Computing Skills Assignment Help
Delivery in day(s): 5
Programme 
Diploma in Business 
Unit Number and Title 
Unit 6 Business Decision Making 
QFC Level 
Level 5 
Unit Code 
D/601/0578 
In this task the scenario is that I am working as a research consultant and I have been approached by the client where the client is wishing to launch a new brand of coffee in London so the research regarding the launching of new coffee brand is to be conducted in this report. The main aim of this research is to conduct an analysis on identification of market situation so that product can be launched without any issue so the requirements of this research are as follows
Before launching the new coffee brand the data is needed to be collected to identify the existing competitors in the marketing planning as well as to know the preferences of the customers which will provide an idea regarding the interest of the people in the new product. Types of data can be as follows.
Primary data: When data for a research is collected by the researcher for the first time it is known as primary data.
In our research where we need to collected the data regarding the interest of customers, preferences of customers data can be collected through conducting interviews with the customer so that their views can be recorded and these interviews can be telephonic too as they can call the customers can ask for the data. Another method for data collection for primary data is distribution of questionnaires which can be distributed to the customers (Ahmed, et. al, 2012).
Secondary data: When earlier collected data is used by the researcher for a particular research it is known as secondary data.
In our research where the data is to be collected for knowing the market situations before launching of new coffee brand the secondary data can be collected through the annual reports of the competitor company which is available on the internet this will help us in assessment of the turnover of other companies also the data can be collected from magazines, articles etc which contains information regarding trending coffee brands in London (Zottnick, 2015).
A plan for collected of data has been made which is as follows.
Plan for collection of data
Serial No. 
Activity 
Description 
1. 
Collection of primary data 
Primary data will be collected through questionnaires, face to face interviews with the customers regarding their taste of coffee.

2. 
Collection of secondary data 
Secondary data will be collected through articles, magazines and journals which contains data regarding coffee brands in London.

3. 
Tools for collection of data 
Tools for collection of data will be questionnaires and manual searching.

4. 
Time involved in collection of data 
Data will be collected in approx 15 days.

5. 
Time period of this research 
Research needs to be completed within one month

6. 
Sampling frame 
In this research random sampling method and stratified sampling method are to be used.

Survey methodology: Survey methodology is used when the data is to be collected through survey where questionnaires are given to the people and responses of those customers are recorded on the basis of questionnaires and research is concluded (de Leeuw, 2013).
Sampling frame: Sampling method is used when the data is to be collected through locusassignments.it is when a target group is selected as a sample and then the responses of that target group are recorded for the research. Sampling methods are of four types which are as follows.
Method used in this research: Method used in this research is random sampling method because according to me this method is the most suitable method that can be used for the research and this method provides a fair chance to each unit of being selected (Hassine & Amyot,. 2016).
Questionnaires are a way of collected of primary data in this research project where the company wants to launch a new coffee brand so in this case questionnaire are prepared with a view to identify the taste, preferences, demands of the customers in regard to coffee and the questionnaire is prepared in such a way that it should cover all the aspects of the research and the questionnaire for this research is as follows.
Q.1 What is your gender?
a) Male
b) Female
Q.2 What is your age?
a) 20 to 25 years
b) 25 to 30 years
c) Above 30 years
Q.3 What is your profession?
a) Student
b) Working
c) Anything else
Q.4 Do you take coffee?
a) Yes
b) No
Q.5 How many times you take coffee while working/studying?
a) 2 cups
b) 02 cups – 04 cups
c) More than 04 cups
Q.6 Which is your favourite coffee brand?
a) Nescafe
b) Baristas
c) Starbucks
d) Any other please specify.................
Q.7 What taste you like the most in your favourite coffee?
a) Caramel
b) Cappuccino
c) Any other please specify.................
Q.8 What type of coffee do you like?
a) Strong coffee
b) Light coffee
c) Any other please specify.................
Q.9 What is your preference in coffee type?
a) Ground coffee
b) Instant coffee
c) Any other please specify.................
Q.10 Which coffee you prefer the most?
a) With sugar
b) Without sugar
Q.11 Please mention your average expected prices for a cup of coffee................
This is the questionnaire which will be distributed to the customers to know their interest in regard to coffee. The responses to this questionnaire will help in launching of new coffee brand with proper planning and control.
Mean
Mean is generally known as average and in simple terms mean is the value which is derived from total sum of the data dividing with the number of the values which are present in a data set. Mean is the most used measure of central tendency (Sinova, et. al, 2014). Calculation of mean is as follows.
Amount (£) 
Mid value(x) 
No of orders (f) 
fx 
0.510 
5.25 
7 
36.75 
1020 
15 
9 
135 
2030 
25 
12 
300 
3040 
35 
14 
490 
4050 
45 
16 
720 
5060 
55 
17 
935 
6070 
65 
16 
1040 
7080 
75 
15 
1125 
8090 
85 
8 
680 
90100 
95 
6 
570 
Total 
120 
6031.75 
Mean = ∑fx/∑f
Mean = 6031/120
Mean= 50.26
Analysis: In the above table mean is 50.26 which represents that Stephanie can take average 50 orders per day
Median: Median is the middle number of all the values which are available in a data set. It is also known as the value which divides the data into two parts the first one is upper half and the second one is lower half (Ricardi, 2011). Calculation of median is as follows.
Amount (£) 
No of orders (f) 
Cumulative frequency 
0.510 
7 
7 
1020 
9 
16 
2030 
12 
28 
3040 
14 
42 
4050 
16 
58 
5060 
17 
75 
6070 
16 
91 
7080 
15 
106 
8090 
8 
114 
90100 
6 
120 
In this table N = 120
To determine the model class for median
N/2 = 120/2 = 60
The class having cf more than 60 is 75
So the model class for median is 5060
l= 50, c.f=58, f=17, h=10
Median = 50+(6058)/17*10
Median= 51.18
Analysis: Median represents the mid value of the numbers so in the above table out of total number of orders 51 is the middle number of order.
Mode
Mode represents the value which occurs the highest time in a data set. In a data set mode value can be considered for a sample as this value represents the highest occurrence of any value so that value can be considered as a sample (Manikandan, 2011).
Amount (£) 
No of orders (f) 
Cumulative frequency 
0.510 
7 
7 
1020 
9 
16 
2030 
12 
28 
3040 
14 
42 
4050 
16 
58 
5060 
17 
75 
6070 
16 
91 
7080 
15 
106 
8090 
8 
114 
90100 
6 
120 
Highest frequency in the above table is 17 so the model class for calculation of mode is 5060
fm= 17, fp=16, fs=16, l=50
Mode = l+(fmfp)/2fmfpfs*10
Mode= 50+ (1716)/ 341616*10
Mode= 50+ ½*10
Mode= 55
Analysis: In the above table mode is 55 so it can be said that the numbers of orders which Stephanie gets most often is 55. So he can use the number of order as a sample that he mostly gets 55 orders.
Range is basically the difference of highest and smallest values of data so for this firstly the data is arranged in a sequence from lowest to highest and then the highest value is subtracted from the lowest value to calculate the range.
Amount (£) 
Range (Upper valuelower value) 
0.510 
9.5 
1020 
10 
2030 
10 
3040 
10 
4050 
10 
5060 
10 
6070 
10 
7080 
10 
8090 
10 
90100 
10 
Range 
99.5 
Standard deviation is used as a measure to identify the amount of variation or dispersion in the values. Low standard deviation denotes that the data points are close to the mean and high standard deviation denotes that the data values are spread widely (Ko?acz & Grzegorzewski, 2016).
Amount (£) 
No of orders (f) 
Mid value(x) 
xbar 
xxbar 
(xxbar)2 
f(xxbar)2 
0.510 
7 
5.25 
50.26 
45.01 
2025.9 
14181.3 
1020 
9 
15 
50.26 
35.26 
1243.268 
11189.41 
2030 
12 
25 
50.26 
25.26 
638.0676 
7656.811 
3040 
14 
35 
50.26 
15.26 
232.8676 
3260.146 
4050 
16 
45 
50.26 
5.26 
27.6676 
442.6816 
5060 
17 
55 
50.26 
4.74 
22.4676 
381.9492 
6070 
16 
65 
50.26 
14.74 
217.2676 
3476.282 
7080 
15 
75 
50.26 
24.74 
612.0676 
9181.014 
8090 
8 
85 
50.26 
34.74 
1206.868 
9654.941 
90100 
6 
95 
50.26 
44.74 
2001.668 
12010.01 
∑f=120 
71434.54 
Standard deviation = √71434.54/120
Standard deviation= √595.29
Standard deviation= 24.40
Lower quartile is denoted as Q^{1} and it represents the mid value between the lowest number and median in a data set (Mahajan, A. 2014).
Amount (£) 
No of orders (f) 
Cumulative frequency 
0.510 
7 
7 
1020 
9 
16 
2030 
12 
28 
3040 
14 
42 
4050 
16 
58 
5060 
17 
75 
6070 
16 
91 
7080 
15 
106 
8090 
8 
114 
90100 
6 
120 
Q^{1= }(n+1/4)*^{th}term
Q^{1= }120+1/4
Q^{1= }30.25
So the model class for Q^{1 }is 3040
In this the formula will be same as which is used in median but in Q^{1 }we will divide the ∑f/4 and will multiply it with 1.
Lower quartile= 31.42
Upper quartile is denoted as Q^{3} and it represents the mid value between median and the highest number in a data set (Ko?acz & Grzegorzewski, 2016).
Amount (£) 
No of orders (f) 
Cumulative frequency 
0.510 
7 
7 
1020 
9 
16 
2030 
12 
28 
3040 
14 
42 
4050 
16 
58 
5060 
17 
75 
6070 
16 
91 
7080 
15 
106 
8090 
8 
114 
90100 
6 
120 
Q^{3= }3(n+1/4)*^{th}term
Q^{3= }(120+1/4)
Q^{3= }90.75
So the upper quartile is 16
The formula for this will be same as median only but in Q^{3 }we will divide the ∑f/4 and will multiply it with 3.
Q^{3}=60+(9075)/16*10
Q^{3}= 60 + 15/16*10
Q^{3}=60+9.375
Q^{3}=69.375
Inter quartile range is the difference of upper quartile and lower quartile.
IQR= Q^{3} Q^{1}
IQR= 69.37531.42
IQR= 37.95
Correlation coefficient represents the relationship between two numbers as well as it shows the strength and direction of the relationship between two variables.
Sales (x) 
Temperature (y) 
(xy) 
x^{2} 
y^{2} 
20 
320 
6400 
400 
102400 
24 
411 
9864 
576 
168921 
11 
192 
2112 
121 
36864 
17 
259 
4403 
289 
67081 
9 
170 
1530 
81 
28900 
15 
243 
3645 
225 
59049 
25 
430 
10750 
625 
184900 
121 
2025 
38704 
2317 
648115 
r = 7*38704121*2025/√ (7*23171212) (7*64811520252)
r =270928245025/√ (1621914641) (45368054100625)
r = 25903/√1578*436180
r= 25903/√688292040
r= 25903/26235.32
r= 0.98
Correlation coefficient is 0.98
Use of quantiles and correlation coefficient: These measure play a major role in decision making of the business as these measures considers all the conservation in a data set so that further analysis can be conducted properly. Quartile helps in determining the values after a regular interval in a data set as between the lowest value and the median and between median and the highest value. Standard deviation provides useful information which are useful in making comparisons which reflects to effective decision making (Ko?acz & Grzegorzewski, 2016).
Get assignment help from full time dedicated experts of Locus assignments.
Call us: +44 – 7497 786 317Scenario for this task is that I am working as a project manager at QWM Investments Limited where the company is tend to start a new project for which project duration has to be calculated also critical path is to be drawn. Activities which are to be conducted for the new project are as follows.
Description of Activity 
Activity 
PrecedingActivity 
Period 
Preparation 
[A] 
 
6days 
Business Planning 
[B] 
[A] 
4days 
Recruitment and selection 
[C] 
[A] 
38days 
Installation of peripherals 
[D] 
[B] 
17days 
Initial training 
[E] 
[D] 
6days 
Design 
[F] 
[E] 
11days 
Conversion 
[G] 
[F] 
11days 
Development of norms 
[H] 
[C] 
4days 
Assessment 
[I] 
[B] 
12days 
Continuous testing 
[J] 
[D] 
11days 
Policy documentation 
[K] 
[G,H,I,J] 
22days 
Appraisal 
[L] 
[K] 
22days 
Project duration
The project duration in which the project will get completed will be
=6 days+4 days +38 days +17 days +6 days +11 days +11 days +4 days +12 days +11 days +22 days +22 days
=164 days
Critical path
Critical path helps in understanding the flow of activities which are to be undertaken for completion of a project and critical path is the longest path which is followed to complete the project (Hendriks, et. al, 2016). Critical path for this project is
= ABDEFGKL
=6 days +4 days +17 days +6 days +11 days +11 days +22 days +22 days
=99 days
Scenario for this task is that board members of Local Construction Company is planning to invest in a new project ad for this they have two options so they want to choose one project which is best option for the company so the selection of the project will be done on the basis of calculation of NPV and IRR of both the projects.
Year 
Project Super 
Project Sonic 
0 
400000 
400000 
1 
55000 
318000 
2 
100000 
20000 
3 
110000 
20000 
4 
95000 
6000 
5 
40000 
50000 
Calculation of NPV and IRR where the rate is 10%
Project Super 
Project Sonic 

NPV 
(£86,352.38) 
(£40,190.66) 
IRR 
0% 
2% 
*Note: NPV and IRR of both the projects are calculated using the spreadsheet.
Report
To,
Board of Directors,
Local Construction Company,
As the company wants to start a new project first one is project super and second one is project sonic and on the basis of NPV both the projects are not profitable for the company as NPV of both the projects is negative but if company has to choose one it must go with project sonic because it is having higher NPV comparatively and if company chooses the project on the basis of IRR then it should go for project sonic as it is having higher IRR (Bas, 2013).
From this report it may be concluded that collection of primary data plays a very important role in a research as it enable to know the actual data also the importance of decision making has been explained in this report that decision making plays a major role in every business. In this report there are various methods explained which can be base for project selection. This report also focuses on relationship of sales, cost and profit along with the importance of forecasting has been explained in this report. This report is a brief of business decision making which covers each and every aspect in regard to decision making.
Ahmed, R.R., Kazim, S.S. & Arif, A.A. 2012, "NEW PRODUCT DEVELOPMENT: STRATEGY & IMPLEMENTATION MECHANISM BASED ON PRIMARY & SECONDARY DATA RESEARCH", Interdisciplinary Journal of Contemporary Research In Business, vol. 4, no. 6, pp. 1034.
Bas, E. 2013, "A robust approach to the decision rules of NPV and IRR for simple projects", Applied Mathematics and Computation, vol. 219, no. 11, pp. 59015908.
de Leeuw, E.D. 2013, "Thirty Years of Survey Methodology / Thirty Years of BMS", Bulletin de Méthodologie Sociologique, vol. 120, no. 1, pp. 4759.\
Hassine, J. & Amyot, D. 2016, "A questionnairebased survey methodology for systematically validating goaloriented models", Requirements Engineering, vol. 21, no. 2, pp. 285308.
Hendriks, M., Verriet, J., Basten, T., Theelen, B., Brassé, M. & Somers, L. 2016, "Analyzing execution traces: criticalpath analysis and distance analysis", International Journal on Software Tools for Technology Transfer, .
Ko?acz, A. & Grzegorzewski, P. 2016, "Measures of dispersion for multidimensional data", European Journal of Operational Research, vol. 251, no. 3, pp. 930937.
Lau, H., Nakandala, D., Samaranayake, P. & Shum, P.K. 2016, "BPM for supporting customer relationship and profit decision", Business Process Management Journal, vol. 22, no. 1, pp. 231255.
Mahajan, A. 2014, "Why standard deviation as a measure of dispersion needs a mention in a dataset?", Neurology India, vol. 62, no. 5, pp. 584584.
Manikandan, S. 2011, "Measures of central tendency: Median and mode", Journal of pharmacology & pharmacotherapeutics, vol. 2, no. 3, pp. 214215.
Ricardi, F.Q. 2011, "Measures of central tendency and dispersion", Medwave, vol. 11, no. 3, pp. e4934.
Sinova, B., Casals, M.R. & Gil, M.Á. 2014, "Central tendency for symmetric random fuzzy numbers", Information Sciences, vol. 278, pp. 599613.
Zottnick, K.L. 2015, "Secondary data: a primary concern", Vanderbilt Journal of Entertainment and Technology Law, vol. 18, no. 1, pp. 193