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1.1 Create a plan for the collection of primary and secondary data for a given business problem

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Page no

Introduction                                                                                                                           2

1.1 create a plan for the collection of primary and secondary

data for a given business problem                                                                                   2


1.2 present  the survey methodology and sampling frame used                                4


1.3 design a questionnaire for a given business problem                                           6

2.1 create information for decision making by summarising data

using representative values                                                                                              7


2.2 analyse the data to draw valid conclusions in a business context                      10


2.3 analyse data using measures of dispersion to inform a given

business scenario                                                                                                   12


2.4 explain how quartiles, percentiles and the correlation coefficient are

used to draw useful conclusions in a business context                                              13


3.1 produce graphs using spreadsheets and draw valid conclusions based

on the information derived                                                                                     15


3.2 create trend lines in spreadsheet graphs to assist in forecasting for

specified business information                                                                             16


3.3 Prepare a business presentation using suitable software and techniques to   

     disseminate information effectively                                                                             17


3.4 Prepare a formal business report                                                                               18


4.1 use appropriate information processing tools                                                          18

4.2 Prepare a project plan for an activity and determine the critical path                  19

4.3 Use financial tools for decision making                                                                    21


Conclusion                                                                                                                           22


References                                                                                                                           23







In business, making good decisions requires the effective use of information. Business Decision Making provides the opportunity of learning a variety of sources and develops techniques in four aspects of information: data gathering, data storage, tools available to create useful information and presenting.

Moreover, using appropriate IT software and spreadsheets for data analysis and the preparation of information provides the advantages of using information systems which is currently used at all levels in every organisation.

Everpia London, a company with 100% Korean invested capital is the owner of high-grade Everon bedding- a reputable brand for lots of consumer in London. It has already 3 factories in Luton and Essex and now wants to establish one more factories in order to expand its activity.

Business Decision Making Purpose and aim of this assessment will give the author opportunity to examine a variety of data sources. For any of the given scenarios, he will collect data from different sources by using variety of methods, and will use spreadsheets and other software for data analysis and the preparation of information in an effective manner. Conclusions on the basis of data analysis are required to clarify the importance and use of different data analysis techniques.

1.1 Prepare and implement a plan for the collection of primary and secondary data

The Evepia’s criteria in choosing the most appropriate location for the new factory embraced a plenty of small and medium English enterprises (SMEs), the closest geographical match, and the production capacity and behavior toward their partners which specialize in textiles in that location are good. And in order to find out the most suitable province, we have to collect both secondary and primary data.

Secondary data

The secondary data are data which have already been collected elsewhere, for some other purpose, but which can be used or adapted for the survey being conduct (Bpp, 2004, p.7). So that the author can use the secondary data to assess the first two criteria of Everpia: the province which having a large number of textiles companies and short distances between Everpia and those companies. These secondary data below are used:

• The geographical location of each province: find out location of the province which is nearest to Everpia Office in Luton (Bradfordshire). We can localize it within 50km from Luton and then searching on Google or from the Atlas Geography of London to determine the province that reach the condition.

• The area of each province: we need to find information about this to know if it suitable for the construction of the factory or not. We can find it on the General Statistics Office of London (GSO)’s website, in Statistics data: land used by province. These data bring us the information of the land use on different purposes so that we can know how many areas have been used and if Everpia can build a factory there or not.

• The number of SMEs which specialized in textiles: find out the province that has textile is the leading economic power or which province has the big concentration of small and medium textile companies. This kind of information can be found from the Department of Planning and Investment (DPI)’s website of each province. For example: the website hungyenbusiness.gov.uk, use function “Finding enterprises by branch”. It will provide us the accuracy source on registered firms and their kind of business. And a very important thing that we need to know is the legitimacy of the company. So we must contact a Chamber of Commerce or Government Trade Office to verify their legitimacy.

•Population of each province: this data help us to figure out where the human resource is abundant and favorable for employees seeking for the factory. We can search on GSO’s website; there is the data of average population by province there.

With all the secondary data above we can choose out 3 cities that have the best fits with the first two criteria that are provided by Everpia.

Besides, it is better that we can see first-hand the reality, by observation we will know clearer about the factors that can influence our decision in choosing the province.

Primary data

From the data that we have searched from the secondary data the number of SMEs which specialized in textiles, we can list out the textile companies of each province with this information:  Name of the company, the location and kinds of textile manufacturing that they do.

In order to investigate the production capacity and the firm behavior we need to collect primary data. Primary data are collected especially for the purpose of whatever survey is being conducted (Bpp, 2004, p7).

In this case, we will do a survey to investigate the companies` features to satisfy the third criteria by finding the information about fixed and operating capital, number of employees, their education and skills, management skills and the credibility. The survey methodology is presented below.

1.2 Present the survey methodology and sampling frame used


The list of textiles companies in 3 cities is called the sampling frame. After having the sampling frame we continue to pick out sample for the investigation of firm’s production capacity and their behavior towards clients. Because of the unnecessary and wasting time and money, we cannot evaluate all the companies that have in the list, we just select some of the companies to represent for the rest by using sampling method or in other words we use the sampling method to narrow the range of the surveyed firm. Here we have the list of all textile companies that have common conditions like: all located within 50km from London, all are textile companies, etc but different in province where they located in. So the stratified method is the most suitable method that we can use.

Take an example in using stratified method to pick out the sample:                                             As we have 3 strata are 3 provinces named A, B and C.

Province A has 15 textile companies                                                                                           Province B has 15 textile companies                                                                                       Province C has 30 textile companies.

Total: 60 textile companies

Assume that we are taking 12 from 60 textiles companies to investigate.

The first step is we have to calculate the percentage of each group (each cities)

% textile company in province A: 15/60.100% = 25%

 % textile company in province B: 15/60.100% = 25%

 % textile company in province C: 30/60.100% = 50%

So that our 12 samples must be:

20% of the textile company in province A = (25x12)/100 = 3 companies

40% of the textile company in province B= (25x12)/100= 3 companies

40% of the textile company in province C= (50x12)/100= 6 companies.

Now we can choose 3 companies from province A, 3 companies from province B and 6 companies from province C to investigate by survey method.

Furthermore, because Everpia wants to choose companies are belonging to three-digit sub-industries of textile: Spinning, weaving and fishing of textiles (171), Manufacture of other textiles (172) and Manufacture of knitted and crocheted fabric and articles (173) so firstly, we need to divide all the companies in each province to 3 part. Part 1, part 2 and part 3 that are belonged to three-digit sub-industries of textile: 171, 172, 173, respectively. Secondly, we choose the company based on these conditions: in each province we need to choose 3 types of sub-industries of textile and exactly the number of company that we have calculated above. For example: in province A we have to choose 3 companies and those 3 companies are from different kind of sub-industries of textile, it means that we will choose 3 companies from province A, 1 company is (171) type, 1 company is (172) type and 1 company is (173) type. It’s similar with the two remaining cities.


1.3 design a questionnaire for a given business problem

Next, our task is to find out firm’s production capacity and company’s behavior towards clients. In order to collect information from those companies, the questionnaire is used. Depending on the purpose we design the question relevant. Furthermore, we also need to identify the targeting respondent for the questionnaire to get the accurate information and we will decide which is the best method to collect the information from the questionnaire.

Here we have to investigate about the company capacity as well as their behavior toward the clients so we need to ask questions about the capital (include fixed and operating capital) of the company, their profit every year, their liquidity, debts, the official employees that the company have, level education of their employees, about their performance, the employees’ skill in textile, their amount of product that they produce in a month (year), the manager’s skill, level education of the manager, their attitude about the company’s responsibility with clients etc. So the targeting respondent for those questions is the director or the CEO of the company who knows best about the company situation as well as the data of everything in the company. And in this case, the personal interview is the best choice although it may cost more of money. In a personal interview the interviewer can avoid all the misunderstanding of the interviewee and moreover, the interview can grasp the situation and more active in colleting the necessary information for the survey, so it’s more efficient.

And because this questionnaire are designed for a personal interview so almost the questions in there are open-ended questions.

The questionnaire has the structure as below

The first is the introduction to introduce about our company and questionnaire’s purpose.

The second is the content of the questionnaire:

About the company’s situation at present: 6 questions

About the employees and managers of the company, the assessment of their skill: 10 questions

About their attitude with clients: 3 questions

About the company’s equipment: 2 questions

About the company’s partnership: 2 questions.

The detailed questionnaire is in the appendix 1.

2.1 create information for decision making by summarising data using representative values


The representative values includes mean, median and mode, it can be calculated by using the measure of location. By using formula in Excel, we have this histrogram:


Expected salary per year:

 Mean             48,6997         

 Median          40,000          

 Mode            40,000

 First quartile 30,000          

 Third quartile 50,000         

 Skewness    8.862355137

With this variable, the median and the mode are the same but different with the mean.

The mean is 48.6997992

   * The average salary that the students at the University of New South Wales, UK expect each year is ≈£48,700 per year.

The median is 40,000

   * It means that there are 50% students expect their salary each year above £40,000 and 50% of students expect their salary per year below £40,000.

The mode is 40,000

   * £40,000 is the salary that has the highest choice for the expected salary of students at University of New South Wales, UK.

The first quartile is 30,000

   * 25% students at University of New South Wales, UK expect their salary equal or less than £30,000/year.

The second quartile is the median.

The third quartile is 50,000

   * 75% students of University of New South Wales, UK expect their salary equal or less than £50,000 per year.



 Mean        22.48394           

 Median          21      

 Mode            22        

 First quartile 20      

 Third quartile 24     

 Skewness    6.565967484




Age Distribution of New South Wales students.

With this variable, the mean, mode and median are nearly the same.

The mean is 22.48394

* The average age of the students at University of New South Wales, UK is ≈ 22,5.

The median is 21

 * In the University of New South Wales there are 50% of students have the age above 21 and 50% of students have the age below 21.

The mode is 22

 * The number of student aged 22 is the most popular in the University.

The first quartile is 20

 * 25% students aged equal or less than 20.

The third quartile is 24

* 75% students aged equal or less than 24.

* When there are extreme values in a set of data, we prefer to use mode or median, because the mean will be affected by the extreme values so it makes the result inappropriate.

With the data of culture back-ground, because it’s qualitative data so we cannot find the mean or median we just find out that UK is the culture back-ground that occur the most in the data.

2.2 analyse the data to draw valid conclusions in a business context


To calculate the skewness of data distribution, we use the Coefficient of Skewness formula.

 If C of S is nearly +1 or -1, the distribution is highly skewed.

In this case, we can see that both C of V of age and expected salary are positive

   * The distribution is skew to the right.

Because the skewness of expected salary is higher than the skewness of age so the expected salary is more spread out.

We can see the histograms which are drew below to check the data distribution that we have commented.


Salary                         Frequency                Cumulative %           

 100,000-200,000                 9                          99.20%      

 >200,000-800,000               4                         100.00%    

 >800,000                             0                          100.00%


A suitable survey methodology in terms of population, sample and sampling methods that could be used to collect the New Wales University’s studentss salary, wages about their opinions on the different range available on the market is the different types of sampling methods, which are used to gathering information about a studentss. The alternative would be to test, measure or question every member of the students, this might be impractical because It could take too long, difficult to access all items in students such as their national insurance and tax contribution, it’s too expensive and total students size may be unknown.

Advantages of sampling; is that it saves time and money, and sometimes can be the only option. Disadvantages are that sampling error, this is when sample is not representative of total university’s expenditure, this bias can be computed and analysed. Non sampling errors, this is when missing data, defective questionnaires etc, these cannot be computed and analysed so sampling must be planned and carried out well.

2.3 analyse data using measures of dispersion to inform a given business scenario          

Cross table showing the distribution of data by cultural background and expected salary


Count of expected salary                                 Cultural background   

Expected salary      English            Chinese   Germanic Indian Latin Slavonic Other       

 1000-20.000             53                      6                    0              0          0          0            2   

 >20.000-40.000       163                 26                 3                4          2          0           23  

 >40.000-60.000       92                    23                    1           8             0          4          10   

 >60.000-80.000       20                    6                     3             4           0          1           7    

 >80.000-100.000     15                   3                     1             0           1          1           2    

 >100.000-200.000     2                     3                     0             1           0          1           2    

 >200.000-800.000     0                     4                     0             0           0          0           0    

 Total                         350               67                       8            17          3          7          46   


Look at the table above, we can see that almost the culture back-ground of students at University of New South Wales, United Kingdom. With 350 students out of total 499 students, the number of English students will influence the number of the other culture back-ground so we cannot see the relationship between the culture back-ground and the expected salary of the students correctly.


Cross table showing the distribution of data by gender and expected salary

 Count of expected salary                                          Gender      

 Expected salary                              Male                                    Female  

 20.000-40.000                                 97                                           125     

 >40.000-60.000                              53                                             85       

 >60.000-80.000                            19                                              22        

 >80.000-100.000                           7                                               16        

 >100.000-200.000                         5                                                4         

 >200.000-800.000                         0                                               4          

 Total                                           206                                             292       


Look at the table above, we can see that at the different salary from less than £20,000 to £100,000 the number of male and female is similar (because there are more female than male here). But the female are tend to expect the salary higher than the male. There are 8 females have the expected salary from over £100,000 to £800,000 per year while there’re only 5 males who want to have the salary over £100,000 per year. May be the male are much more realistic than the female so they just expect the lower salary than the female.

2.4 explain how quartiles, percentiles and the correlation coefficient are used to draw useful conclusions in a business context

Quartile: ‘Quartile represents the middle value between two quarters of a distribution’ (Dransfield, 2003). The lower quartile is the value between the first and second quarter of the distribution. The upper quartile is the value between the third and fourth quarter of the distribution. The middle quarter is called median.

Percentile: Percentiles are values that divide a sample of data into one hundred groups containing equal numbers of observations. For example, 30% of the data values lie below the 30th percentile (Easton and McColl, 2010).

Coefficient: the coefficient of variation measures the spread of a set of data as a proportion of its mean. It is the ratio of the sample standard deviation to the sample mean. It is often expressed as a percentage (Easton and McColl, 2010).


Quartiles, deciles, and percentiles divide a frequency distribution into a number of parts containing equal frequencies. According to the 50% students expect their salary each year above £40,000 and 50% of students expect their salary per year below £40,000. The items are first put into order of increasing magnitude. Quartiles divide the range of values into four parts, each containing one quarter of the values. Again, if an item comes exactly on a dividing line, half of salary of students counted in the group above and half is counted below. Similarly, deciles divide into ten parts, each containing one tenth of the total frequency, and percentiles divide into a hundred parts, each containing one hundredth of the total frequency. If we think again about the median, it is the second or middle quartile, the fifth decile, and the fiftieth percentile. If a quartile, decile, or percentile falls between two items in order of size, for our purposes the value halfway between the two items will be used. Other conventions are also common, but the effect of different choices is usually not important.


3.1 produce graphs using spreadsheets and draw valid conclusions based on the information derived


The line chart shows the trend which took place in sales of domestic and international markets during the four-year period from 2003-2006. As we can see in the chart, the trends of two markets were contradictory. Firstly, in 2003 there was a big different between the sales of domestic and export markets because the export sales was 10 times higher than the domestic sales with £80,5m and £8,4 m, respectively. However, in 2004 the export sales sudden plunged significantly from £80,5m to £37,4m and continued to go down in subsequent years and remarkable decrease to £1,8 m in 2006 while the domestic sales from 2003-2006 were erratic a little bit but in general it still remained stable, from £8,4 m in 2003 to £13,8 m in 2006. It can be said that Everpia Company has found the stability in domestic market but they were losing their place in the international markets although they used to be successful before with the high sales. So it’s necessary that the company need to attach more special importance to the international market by some prosper strategy and plan in marketing or in the quality of product in order to affirmed the company’s brand and attract more foreign partner.








3.2 create trend lines in spreadsheet graphs to assist in forecasting for specified business information



(This tread line chart has drawn by J. Scott Armstrong is quite hypothetical http://bit.ly/bj8IKv)

The chart above illustrates the use of a third order polynomial and projects a significantly higher 30-period outcome of the sales information.

The forecasting problem is integral to successful enterprise such as Everpia Company. Yet, many senior executives, managers, and administrators have difficulty interpreting the meaning of a given time-series forecast based on methodology alone in the business information.

We have used the forecasts generated by each method are quite different from each other. Moreover, the methods depicted above illustrate only a small sampling of the most common time-series forecasting techniques in use today. Other forecasting methods include such techniques as autoregressive moving averages, generalized autoregressive conditional heteroskedastic methods, multivariate forecasting methods, and a long list of other advanced techniques in frequent use by companies.


3.3 Prepare a business presentation using suitable software and techniques to disseminate information effectively


Power Point, Excel will be the medium for the presentation to the CEO and other high ranking officers of the company. The author chose this medium because Power Point is powerful, easy-to-use presentation software that is part of the Microsoft Office suite of products. We can use Power Point to create presentations for a wide variety of audiences and for a wide variety of purposes. PowerPoint tenders a number of advantages over traditional methods for presentations. Some advantages include the following:

--Easy to edit

--Professional appearance

--Can be used in a small classroom up to a large auditorium


--Slides can`t get lost

--Allows for dynamic content on slides, in the form of animations, multi-media inserts, etc.

--Can easily be exported to the web for viewing

The image the author wants to portray is strong confidence, business capability, and wide-ranging knowledge of the distance learning market, including the current demand of that market. It is essential that the author speak with confidence and belief, so that the CEO and other high ranking officers of the company will see that the author really believe in what speaking of. The information the author will present will have the exact data which are usually required for a research paper (Introduction, Body, and Conclusion).

As a final point, the image the author will portray would most likely encompass a positive reflection of distance learning and how my company will undeniably be a valuable and profitable business endeavor.

3.4 Prepare a formal business report

February 3, 2011

Ms. Leonore Fielding

Vice-President of Operations

Everpia Group, London,



Dear Ms. Fielding

The attached report, which you requested on January 1, represents our findings regarding the survey publications at bedding Manufacturing.

Our report includes an assessment of current publications at London as well as an analysis of the current and future communication needs of your company.

The communications action plan outlined in our report reflects the results of our research both within the company and in the national and international marketplace. We are especially grateful to the Everpia staff, in particular the members of the communications group, for their input.

I look forward to discussing our recommendations with you and will be happy to meet with you and your staff regarding our report and its exciting implications for Pemberton.


Mr. X

4.1 use appropriate information processing tools

All the business organisations require information for following purposes;

   * Planning

   * Controlling

   * Recording transactions

   * Performance measurement and

   * Decision making

In order to fulfil those purposes, good quality information is needed. There are three types of information. They are Strategic, Tactical and Operational information.

Strategic information is used for planning the organisation’s long term objectives and goals and the senior managers are involved. It is both quantitative and qualitative but the future cannot be predicted.

Tactical information is short and medium term planning in which productivity measurements are included. It is generated internally and external component are limited. It based on quantitative measures and prepared regularly.

Operational information is used to ensure specific tasks properly within a factory or office. It is relevant to day-to-day plans which involve all managers. It is largely quantitative and task-specific.

In Management Information System (MIS), the installation of new IT will be identified as Operational decision.



4.2 Prepare a project plan for an activity and determine the critical path

This section shall contain, either directly or by reference, plans for the supporting functions of the software project. Supporting functions include (but may not be limited to):

 • Software quality assurance,

 • Verification and validation plans,

 • Production support and operational support functions.

 Work Packages, Schedules, and Budget

The Gantt Chart shows the a project plan analysis dateline.


This section of the Project Management Plan will specify the work packages, identify the dependency relationships among them, state the project resource requirements, provide the allocation of budget and resources to work packages, and establish a project schedule.

 1 Work Packages

This subsection will define the work packages (work breakdown structure (WBS)) for the activities and tasks that must be completed in order to satisfy the project agreement. Each work package must be uniquely identified; identification may be based on a numbering scheme and descriptive title. A diagram depicting the breakdown of activities (Gantt Chart) may be used to depict a hierarchical relationship among work packages.

2 Dependencies

This section will state the ordering relations among work packages to account for interdependencies among them and dependencies on external events. Techniques such as dependency lists, activity networks, and the critical path method may be used to depict dependencies among work packages.

3 Resource Requirements

Identifies, as a function of time, is estimated of total resources required to complete the project. Numbers and types of personnel, computer time, hardware, software, office facilities, travel, training, and maintenance requirements are typical resources that should be specified.

4 Budget Requirements

Identifies, as a function of time, is estimated of total budget dollars required to complete the project.

4.3 Use financial tools for decision making

Financial tools underlie any process of intelligent decision making. Most families seeking to reform their financial habits focus on tactics rather than strategy. These are two different terms that are often confused. Tools refer to a long-term plan with set criteria for success. Tactics are shorter term plans that are used to push forward to the goals laid out by the strategy. A tactic might be something like deciding to cut down on energy usage to reduce electricity bills.

Changing Habits

Improving financial decision making can be the accumulation of small choices made over a longer period of time combined with some major decisions, such as a career change or returning to school. In some cases, a decision to save money may result in higher risk incurred to the household. For example, anyone can save money by dropping their health insurance plan, but it exposes them to more financial risk from illness or injury.

Getting Help

Learning how to make better financial decisions can be made easier by getting assistance from others. Professional financial advisers can assist households in constructing sensible budgets, coming up with debt repayment plans and allocating resources for retirement. Self-education in matters of investment, better spending practices and reforming deleterious habits are also quite useful.

Big Choices

Adopting more frugal habits such as eating at home, paying down credit card debt and negotiating down cell phone bills are all beneficial financial decisions, but their benefits pale in comparison to the potential benefits of making major life changes. Moving into a smaller house, taking time to get additional education to qualify for better jobs or perhaps starting a new business are all examples of major--and risky--financial decisions with life-changing effects.


In Business Decision Making, using a variety of sources for data collection in order to analyse the different costs of business is presented. It also applies a range of techniques for analysing data for the purpose of business. By using different kind of graphs show the business how to draw valid conclusions based on the information and leads to prepare a formal business report. Moreover, management information systems suggest suitable information tools for the different levels of the organisation through calculation and a diagram.












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Appendix 1:

Company’s current situation

1. Who are your key customers and where are they (current and prospective)?

                          A. What are their problems, needs and wants


2. What benefit can your company provide these customers that they can’t obtain elsewhere?


3. Where are you now?


A. Where do you want to be in 1 year, 3 years, 5 years and beyond?


4. Why do you want to be there?


5. What problems must you overcome to get there?


6. What methods, tools and strategies are now being used to get there?

Client que

International House, 12 Constance Street, London, United Kingdom,
E16 2DQ

Company # 11483120

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