Questionnaire Design for Business Research
Disclaimer: This dissertation has been submitted by a student. This is not an example of the work written by our professional dissertation writers. You can view samples of our professional work here.
Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.
INTRODUCTION:
Many businesses, economic and social questions are not amenable to a simple YES or No answer. Every business needs some clarification or discussion. Solutions can be presented and every criterion can be either accepted or rejected. To consider the arguments and indeed the facts presented, the completeness of current information and the requirements for new information need to be assessed.
Decision maker not only need the DATA but also need to evaluate the quality of the data. One Dictionary definition of data is ‘things known and from which inferences may be deduced'.
Data refers to information or facts usually collected as the result of experience, observation or experiment, or processes within a computer system, or premises. Data may consist of numbers, words, or images, particularly as measurements or observations of a set of variables. Data are often viewed as a lowest level of abstraction from which information and knowledge are derived.
Data in business means numerical values such as size of a business, its profitability, its product range, the features of the workforce and host of other factor. However numbers alone cannot give clear understanding of the business problems therefore it is important to consider the entire factor that affects the business during its existence such as legal, economic, culture and etc.
In general business, require a multi disciplinary approach.
The completeness of data is always a problem for the decision maker. Collection of data is always vast, but it depends on the decision maker to decide whether the current data is enough or more additional has to be collected.
Collection data is a tedious job and costly also. The main issue for the decision maker is that whether the data has some quality information or no. Data that has bias or is misleading can damage any effective decision making process which can further affect the profit in future.
Data can be collected by different sources and most people underestimate the number of sources and the amount of data within each of these data:
 Paper based sourced: It is books, journals, periodicals, abstract, indexes, directories, research reports, conference papers, markets reports, annual reports, internal record, magazines, newspaper.
 Electronic based sources: CDROMs, online database, Internet, videos and broadcast.
Precollection activity is the most crucial steps in the collection of data. There is always a formal need of checking the data collected so as to ensures that the data collected defined and accurate and the finding in the collection of data are valid and not bias.
In this Globalization century it is important to be abreast with the updated information and data so as to have a competitive edge.
1. Company Overview:
My research is based on Dominos:
Starting in business with his brother James in 1960, Tom Monaghan brought a pizza store named DomiNicks in Ypsilanti, Michigan. A year later, James traded his interest in the store for a Volkswagen. Tom formed another partnership and, during the next three years, continued to open stores in Mt Pleasant, Ann Arbor and Ypsilanti. In 1965 that partnership was dissolved, leaving Tom with one store in Ann Arbor and two in Ypsilanti.
When Tom was searching for the name for his new corporation, a driver suggested the name ‘Dominos'. The name was adopted and Tom helped create the now familiar red and white threedot logo.
Through hard work and dedicated team, Dominos grew into international leader in the pizza delivery industry, with over 8,000 stores in 50+ markets.
December 1998, saw a change in ownership for Dominos pizza when Bain Capital, a Boston based private equity investment firm, purchased Domino's from Mr. Monaghan.
The new leadership has brought an even stronger focus to operational quality and growth, as well as renewed commitment to recruiting and developing exceptional people.
To forward the goals, David A Brandon was named Chairman and Chief Executive Officer of Domino's Pizza, LLC in March, 1999.
Dominos pizza first opened its UK store in 1985 and has over 500 stores now in UK and Ireland.
TASK 1:
1. Sources of Data Collection:
Nowadays data collection is become very important in this Economic world .When there are many business ,economic, and social question they are not amendable by a simple yes or no, So here to consider the argument and indeed the ‘facts' presented, the completeness of current information and the requirement for new information need to be assessed. According to Jon Curwin and Roger Slater, Third edition 1991, stated that one dictionary definition of data is ‘things known and from which inferences may be induced'
Appraisal and market studies use two types of data Primary data and Secondary Data. All the data collected should be current, relevant, accurate and conceptually correct. Primary data and Secondary data are defined in The Dictionary of Real Estate Appraisal as follow:
Information that a researcher gathers first hand is primary data.
Information from secondary sources i.e. not directly complied by the analyst may include published and unpublished work based on research that relies on primary sources of any material other than primary sources used to be prepare a written work.
Decision makers not only data but also the quality of the data because Data that are bias or misleading can damage any effective decisionmaking process. Whenever we look at data or consider data collection we need to ask ‘what is the problem?' or ‘what is the question?'Basically there are two types of sources of data 1) Primary Data and 2) Secondary Data.
A) PRIMARY DATA:
Primary data is facts and information collected specifically for the purpose of the investigation at hand. Primary data is collected specifically to address the problem in question and is conducted by the decision maker, marketing firm, a university and etc. Primary data cannot found elsewhere. Primary data may be collected through surveys, focus groups or in depth interviews, or through experiments such as taste tests.
According to Jon Curwin and Roger Slater, Third edition 1991, stated that a statistical enquiry may require the collection of new data, referred to as primary data, or be able to use existing data, Primary data is its collection for a specific project.
Advantages:
 Basic data are included in primary data collection.
 It is unbiased information
 It is the information that is collected originally.
Disadvantages:
 Data collected is large in volume
 It is time consuming
 Direct and personal intervention has to be there to collect the data
 The data collected is raw.
For example:
A distribution census, taken every five years, dealing with retail data
Ø Population census which has been carried out in the U.K in every 10years since 1801 ,this exercise gives highly detailed information and reflect data from all part of the population
EXAMPLE:
Metro Newspaper, Thursday, May 14, 2009.
BANKS are slowing down Britain's economic recovery by not lending, it was claimed yesterday. The Banks some of which have been propped up with billions from the taxpayer are displaying an ‘extreme level of risk aversion' when lending to businesses and households, Banks of England governor Mervyn King said. The warning came as the Bank predicted the economy would shrink by 4.5 per cent at the peak of the recession in the summer. Consumer price inflation currently at 2.9 per cent target this year. However, a weak pound, the impact of 0.5 per cent interest rates and government spending offered hope of recovery, Mr King added in his quarterly inflation report.
http://goliath.ecnext.com/coms2/gi_0199934872/Primaryandsecondarydataconcepts.html
B) TYPES OF METHODS OF COLLECTING PRIMARY DATA:
 Questionnaire
 Interviews
 Focus Group Interviews
1. Questionnaire:
Questionnaire are a popular means of collecting data, but are difficult to design and often require many rewrites before an acceptable questionnaire produced. Questionnaire is the series of question to be asked to an individual so as to obtain statistically useful information about any given task. It became a vital instrument if it is constructed and responsibly administered. It is frequently used in quantitative marketing research and social research. They are valuable method of collecting a wide range of information from large number of individuals, often they are referred to as respondents. Good questionnaire construction is important for the success of a survey. Inappropriate question, incorrect order of question, incorrect scaling, and bad format can make the questionnaire worthless. In order to have a successful questionnaire it is important to have the subset of target respondent to be tested first.
Advantages:
 It can be used as a method in its own right or as a basis for interviewing or a telephone survey.
 It can be posted, emailed or faxed.
 It can be used for large volume of people or organization
 It has wide geographic coverage.
 It is relatively cheaper
 No prior arrangements are needed.
 It avoids embarrassment on the part of the respondent.
 Respondent can consider responses.
 There is a possibility of respondent being anonymous
 There is no Interviews bias.
Disadvantages:
 Designing the questionnaire is a problem
 Questions have to be relatively simple.
 It has low response rate.
 It is time consuming whilst waiting for the response to be returned.
 It requires return deadline.
 Several remainders are required while conducting the questionnaire.
 It assumes no literacy problems.
 There is no control over who completes the questionnaire.
 It is not possible to give assistant if required.
 There is a problem with incomplete questionnaire.
 The replies are not spontaneous and independent of each other.
 Respondent can read all questions beforehand and then decide whether to complete or not may be because it is too long complex, uninteresting, or too personal
1.1 SUCCESSFUL QUESTIONNAIRE DESIGN:
To be successful, a questionnaire needs both a logical structure and well thought out questions. The structure of the questionnaire should have a flow from question to question and from topic to topic, just like the conversation between two people. Any radical jump between questions or topic would create a problem or confusion to the respondent. It is often suggested that a successful and useful technique is to move from general to specific questions on any particular issue.
The Gallup organization has suggested that there are five possible objectives for a question:
 To find if the respondent is aware of the issue
 To get general feelings on an issue
 To get answer on specific parts of the issue
 To get reasons for a respondent's views
 To find how strongly these views are held
1.2 DESIGN OF POSTAL QUESTIONNAIRE:
Theme and covering letter:
The general theme of the questionnaire should be explicit in a covering letter. You should state who you are, why the data is required, give if necessary, an assurance of confidentially and/or anonymity and contact number and address or telephone number. This ensures that what respondent is known what they are committing. If possible, you should offer estimate time for completion. Instruction for return should be included with the return date made obvious.
Instruction for completion:
You need to provide clear and unambiguous instruction for completion. There should be a general instruction for particular question structure. The response method should be indicated (circle, tick, cross and etc). Even example can be given to make question clearer.
Appearance:
Appearance is the first thing which the recipient reacts. A neat and professional look will encourage further consideration of request, increasing your response. To improve the questionnaire appearance:
 Liberal spacing makes the reading easier.
 Photo reduction can produce more space without reducing content.
 Consistent positioning of response boxes, usually to the right speeds up completion.
 Choose the font style to maximize legibility
 Differentiate between instruction and question.
Length
The length of the questionnaire should not be that long because this could affect the completion of it and respondent may be uninterested to complete.
Order:
The most important and crucial stage in questionnaire is the beginning. Once the respondents have started to complete the question they will normally finish provided if it not too long or difficult.
Coding:
It is advisable non numerical responses when designing the questionnaire rather than trying to code the responses when they are returned.
Thank you:
Respondents to questionnaire rarely benefits personally from their efforts and the least the researcher can do is to thank them. Even though the covering letter will express appreciation for the help given, but it is always advisable to thank the respondent at the end of the questionnaire.
Question:
Question asked should be short, simple and to the point avoid any unnecessary words. It shouldn't confuse the respondent as it could affect the completion of questionnaire.
Types of Questions:
 Contingency question: A question that is answered only if the respondent gives a particular response to a previous question. This avoids asking questions of people that do not apply to them.
 Matrix question: Identical response categories to multiple questions. The question are placed one under the other, forming a matrix with response categories along the top and a list of question down the side. This is efficient use of page space and respondents' time.
 Close ended question: Respondents' answers are very limited to a fixed set of responses. Other types of closed ended question include:
Yes or No question: The respondent answer with a ‘yes' or a ‘no'.
Multiple choices: The respondents are given with several options from which to choose.
Scaled question: Responses are graded on a scale for e.g. rate the food quality scale from 1 to 10, with 1 being the least preferred and with 10 being most preferred.
 Open ended question: No option or predefined categories are suggested. The respondent gives their own answer without being constrained by fixed set of possible responses.
2) Interviews:
Interviewing is a technique that is primarily used to gain an understanding of the underlying reasons and motivation for people attitudes, preferences or behavior. Interviews can be undertaken one to one basis or in group. There different types of interview that can be conducted such as personal interview and telephone interview. Interviews can be structured, semi structured and unstructured.
A personal interview has a serious approach by respondent resulting in accurate information. It has good response rate with completed and immediate. Interviewer can also give help to the interviewee if in case it requires some help.
There is need to the setup interviews. It is time consuming and expensive. Interviewer can even ask some personal question which could be embarrassing for the respondent.
Telephone interview is an alternative form of interview to the personal, face to face interview. It is relatively cheaper, quick and has wider coverage. It has high rate of spontaneous response.
Telephone interview is often connected with selling. It often requires questionnaire. Time is wasted if lines get disconnected and if call backs are given it could make the respondent irritate. A strong telephone manner is needed to handle the question raised by the respondent.
3) Focus Group interview:
A focus group is an interview conducted by a trained moderator with a small group of respondent. The moderator starts the discussion and then leads the same. The main purpose of the focus groups is to get the insight or complete knowledge by listening to a group of people from the targeted market about the specific issues of interest.
SECONDARY DATA:
All methods of data collection can supply quantitative data or qualitative data. When using secondary research, one must be caution when using dated information from the past. Secondary data is facts and information gathered not for the immediate study at hand but for the purpose. Secondary data is data which has been collected by individuals or agencies for purposes other those of our particular research study. Common sources of secondary data for social science include censuses, large surveys and organizational records. Secondary data is a data which is collected from primary data to create new research. A secondary data source is a summary of a book or set of records. Secondary data, Sources of primary data include observation, group discussions and the use of questionnaires.
Advantages:
 It is easily accessible and saves time that would otherwise be required for collecting data.
 The cost to access secondary data is little or no cost to acquire.
 Secondary data helps to clarify the research focus or question.
Disadvantages:
 Quality of research is questionable because the secondary data is originated from primary data research which is collected and controlled by the marketer itself.
 In many cases, secondary data is not well presented in a form that exactly meets the researcher's needs.
 In secondary research, much information is incomplete because the researcher may not get the full version of the research to gain the full value of the study. This is because many researcher suppliers offers free portions of their research and then charge expensive fees for their full reports.
Example:
 Data collected by the hotels or the organization through its history system.
 Data supplied by a marketing organization
 Annual company reports
 Government statistics
Secondary Data Analysis:
Secondary data analysis is commonly known as second hand analysis. It is simply analysis of preexisted data in a different way to answer a different question than originally needed. It analysis the data that was collected by someone else and uses it in for further study that is intended to complete.
Secondary data can be gathered by internal and external source of data collection. Where internal sources includes sales data, reports data, financial data,
Transport data, storage data and external sources includes government statistics, trade association, and commercial services
There are common sources of collecting secondary data such as from Bureau of the census, the Bureau of Labor Statistics and various other agencies.
Example:
U.S Bureau of the census has kept track of the census of the population for over two hundred years. Moreover, the census includes housing, the labor force, manufacturers, business, agriculture and so on. Census data can be used for various research questions. Anyone has access to the large amount of information nearly one hundred surveys, by visiting their website at (http://www.census.gov).
Bureau of Labor Statistics collects information or data on employment, industrial relations, prices, earning, living condition, technology and productivity. Report is out every month in this bureau and they can be viewed at (http://stats.bls.gov )
International Data Sources is a strong source for comparative researchers and can deal with economic aspects, including political events across many other nations. In Europe, a Euro barometer Survey Series is used to publish reports on social and political events in the country.
The Design and purpose of research:
Secondary data analysis means collecting the data which is collected by some other person and using the same data for understanding the current issue or problem face by the researcher. It is important to have a well defined research type which in turn would help the research to be successful. In order to use the secondary data three steps must be completed:
 Locate the data
 Evaluate the data
 Verify the data
Collecting data is easy online but to verify the data whether they care uptodate or current is important. Therefore it is important to be alert and cautious while using the online sources while collecting the data
Example:
Ethnicity, discrimination and health outcomes: a secondary analysis of hospital data from Victoria, Australia
In this study, secondary data was used in the form of hospital discharge abstract for the state of Victoria in Australia. The variables that were looked at were a person's country of birth and the quality of care they received in a universal health care system. It was secondary data because it had already been collected by the hospital in the way of their charts and discharge abstracts. The researchers were simply looking at the data and the relationship between the listed country of birth and what type of care was listed. The goal of the research was to explore the relationship between a person ethnic background and the amount of care they received from the hospital. The researchers were interested in developing a preliminary set of data that would allow them to develop methods to study the issue further.
The discharge abstract contained demographic and clinical information about each patient. From the abstract the researcher separated the patient into three groups. The first being Australian or English patients. The second group consisted of patients who did not visibly appear to be minority e.g. people from Europe, South and Central American. The third group contained people that were visible minorities e.g. Middle Easterners, Asians, Africans and Pacific Islanders.
Dominos strives to excel in customer's satisfaction. Its major competitors are Perfect Pizza with over 200 outlets, Pizza Hut with over 170 restaurants and also small pizza delivery business; it is believed that there are as many as 4,000 pizza delivery companies in UK.
It is important to have updated knowledge about the market so as to survive the recession. Dominos store in UK conducted the questionnaire to have clear idea about the market needs and customer expectation.
DOMINOS PIZZA
315 Chiswick High Road, London W4 4HH
Telephone: 020 8995 4555
http://www.dominos.uk.com/people/Current_Positions.aspx
Q1) Do you use fast food service in the area? Q2) Which fast food services do you use (if any)?
Q3) What type of fast food do you prefer?
Q4) What else do you buy when purchasing fast food?
Q5) What time do you use fast food service?
Q6) How often do you use fast food services?
Q7) How much are you prepared to pay for the fast food?

Thank you for your help! 
TASK 2:
TECHNIQUES TO ANALYSE DATA:
Data which is collected needs to be analyzed and then interpreted or technique to presented in the form that is self explanatory and easily understandable. Therefore, it is important to know the process that is included in process of analyzing the data.
Data analysis is the process of gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusion and supporting decision making. Data analysis has multiple ways, approaches and technique.
The main task is to interpret the information or the data that is collected. There are various ways to interpret the data in a simple for easy understanding. Interpretation of data is important for making a decision for the business. There are different ways or methods how a data can be interpreted, that is:
Method1:
Graphical presentation
Method 2:
Mean, Median and Mode
Method 3:
Quartile, percentile and Standard deviation.
Method 1:
Graphically presentation:
The easiest way to present the data is through graphs and diagrams. There are different graphical presentations that are used for interpreting the data or presenting the data. To show the relationship between two variables we use graphs and diagrams.
Using graph can have quick and direct understanding. It highlights the most important facts. It gives easy understanding of the data and can have a long lasting impression.
Graph can be used when the data is dispersed, few or numerous and has little or no variation.
Below is the detail for the local garage which is facing the fierce competition and wants to compete in the market with the reasonable prices.
HISTOGRAM:
Histogram is the popular graphing tool. It is used to explain discrete or continuous data that are measured on an interval scale. It is often used to present the distribution of data that is collected for the purpose. It divides the range of values in the data set into group classes. Histogram is more similar to vertical bar graph but when the data are continuous, there are no gaps between the bars. When the variables are discrete, gaps should be left the between the bars.
In histogram, frequency is measured by the area of the column and in a vertical bar graph; frequency is measured by the height of the bar.
Histogram graphically shows:
 Center (i.e. the location) of the data
 Spread (i.e. the scale) of the data
 skewness of the data
 presence of outliers and
 Presence of multiple modes in the data.
The most common form of the histogram is taken by dividing the range of data into equal classes. That is,
Vertical axis: frequency
Horizontal axis: Response variable
The histogram is a popular graphing tool used in the presentation of the data. It is used to summaries discrete or continuous data that are measured on an interval scale. It is often used to represent the major features of the distribution of the data in an easy form.
The October costs of the garage.
225 
237 
470 
319 
209 
430 
430 
246 
360 
475 
277 
241 
447 
430 
211 
425 
347 
339 
243 
310 
345 
214 
265 
389 
390 
219 
250 
489 
257 
475 
259 
493 
278 
390 
287 
388 
234 
490 
237 
346 
240 
278 
248 
375 
288 
460 
478 
350 
326 
459 
220 
289 
399 
299 
In the data the costs of the servicing may be grouped into classes as follow:
October costs Frequency Tally 
200300      25 
300400    15 
400500    14 
Tabulated (grouped) continuous data
Method 2:
Mean, Median and Mode are the most commonly used forms of average for the most business data. Each has its own characteristics, and whilst it will be possible to use them interchangeably with some data sets, for others there will be a single average which will be most appropriate. One consideration will be the type of the data with which we are dealing is it categorical, ordinal or cardinal; secondly we must ask if the data is discrete or continuous.
2.1 Mean:
The arithmetic mean is the name used for the simple average which you can already calculate. Almost everyone understands this average and thus it will succeed in communicating the concepts of the location of the data to a wide range of people. It does not apply to the apply to the categorical data and its interpretation when used with ordinal data is to open to considerable doubt. When used with discrete data it may give an answer which cannot occur, for example fractional number of people.
This is the most commonly used average. The mean is calculated by adding the given values and then dividing the sum by the number of addends.
Potential Problem:
 If you have a large number of small values with a few very large values in your sample, mean averages get skewed: the mean is nearer to the bigger values even though the small values there are smaller numbers. If you have a few small values and a few large values, the mean average can get skewed this way too.
 If you have one, or more, outlying values that do not follow the general trend of the numbers in a sample, the mean average can be affected more dramatically than intended.
There are different ways of calculating Mean in different Data:
2.1.a) Untabulated data:
Suppose that there are number of people were 7, 5, 6, 7 and 8.
To calculate the mean, we would add all the numbers together to find the total number of people taken, and then divide by the number of values included. Here, the mean would be:
That is, / 5
=33/5
=6.6
In the above example, x is pronounced as x bar is the symbol used to represent used to represent the ‘Mean'. ∑ is pronounced as sigma is an instruction to sum the values and n is the number of the values.
Since the data we have used is continuous, cardinal data, this is a realistic. It sometime states that mean means average. The mean is the arithmetic average of a set values or distribution.
2.2 Median:
The median represent the value of the middle item of an ordered list of data. It is becoming more widely used and more generally accepted and will thus communicate to a relatively wide range of people. Median can be appropriate if mean is proved to be misleading. It is again really useful for cardinal data. When used with the discrete data it will generally give an answer which has actually occurred in the data set.
2.2 b) There are different ways of calculating Median in different Data:
Untabulated data
Suppose there are number of people were 7, 5, 6, 7 and 8.
Then to find the Median we first have to arrange the data so that it is in numerical order.
5, 6, 7, 7, 8
Since there are five numbers in the data, the middle one must be the third. Counting from either side, it give us the number as 7as the Median.
As there were just five number it was easy to find the Median, but if there were ten number then we would take fifth and sixth number and then give a Median value by adding the value of the fifth and sixth number and dividing it by number 2
Note: The value we get will not be there in the data that we have arranged but the answer will not be incorrect as we have averaged the two numbers.
2.3 Mode:
In statistics, the mode is the value which occurs the most frequently in a data set. It may not be unique for certain data sets but it does apply to all types of data. The mode is not necessarily unique, since the same maximum frequency may be attained at different values.
Where the mean has problems with ‘ representativeness', mode focuses on the most common numbers and gives less or no attention to less frequently occurring numbers.
Potential Problems  Mode is less useful when you have a lot of values that are close together but have not been rounded to the nearest whole number. This means an inaccurate mode of the numbers will be taken. It would be better in this example to round the numbers first before using mode.
2.3 c) There is different ways of calculating Mode in different Data:
Untabulated data
There are number of people waiting in the queue 7, 5, 6, 7, 7 and 8.
The use of the mode for simple data is only a question of observation, looking for the most frequently occurring value
Then with the given data 7, 5, 6, 7, 7, 8
We can easily say the number that occur frequently is number 7, therefore the Mode is number 7.This gives a value which does actually occur in the data and will apply whether data is discrete or continuous, and whether it is cardinal, ordinal or categorical.
To solve Mean, Median and Mode in a given example:
Example:
If the heights of the first seven person who enter the theatre are 65 in, 72 in, 75 in, 67 in, 60 in, 65 in, find if possible, the mean
The listed heights are called as Data. From the data we determine...
Mean  65 + 72 + 75 + 67 + 60 + 65 + 65 = 469 in
There are 7 addends, so 469 ^ 7 = 67 in
Median = 60 + 65 + 65 + (65) + 67 + 72 + 75 in. The centre value is the fourth one, 65 in.
Thus: The Mean height is 67 in.
The Median height is 65 in.
The Mode height is 65 in.
Standard Deviation:
The standard deviation measures the spread of the data about the mean value. It is useful in comparing sets of data which may have the same mean but a different range
Standard Deviation becomes:
Lower case sigma means 'standard deviation'.
Capital sigma means 'the sum of'.
x bar means 'the mean'
Method 3:
1) Quartile:
A quartile is any of the three values which divide the sorted data set into four equal parts, so that each part represent one fourth of the sample.
 First quartile(designated Q1)= lower quartile=cuts off lowest 25% of data=25th percentile
 Second quartile(designated Q2)=median=cuts data set in half=50th percentile
 Third quartile(designated Q3)=upper quartile=cuts off highest 25% of data, or lowest 75%=75th percentile
The difference between the upper and lower quartile is called the interquartile range.
There is no universal agreement on choosing the quartile values.
The formula for the position of the observation at a given percentile, y with n data points in ascending order is:
Suppose there is a data set given: 6, 47, 49, 15, 42, 41, 7, 39, 43, 40, and 36.
Ordered Data Set: 6, 7, 15, 36, 39, 40, 41, 42, 43, 47, and 49.
Q1 = 15
Q2 = 40
Q3 = 43
Interquartile range also called as midspread or middle fifty.
Data set in a plaintext box plot
+++
o *   
+++
+++++++++++++ number line
0 1 2 3 4 5 6 7 8 9 10 11 12
For this data set:
 lower (first) quartile (Q1, x.25) = 7
 median (second quartile) (Med, x.5) = 8.5
 upper (third) quartile (Q3, x.75) = 9
 interquartile range, IQR = Q3 − Q1 = 2
2) Percentile:
Percentile (or centile) is the value of a variable below which a certain percent of observation fall.
One quartile is equivalent to 25 percentile while 1 decile is equal to 10 percentile.
Below is the detail for the local garage which is facing the fierce competition and wants to compete in the market with the reasonable prices.
The October costs of the garage.
225 
237 
470 
319 
209 
430 
430 
246 
360 
475 
277 
241 
447 
430 
211 
425 
347 
339 
243 
310 
345 
214 
265 
389 
390 
219 
250 
489 
257 
475 
259 
493 
278 
390 
287 
388 
234 
490 
237 
346 
240 
278 
248 
375 
288 
460 
478 
350 
326 
459 
220 
289 
399 
299 
Service Cost 
No. of cars 
Class Mark 

F 
X 
Fx 
cf 
fx2 
x2 

100200 
0 
150 
0 
0 
0 
22500 
200300 
25 
250 
6250 
25 
1562500 
62500 
300400 
15 
350 
5250 
40 
1837500 
122500 
400500 
14 
450 
6300 
54 
2835000 
202500 
Total 
54 
17800 
6235000 
410000 
Mean, Median and Mode are as follows:
Mean:
= 329.62
Median:
N= 54
Therefore, Median = 54 / 2
= 27.
The median lies in the class interval 300400
So, Median =
=
= 300 + 13.33
= 313.33
Where:
M= Median
f =frequency of the class interval 300400
c = cumulative frequency of the class interval
Mode:
The Modal class is 200300 against the highest frequency 25
So, Mode =
= 2
= 2
= 271
Where: The Modal class is 200300 against the highest frequency 25.
=frequency for the class interval 100200
Frequency for the class interval 200300
=frequency for the class interval 100200
Standard Deviation:
= 115462.963  108655.693
= √6807.27
= 82.50
Quartile:
a) Q1=n*1/4
Therefore, q1= 54*1/4
= 13.50
Q1 lies in the class interval 200300
Q1 =
=
=200 + 54
=254
b) Q2=n*2/4
Therefore, q2= 54*2/4
= 27
Q2 lies in the class interval 300400.
Q2 =
=300
=300 + 44.44
=344.44
c) Q3=n*3/4
Therefore, q3= 54*3/4
= 40.50
Q3 lies in the class interval 400500
Q3 =
=
=400 + 50
=400 +3.57
=403.57
Q3 lies in the class interval 400500
d) Q4=n*4/4
Therefore, q4= 54*4/4
= 54
Q4 lies in the class interval 400500.
Q4 =
=4
=400 + 100
=500
TASK 3:
Trevor plc business scenario can be explained by using different types of graphs as follow.
A) Pie Chart (description of components)
B) Horizontal bar graph ( comparison of items and relationship, time series)
C) Vertical bar graph ( comparison of items and relationships, time series, frequency distribution)
D) Line graph ( time series and frequency distribution)
E) Scatter graph (analysis relationship )
Year Model 
2005 
2006 
2007 
2008 
2009 
BMW 
£4M 
£4.2M 
£5M 
£5.5M 
£6.6M 
Mercedes 
£6M 
£6.3M 
£6.9M 
£7M 
£7.8M 
Lexus 
£2M 
£2.1M 
£2.3M 
£2.5M 
£2.9M 
Jaguar 
£3M 
£3M 
£2.5M 
£2M 
£2.2M 
A) PIE DIAGRAM:
Pie diagram is a circle which is divided into number of parts which suggest the contribution of each data collected. The area of each segment is the same proportion of a circle as the category is of the total data set.
It usually shows the component parts as a whole. Often you will see a segment of drawing separated from the rest of the pie in order to emphasis an important piece of information. The use of the pie chart is very popular and gives an easy explanation that last for long lasting impression. It has a visual concept of the whole (100%). Pie charts are one of the most commonly used charts as it is easy to use.
There are basically two common problem faced by the pie charts, firstly that if the component is more than it is complex to divide and explain each one of them. If the components are similar than it is difficult to see the difference between the sizes.
Year Model 
2005 
2006 
2007 
2008 
2009 
BMW 
£4M 
£4.2M 
£5M 
£5.5M 
£6.6M 
Mercedes 
£6M 
£6.3M 
£6.9M 
£7M 
£7.8M 
Lexus 
£2M 
£2.1M 
£2.3M 
£2.5M 
£2.9M 
Jaguar 
£3M 
£3M 
£2.5M 
£2M 
£2.2M 
YEAR 2005:
BMW = 4/15*360
= 96%
Mercedes = 6/15*360
= 144%
Lexus = 2/15*360
=48%
Jaguar = 3/15*360
=72%
Diagram 2005:
YEAR 2006
BMW = 4.2/15.6*360
= 96.90%
Mercedes = 6.3/15.6 *360
= 145.40%
Lexus = 2.1/15.6 *360
= 48.50%
Jaguar = 3/15.6*360
= 69.20%
Diagram 2006:
YEAR 2007:
BMW = 5/16.70*360
= 107.80%
Mercedes = 6.9/16.70 *360
= 148.70%
Lexus = 2.3/16.70 *360
= 49.60%
Jaguar = 2.5/16.70*360
= 53.90%
Diagram 2007:
YEAR 2008:
BMW = 5.5/17*360
= 116.50%
Mercedes = 7/17*360
= 148.20%
Lexus = 2.5/17*360
= 52.90%
Jaguar = 2/17*360
= 42.40%
Diagram 2008:
YEAR 2009:
BMW: 6.6/19.50*360
=121.84%
Mercedes: 7.8/19.50*360
=144%
Lexus: 2.9/19.50*360
=53.54%
Jaguar: 2.2/19.50*360
=40.62%
Diagram 2009:
Hence, According to the pie charts above the Mercedes have the highest percentage compare to all the years. This clearly shows the sales for the model of Mercedes were greatest.
Hence, the predicted sales for Mercedes in year 2009 against the cars models would be highest as assumed.
B) Horizontal bar graph (comparison of items and relationship, time series)
Horizontal is one of the type of bar graph .A bar graph may be either horizontal or vertical. Horizontal bar graph is one of the many techniques used to present data in a visual form. This graph uses the y axis (vertical line) for labeling. There is more room to fit text labels for categorical variable on the y axis.
The horizontal bar graph has been used to show a comparison of the data collected. This graph is the best method to present the type of information because the labels at times are too long to appear clear on the x axis.
Hence, According to the Horizontal bar diagram above the Mercedes have the highest percentage compare to all the years. This clearly shows the sales for the model of Mercedes were greatest. Hence, the predicted sales for Mercedes in year 2009 against the cars models would be highest as assumed.
C) Vertical bar graph (comparison of items and relationships, time series, frequency distribution)
Vertical bar graph is particularly useful for the time series data. The space for labels on the x axis is small, but it is ideal for smaller words or numbers such as years, minutes, hours and months.
The group vertical graph is another effective means so comparing sets of data about the same places or items. This type of vertical group graph gives two or more information of every item on the same axis. This makes the comparison easier on the same graph by age group, sex race and so on. But if there is too many data to compare than it become too cluttered and confusing to compare. The disadvantage of vertical graph is that it gives less space to write full information about the data on the axis.
Hence, According to the Vertical chart above the Mercedes have the highest percentage compare to all the years. This clearly shows the sales for the model of Mercedes were greatest. Hence, the predicted sales for Mercedes in year 2009 against the cars models would be highest as assumed.
D) Line graph (time series and frequency distribution)
A Line graph is a visual comparison of how variable shown on x axis and y axis related or vary with each other. Line graph compares two variable: one is plotted along on axis (horizontal) and other along is on the y axis. The y axis in a line graph usually indicates quantity or percentage, while the horizontal x axis often measure units of time.
Line graph are able to show relationship more clearly than tables do. It shows the specific values of data well, it is reveals the trends and the relationship between data and shows the comparison of different group of variable.
Hence, According to the line charts above the Mercedes have the highest percentage compare to all the years. This clearly shows the sales for the model of Mercedes were greatest. Hence, the predicted sales for Mercedes in year 2009 against the cars models would be highest as assumed.
F) Scatter graph (analysis relationship )
A scatter plot only specifies variables or independent variables when a variable that is under the control of the experimenter. If a parameter exists that is systematically incremented or decremented is called as control parameter or independent variable and it is plotted on horizontal axis. The measures or dependent variable is plotted along the vertical axis. The scatter diagram is one of the basic tools of quality control.
Diagram 2005 & 2006:
Series 1 = 2005 sales
Series 2 = 2006 sales
Diagram 2007 & 2008:
Diagram 2008 & 2009
Hence, in the scatter chart Mercedes has the highest sales irrespective of which years are compared. This proves that the assumed sales for Mercedes for the year 2009 would again be highest.
The company started commission scheme back in 2005 which entitles the salesmen a share of profit depending on certain factors. Commissions so far paid to the salesman are given below.
year 
2005 
2006 
2007 
2008 
2009 
Commission 
£15,000 
£15,500 
£16,700 
£17,000 
£17,700 
There have been a increase in commission consecutively for all salesman and therefore the commission for the year 2009 as predictable will yield more commission for the salesman as shown in the above diagram.
TASK 4:
Summary:
Every organization needs to have updated information about the data to make the decision at operational, tactical and strategic levels in organization. Success or failure of any organization largely depends upon the decision taken by the higher authority which further depends on the data or information taken into consideration.
Operational plans include the goals of an internal organization, working group or department which needs to be achieved so that the organization works smoothly and efficiently.
Management is classified into three different parts:
 Top level
 Middle level
 Operational level
Top level makes all the strategic decision for the company, Middle level is responsible for solving and tackling the problems that cannot be solved or which needs to have an acceptance from the authority and operational level takes all the decision at operational level.
It is therefore important to have an updated and complete information system in any organization to face the competition and have higher efficiencies. Information is a resource that is scarce, costly and can be used as an alternative choice.
Information is needed to ensure effective and efficient decision making leading to prosperity of the organization.
Information that supports management decision making not only for day to day business decision but also supports at strategic, tactical and operational planning, monitoring and control.
INTRANET:
An INTRANET can be understood as private version of the internet, or as a private extension of the internet confined to an organization.
An intranet is a private computer network that uses internet technologies to share any of an organization's information or operational system. An organizational intranet does not necessarily have to provide access to the internet. Increasingly, intranets are used to deliver tools and applications. Intranets and their use are growing rapidly.
According to the Intranet design annual 2007 from Nielsen Norman Group, the number of pages on participants' intranet averaged 200,000 over the years 2001 to 2003 and has grown to an average of 6 million pages over 20052007.
(http://en.wikipedia.org/wiki/Intranet#Planning_and_creating_an_intranet)
Benefits of Intranet:
Workforce productivity:
Employees can easily find the information and can use the relevant to their roles and responsibility. User can access information or database of the organization from anywhere and anytime within the company workstation.
Time:
With intranets, organization can make more information available to employees anytime because employee can access these information from anywhere and anytime rather than emails.
Communication:
Internets can be used as powerful tools for communication within an organization, vertically and horizontally. Strategies initiative that has a global reach can be out throughout the organization through the use of Internet.
Business operation and Management:
It is also used as a platform for developing and deploying applications to support business operations and decision.
Option A Infosys Ltd
Year 
2009 
2010 
2011 
2012 
2013 
Income 
£400,000 
£300,000 
£350,000 
£400,000 
£300,000 
Option B NCR Ltd
Year 
2009 
2010 
2011 
2012 
2013 
Income 
£450,000 
£400,000 
£425,000 
£450,000 
£400,000 
PAYBACK PERIOD:
The payback period is perhaps the simplest method of looking at one or more investment projects or ideas. It focuses on recovering the cost of investments. It represents the amount of time that it takes for a capital budgeting project to recover its initial cost.
Calculating the payback period can be done in the following way:
The Costs of Project / Investment
PP = x 12
Annual Cash Inflows
The Payback Period concept holds that all other things being equal, the better investment is the one with the shorter payback period.
Benefits:
The payback period is easy to calculate and easy to understand.
Limitation:
It ignores any benefits that occur after payback period. It does not measure total incomes.
It ignores the time value of money.
Option A Infosys Ltd
Year 
2009 
2010 
2011 
2012 
2013 
Income 
£400,000 
£300,000 
£350,000 
£400,000 
£300,000 
Year 2009:
 Cost of the equipment and installation = £ 1million
 Annual income from investment = £ 400,000
 Payback = 1000,000 / 400,000 * 12 = 30 months = 2years, 6 months
Year 2010
 Cost of the equipment and installation = £ 1million
 Annual income from investment = £ 300,000
 Payback = 1000,000 / 300,000 * 12 = 40 months = 3years, 4 months
Year 2011
 Cost of the equipment and installation = £ 1million
 Annual income from investment = £ 350,000
 Payback = 1000,000 / 350,000 * 12 = 34.29 months = 2years, 9 months
Year 2012
 Cost of the equipment and installation = £ 1million
 Annual income from investment = £ 400,000
 Payback = 1000,000 / 400,000 * 12 = 30 months = 2years, 6 months
Year 2013
 Cost of the equipment and installation = £ 1million
 Annual income from investment = £ 300,000
 Payback = 1000,000 / 300,000 * 12 = 40 months = 3years, 4 months
Hence, the payback period for Infosys is 12 years, 29 months.
Option B NCR Ltd
Year 
2009 
2010 
2011 
2012 
2013 
Income 
£450,000 
£400,000 
£425,000 
£450,000 
£400,000 
Year 2009
 Cost of the equipment and installation = £ 1.5million
 Annual income from investment = £ 450,000
 Payback = 1500,000 / 450,000 * 12 = 40 months = 3years, 4 months
Year 2010
 Cost of the equipment and installation = £ 1.5million
 Annual income from investment = £ 400,000
 Payback = 1500,000 / 400,000 * 12 = 45 months = 3years, 8 months
Year 2011
 Cost of the equipment and installation = £ 1.5million
 Annual income from investment = £ 425,000
 Payback = 1500,000 / 425,000 * 12 = 42.35 months = 3years, 5 months
Year 2012
 Cost of the equipment and installation = £ 1.5million
 Annual income from investment = £ 450,000
 Payback = 1500,000 / 450,000 * 12 = 40 months = 3years, 4 months
Year 2013
 Cost of the equipment and installation = £ 1.5million
 Annual income from investment = £ 450,000
 Payback = 1500,000 / 400,000 * 12 = 45 months = 3years, 8 months
Hence, the payback period NCR is 15 years, 29 months.
Therefore, Infosys is better option as the payback period is lesser than NCR
Net Present Value (NPV):
The Net Present Value (NPV) of an investment is the difference between the sum of the discounted cash flows which are expected from the investment, and the amount which is initially invested. It is a traditional method often used for a project used in the discounted cash flow methodology. Each cash inflow/outflow is discount back to its present value (PV). Then they are summed.
Therefore NPV is the sum of all term, where
t  The time of the cash flow
i  The discount rate
Rt  the net cash flow
If, NPV > 0 then the project may be accepted
If, NPV < 0 then the project should be rejected
If, NPV = 0 then either the project should be accepted or rejected.
Option A Infosys Ltd
Year 
2009 
2010 
2011 
2012 
2013 
Income 
£400,000 
£300,000 
£350,000 
£400,000 
£300,000 
Option A 
Infosys 

Outflow 
1,000,000 

Inflow 

Year 
Income 
Cost of Capital @ 5% 

2009 
400,000 
0.9524 
380,952 
2010 
300,000 
0.9070 
272,109 
2011 
350,000 
0.8638 
302,343 
2012 
400,000 
0.8227 
329,081 
2013 
300,000 
0.7835 
235,058 
Total Inflow 
1,519,543 

Total outflow 
1,000,000 

NPV 
519,543 
Option B NCR Ltd
Year 
2009
2010
2011
2012
2013
Income
£450,000
£400,000
£425,000
£450,000
£400,000
Option B 
NCR Ltd 

Outflow 
1,500,000 

Inflow 

Year 
Income 
Cost of Capital @ 5% 

2009 
450,000 
0.9524 
428,571 
2010 
400,000 
0.9070 
362,812 
2011 
425,000 
0.8638 
367,131 
2012 
450,000 
Cite This DissertationTo export a reference to this article please select a referencing stye below:
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Get help with your dissertationFind out moreBuild Time: 0.0054 Seconds
