܴܶܧܰܦ௧ is an index indicating the period to which the data point corresponds (1, 2, 3,
… 40),
• ܩܦܲ_ܲܥ௧ is the Gross Domestic Product (GDP) per capita in the UK for period ݐ,
• ܳ2௧ is a dummy variable equal to one if the observation corresponds to quarter 2 (April-
May-June),
• ܳ3௧ is a dummy variable equal to one if the observation corresponds to quarter 3 (July-
August-September), and
• ܳ4௧ is a dummy variable equal to one if the observation corresponds to quarter 4
(October-November-December).

The results of the model estimation are presented in the following table.

Dependent Variable: VISITS
Method: Least Squares
Date: --/--/-- Time: --:--
Sample: 1 40
Included observations: 40
Variable Coefficient Std. Error t-Statistic Prob.
C 95.19166 45.45449 2.094219 0.0438
TREND 0.503173 0.152674 3.295738 0.0023
GDP_PC -2.077089 1.158584 -1.792783 0.0819
Q2 1.650664 0.576868 2.861422 0.0072
Q3 2.674084 0.578551 4.622037 0.0001
Q4 0.192040 0.579254 0.331531 0.7423
R-squared 0.859137 Mean dependent var 19.58068
Adjusted R-squared 0.838421 S.D. dependent var 3.207040
S.E. of regression 1.289129 Akaike info criterion 3.483291
Sum squared resid 56.50300 Schwarz criterion 3.736623
Log likelihood -63.66583 Hannan-Quinn criter. 3.574888
F-statistic 41.47372 Durbin-Watson stat 0.915586
Prob(F-statistic) 0.000000

(a) Report the estimated model in standard form, including standard errors.
(10)

(b) Interpret the estimates of all parameters (including the constant term), paying special
attention to the units of measurement of the dependent and independent variables.
(25)

(c) Interpret the number “0.0819” that appears under the “Prob.” column and on the row
corresponding to the “GDP_PC” variable on the table above.
(10)
BU52018 Page 3 of 7

(d) Interpret the F-statistic of the model (clearly state the null and alternative hypotheses
and whether you reject the null or not and at what level of significance).
(15)

(e) Interpret the R2 of the model.
(10)

(f) Test at the 5% significance level the hypothesis that the parameter associated with the
“Q4” variable is greater than 1.0.
(15)

(g) The residuals from the model are plotted against the time index in the figure below.
What deviation(s) from the Gauss-Markov assumptions do you identify from this plot?
What are the consequences of this deviation for the Ordinary Least Squares estimator,
in general?
(15)

-3
-2
-1
0
1
2
3
5 10 15 20 25 30 35 40

BU52018 Page 4 of 7

SECTION B

ANSWER THREE QUESTIONS IN THIS SECTION

2. SeaShellsByTheSeaShore Investments is a small retirement fund that usually invests in low-
risk, low-tech stocks. However, the fund’s CEO recently learned about a new start-up, high-
tech company with great potential for profitability, but also high risk:

 the probability of such a start-up surviving the first year,  SP , is only 10%
 the probability of such a start-up failing in the first year,  SP , is 90%

If the start-up survives the first year then it will turn highly profitable.

Because SeaShellsByTheSeaShore Investments’ analysts are unfamiliar with the hi-tech
market, the CEO decides to hire an external consultant, HappyDay Analysts, to predict whether
the start-up company will survive the first year. HappyDay Analysts are not always correct in
their predictions: they are known to be overly optimistic about the prospects of start-ups. In their
website they provide the following data:
 “out of 100 start-ups that survived we predicted correctly on 80!”
 “out of 900 start-ups that failed we predicted correctly on 540!”

Do the following, showing the steps of your derivations:

(a) Calculate the probability of HappyDay Analysts predicting that a start-up will survive
given that it survived –  SPSP .
(10)

(b) Calculate the probability of HappyDay Analysts predicting that a start-up will fail given
that it failed –  SPSP .
(10)

(c) Use the law of total probability to calculate the probability of HappyDay Analysts
predicting that a start-up will survive –  PSP .
(20)

(d) Use Bayes’ theorem to calculate the probability of a start-up surviving given a prediction
from HappyDay Analysts that it will survive –  PSSP .
(20)

(e) Calculate the probability of the start-up failing given a prediction from HappyDay
Analysts that it will survive –  PSSP .
(10)

(f) Calculate the probability of HappyDay Analysts predicting that the start-up will fail – PSP .
(10)

(g) Calculate the probability of the start-up surviving given a prediction from HappyDay
Analysts that it will fail –  PSSP .
(20)

BU52018 Page 5 of 7

3. A health insurance company offers a policy according to which it charges an annual fee
(premium) to a customer (policy holder) and, in return, it covers all costs resulting from visits of
the customer to hospitals or medical doctors during the year. The current level of the premium
is £150 and the cost per visit to a hospital/doctor is £40.

The company’s manager is currently reconsidering the value of the premium because he is
concerned that the expected profit (revenue from the premium minus expected cost from visits
to hospitals/doctors) from this policy may be negative. To make an informed decision, the
manager collects data on the number of visits to hospitals/doctors during the previous year from
100 policy holders. The data appear on the following table.

# of visits to
hospitals/doctors
in the past year
# of policy
holders with
this many visits
0 5
1 19
2 26
3 22
4 13
5 8
6 4
7 2
8 1
Total 100

Please do the following while using appropriate notation and showing your workings.

(a) Calculate the average number of visits to hospitals/doctors per year by a policy holder.
(20)

The insurance company’s manager decides to model the number of visits to hospitals/doctors
per policy holder as a random variable which follows a Poisson distribution with parameter, μ,
equal to the number you identified in (a).

(b) What is the probability of a policy holder visiting a hospital/doctor no more than 2 times
in a year if the number of visits follows a Poisson distribution?
(15)

(c) Assuming that the number of visits to hospitals/doctors per year does indeed follow a
Poisson distribution, what is the probability that a policy holder will visit a hospital/doctor
more than 6 times in a year?
(15)

(d) Assuming that the number of visits to hospitals/doctors per year does indeed follow a
Poisson distribution, what is the probability that a policy holder will visit a hospital/doctor
between 2 and 6 times in a year (margins included)?
(20)

(e) What is the probability that the insurance company makes a loss from a given policy
holder? Please explain.
(15)

(f) What is the expected profit per customer? Please define random variables explicitly
and show your working.
(15)

BU52018 Page 6 of 7

4. A large corporation frequently hires data scientists to expand its operations, as well as to fill
posts that become vacant from their own data scientists moving to other companies. For that
reason they need to have good knowledge of the average salary of a data scientist in their
industry. Until now the hiring manager of this corporation has been operating under the
assumption that the average salary of a data scientist is £50,000 and she has been making
offers to job candidates at that rate. However, due to recent trends in the industry, she suspects
that the mean salary has gone up. To examine this possibility she collected data on 49 newly-
hired data scientists and she found that the average salary offered to them was £54,000, with
a standard deviation of £9,000 (sample variance of £281,000,000)

(a) Set up appropriate null and alternative hypotheses to test the claim that the mean
salary of a data scientist in the industry is above £50,000.
(20)

(b) Test the hypothesis that you identified in (a) at the 5% level of significance. Should the
hiring manager alter the salary she has been offering to job applicants?
(30)

Until now the hiring manager has been operating under the assumption that the standard
deviation of data-scientist salaries is £10,000, but given the new data she obtained, she
suspects that the variability in salaries has gone down.

(c) Set up appropriate null and alternative hypotheses to test the claim that the standard
deviation of data-scientist salaries is greater than £10,000 (variance greater than
£2100,000,000).
(20)

(d) Test the hypothesis that you identified in (c) at the 5% level of significance. Can it be
concluded that the variability in salaries is below the £10,000 threshold used until now?
(30)

BU52018 Page 7 of 7

5. A class of 12 university students takes a test on Business Analytics. The invigilator assigned to
the class recently read an article which stated that 15% of all people are left handed and he
wants to evaluate whether this claim has any credibility. Let X be the number of students in the
class who are left handed.

Please answer the following questions using appropriate notation and showing your workings:

(a) What distribution does X follow if 15% of all people are left handed? State the name of
the distribution and the value(s) of its parameter(s)?
(15)

(b) What is the probability of exactly 3 students being left handed if 15% of all people are
left handed?
(10)

(c) What is the probability of no more than 3 students being left handed if 15% of all people
are left handed?
(10)

(d) What is the probability of 4 or more students being left handed if 15% of all people are
left handed?
(15)

(e) What is the probability of 2 to 7 students being left handed (margins included) if 15% of
all people are left handed?
(20)

(f) If 15% of all people are left handed, what is the expected number of left-handed
students in the class?
(15)

(g) If 15% of all people are left handed, what is the expected number of right-handed
students in the class? Please define random variables explicitly and show your working.
(Here you can assume that people are either left or right handed, not both).

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EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 ACCOUNTING BUSS1040 CHEM 181 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