BUS508 Business Analytics

Written Assignment: Computer Application Project

(worth 40% of the overall assessment)

Instructions:

• This assignment has 2 Questions.
• You will need to download Assignment Question file (this file) and data file to complete your assignment.
• All numerical calculations and graphs/plots should be done using EXCEL or R as much as possible.
• Only typed assessments in a Word document will be marked. Hand-written equations and symbols are accepted if scanned and pasted into the word document. You have to copy and paste Excel/R outputs (e.g., plots, tables etc) into your main assignment, which is a Word document. Any answer in the Excel document, but not in the main document, will NOT be marked.
• The completed assignment (Word document, Excel file, R script) must be submitted electronically via “Submission” point in Assessment 3 Assignment folder.
• Word files that are not accompanied by Excel files will NOT be marked.
• If R outputs are given in the submission, you have to upload the associated R script.
• You are required to keep a hard copy of the submitted assignment to re-submit, in case the original submission is lost for some reason.

Important Notice:

As this is an individual assessment item, students should submit their individual assignment. All assignments submitted will go through a matching process. If found to have plagiarised, all submissions involved would receive a mark of zero for this assessment item.

Description of Marking Criterion

Application and use of statistical software

In completing this assignment, you should ensure that:

1. All graphical or numerical descriptive measures are generated using Excel and R. The use of Excel or R is task dependent.
2. You submit all required file, these being Excel calculation file, any Rscripts and the Word document with written answers to all questions and all outputs pasted appropriately with a heading that includes the associated question number.
3. Answers to Question 1 are generated using the Question 1 Parts A-C Excel sheets in the “Assignment 3 Data file”. You have to develop your own answer template for this question (hence its empty sheet at the moment).
4. Either Excel or R outputs are generated as required in Question 2.

Important Notes:

• For this question, 5 out of 25 marks are allocated to appropriate outputs generated in R.
• All outputs for Question 2 can be produced in Excel. However, to realise the full allocation of marks, a minimum of(any) 4 outputs should be generated in R.

5. When using R, you use the .csv form files in the Assignment 3 folder to generate the outputs.

Hint:

• Save the csv files in your lecture example ‘Data’ folder and use the Lecture example Rscript to build your own Rscript for the assignment.

6. For all presented R outputs, Rscript is uploaded with the other assignment documentation.

 Important Notes:

• R outputs that are not supplemented by submitted Rscript will not be considered for marking.

7. All outputs are generated using the correct data otherwise they will not be marked.
8. You may use the Excel workbooks provided, with weekly learning materials, for hypothesis testing and confidence interval estimate questions, if required and appropriate.
9. All outputs are formatted appropriately for readability although no marks will be allocated for the advanced formatting of outputs.
10. All answers should include any information needed to appropriately facilitate the interpretation of outputs.

Knowledge and application of Analytics concepts

In completing this assignment, you should ensure that:

11. All sub-questions in the main questions have been answered.
12. The graphical or numerical descriptive measures are suitable and used according to the requirements of the question.
13. Where the question says to explain, discuss, interpret, a correct and concise explanation/interpretation is provided.
14. Not exceed the word count requirement given in each question (excluding the word count in excel and R outputs).

Report writing with data analytics

15. The report in Part 2C is a standalone report that includes appropriate components such as introduction, description of data analytics techniques used and reasons for selecting such techniques, discussion of results and conclusion. Any outputs generated in previous sections of the question should be reproduced in the report if required.
16. Remember that Part 2C is a complete Research Report you might submit to your manager.
17. You are not expected to do additional research to complete this assignment. However, if you do so, include the list of references used.

QUESTION 1 (15 marks)

A manufacturer makes two products, widget A and widget B, each requiring time on each of two machines. These requirements, along with the limitations on the available labour-hours are given in the following table. Using Excel Solver, answer the following questions.

Part 1A [5 marks]

Determine how many units of widget A and widget B must be produced each week to maximize the total profit. (fractional units allowed)

1. Units of widget A
2. Units of widget B
3. Maximum Profit

Part 1B [5 marks]

Suppose that a single order is received for 16 units of widget A per week, and if it is decided that this order must be filled, determine the new values below (fractional units allowed).

1. Units of A
2. Units of B
3. Maximum Profit

Part 1C [5 marks]

In addition to the above, the company estimates that at most 25 units of widgets can be handled by its distribution network each week. Determine the new values below (fractional units allowed).

1. Units of A
2. Units of B
3. Maximum Profit

QUESTION 2: Global warming and a possible cause (25 marks)

Preamble

In the last part of the 20th century, scientists developed the theory that the planet was warming and that the primary cause was the increasing amounts of atmospheric carbon dioxide (CO₂), which are the product of burning oil, natural gas, and coal (fossil fuels). Although many climatologists believe in the so-called greenhouse effect, many others do not subscribe to this theory. Further, Earth’s temperature has increased and decreased many times in its long history. We have had higher temperatures and we have had lower temperatures, including various ice ages. In fact, a period called the ‘little ice age’ ended around the middle to the end of the nineteenth century. Then the temperature rose until about 1940, at which point it decreased until 1975. In fact, a Newsweek article published 28 April 1975, discussed the possibility of global cooling, which seemed to be the consensus among scientists at the time. There are two critical questions that need to be answered in order to resolve the issue.

1. Is Earth actually warming?
2. If the planet is warming, is CO₂ the cause?

In terms of data, the generally accepted procedure is to record monthly temperature anomalies. To do so, we calculate the average for each month over many years. We then calculate any deviations between the latest month’s temperature reading and its average. A positive anomaly would represent a month’s temperature that is above the average. A negative anomaly indicates a month where the temperature is less than the average.

Part 2A [12 = 3 + 4+ 5 marks including R outputs] (300 words)

Is Earth actually warming?

GLOBAL_A1.xlsx sheet in Assignment 3 Data file contains the monthly temperature anomalies (°C) from 1880 to 2016. Sheet GLOBAL_A2.xlsxstores the monthly temperature anomalies (°C) and time period from 1880 to 1940, GLOBAL_A3.xlsxstores the data from 1941 to 1975, GLOBAL_A4.xlsxstores the sample data from 1976 to 1997 and GLOBAL_A5.xlsx stores the data from 1998 to 2016. Using these data (or the files in .csv format if you use R) answer the following:

1. Considering the monthly temperature anomalies data over the data period 1880-2016 in GLOBAL_A1.xlsx, use a graphical technique to display whether there are signs of global warming. Discuss the trend in data.

1. Considering the average of the temperature anomalies in GLOBAL_A1.xlsx, test to show that, on average, there is global warming at the 5 percent level of significance. [Hint: Hypothesis testing on whether the population mean temperature anomalies is positive].

1. To investigate the relationship between temperature anomalies and time, depict this relationship in appropriate graph(s), estimate the least squares line and the coefficient of determination for each of the four time period data in GLOBALA2 to GLOBALA5 files (.xlsx or .csv). Report your findings. Has there been global warming in each of the four periods? Discuss.

Part 2B [5 = 4 + 1 marks including R outputs] (200 words)

If the planet is warming, is CO₂ the cause?

Data for CO₂ levels (ppm) in the atmosphere together with the temperature anomalies for March 1958 to May 2016 are stored in fileGLOBALB.xlsx (and GLOBALB.csv).

1. Use a graphical technique to determine whether there is a linear relationship between temperature anomalies and CO₂ levels. On the plot, insert the trend line. Estimate and interpret the intercept and the slope coefficient of the estimated linear trend line equation and comment on the fitness of the model.

1. Using the estimated trend line equation, predict the temperature anomaly if the CO₂ level reaches 400ppm?

Part 2C [8 marks including R outputs] (500 words)

Consolidating the answers in Part 2A and Part 2B, and using any other information generated from the same data using the appropriate data analytics techniques studied in this unit, write a report answering the two critical questions; Is Earth actually warming? and if the planet is warming, is CO₂ the cause?

COMPUTER APPLICATION PROJECT

Part 1A.

The widget company has been determining to marginal function of average or maximum profit, then the calculation formula is depending on the“profit = total revenue – total cost”. Moreover, the firm profit maximizes with the profit when the MR = Mc, then it is the first order as well as it is the second order has been depending on the first order.

Part 1B.

The objective has been maximizing to total profit, the maximize basic assumptions are every cargo has been split with that it is fractions or proportions has been desired.Every cargo has been split along to a compartment such as if the spherical cargo it could be not possible that the volume compartment with the capacity.

Part 1C.

Probability sampling are referring with the sample selection from the provided maximum labor availability in per hours. The non-probability of sampling units are machine time and unit of profit. The fraction that numerator is 1, then represents with the shaded then the part is equal with the parts in the whole.

Part 2A

(I)

Global warming in the long term is trending largely due to human activities which can be emissions increased to carbon dioxide. It is another greenhouse gases are into the atmosphere. This planet has already effects to see global warming, the arctic sea ice has been declining, rising and sea level, as well as wildfires, has been becoming more severe (Al, 2019).

Figure 1: 1880 temperature anomalies

(Source: Acquired from excel)

Global warming has been referred to as the upward trend of temperature across the overall earth hence, it is early in the 20th century.

(ii)

In this question, work is implementing hypothesis testing on the population temperature of anomalies and it is also positive. The positive hypothesis test examines or generates what is expected to have interesting property then the hypothesis is correct. Moreover, it is a negative hypothesis of test that can be examined or generated that it is not to be expected that has interest property in the hypothesis that is also correct.

Figure 2: Hypothesis testing model table

(Source: Acquired from excel)

The hypothesis research of states is expectations in the positive sense. The hypothesis value is null and it is also stated with negative values (Baldwin et al. 2019).

(iii)

There are four different types of effects of global warming, climate change has encompassed global warming, and it can be referred to the range of broader that can be changed that can be happening with the planet, and it is included the sea levels rising, mountain shrinking glaciers, plant times blooming and flowers in shifts.

Figure 3: Time and temperature anomalies relationship plot

(Source: Acquired from excel)

Global warming has increased in temperature average in the earth's atmosphere. It can be caused by a rise in carbon dioxide of levels in the atmosphere.

Part 2B

(I)

The relationship between the temperature and CO₂ and this relationship is dependent on linear relation. It can be suggested that the relationship between the global mean changing temperature and emissions of CO₂ cumulative can be approximated with the linear and it can be during the periods of net negative emissions of CO₂ (Imadaet al. 2019). it can be provided with those emissions of negative that can be applied in the form of a state that can be a system of the equilibrium of the past emissions of CO₂.

Figure 4: Linear relationship model

(Source: Acquired from excel)

(ii)

The current growth rate is CO₂, that level hit 400 ppm and it can be put with to tracking to temperature reach boosts in the perhaps that more than 3 degrees in C. At this level, scientists of climate say that extreme bouts of rising sea levels and weather can be endangered the global supplies of food and caused disruption. It also results from fossil burning those natural sources and fuels as volcanic eruptions. The levels of CO₂ in outdoor air typically range from 350 to 400 ppm and it can be high such as 650 to 900 ppm in metropolitan areas.

Part 2C

In the previous section Part, 2A and also Part 2B are used to any generating information that can be with the several data and it using proper techniques of data analytics. These data analytics techniques are briefly explained in this section (Saklani and Khurana, 2019). These two parts have two most important techniques which are hypothesis techniques and linear regression models. These two techniques are described here and it is given below,

Hypothesis testing - This testing process is such a statistical form that can be inference to use the sample data to draw conclusions. It is also about the parameter of the population or the probability of population distribution. It is first with the assumption tentative that can be made with the distribution or parameter. The alternative hypothesis is initially with to predict the hypothesis relationship along the variables. These types of null hypotheses are predicted with no relationship to the variables that are interested.

Figure 5: Hypothesis values

(Source: Acquired from excel)

In some cases, it can be used for p-value and it is generating the statistical test that can be guided by the decision. Most cases predetermined significant levels for rejecting some null hypothesis values that can be up to 0.05 which is there and it is less than chance at 5% that can see some results if the null hypothesis is true.

Figure 6: Hypothesis values

(Source: Acquired from excel)

The simple hypothesis has specified the exact parameter value. It is composite with the hypothesis and it is specified with the values range. This test is mainly used to strengthen evaluation for a shred of evidence and it is provided and sample with the framework that can be made with the relation determinations in the population.

Linear relationship model - Linear regression is a tool of statistical in Excel that can be used in the model of predictive analysis to check the relationship between two data sets or variables. It can be estimated with the relationship with one or more using variables for this analysis. It is such as, it can be seen with the two different types of variables are independent as well as dependent variables.

Figure 7: Linear model main equation

(Source: Acquired from excel)

The formula is Y = mX + b, in this regression interpretation coefficient slope is m, and the estimated change is that y is a unit of 1 and it is increasing with the X. The intercept interpretation parameter is b, and the estimation value is Y while X = 0.

Figure 8: Linear model values

(Source: Acquired from excel)

The linear data a data that has been represented on the line graph. It means that there was seen a clear relationship between variables as well as a graph that can be shown with a straight line. The non-linear data, on other hand, is represented with the line graph. The linear model is mainly used for data prediction of a variable that can be dependent on the value and it is with other variables.

Reference List

Al‐Ghussain, L., 2019. Global warming: Review on driving forces and mitigation. Environmental Progress & Sustainable Energy, 38(1), pp.13-21.

Baldwin, J.W., Dessy, J.B., Vecchi, G.A. and Oppenheimer, M., 2019. Temporally compound heat wave events and global warming: an emerging hazard. Earth's Future, 7(4), pp.411-427.

Imada, Y., Watanabe, M., Kawase, H., Shiogama, H. and Arai, M., 2019. The July 2018 high temperature event in Japan could not have happened without human-induced global warming. Sola, pp.15A-002.

Saklani, N. and Khurana, A., 2019. Global warming: Effect on living organisms, causes and its solutions. International Journal of Engineering and Management Research.