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ECON940 Statistics For Decision Making

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ECON940 Statistics For Decision Making

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Course Code: ECON940
University: University Of Wollongong

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Country: Australia

Question:
Output based on different Classes
Analysis based on different ClassesOutput based on different types of StudentsAnalysis based on different types of studentsOutput based on marks of class for different types of students Analysis based on marks of class for different types of students
Answer:

Introduction:
This report is aimed to discuss the recent car survey data with the aid of the statistical tools and software like Excel. For better understanding of the preference for buying cars, the report will consider different hypothesis and depict the data with the graphical presentation. Moving forward, the report will portray the impact of the finding and recommendations will be given to gauge to enhance the present situation.
Business Problem:
As per the given context, it can be seen that the Automobile Association attempted to predict the demand of luxury cars utilising the survey performed by Nelson Perera during the year 2012. In order to direct the survey, various factors that can alter the preference of the buyer and change the demand of the cars has been chosen. The business problem in this case lies within the relation between age, education years of the buyer, and income with the car purchasing preference (Zhang and Kim 2013). To be more specific, objective of the business is to determine three different models of consumer preference for purchasing cars depending upon the age of consumer, education years of the buyer and income of the purchaser. It will help the Automobile Association to establish consumer profile for different cars model and help the industry to mark their prospect buyers.
Statistical Problem:
The business problem as per the direction need to be analysed with the help of statistical tool and theories. In order to perform statistical interpretation of the given data, distribution of income, education years of the consumer and age will be utilised. In addition to this, shape of the distribution, location, will be judged with the help of dispersions and central tendency (Dotsch et al. 2017). In order to determine whether there is any association between the willingness to buy Lexus, Mercedes or BMW with the age of the consumer, income of the purchaser and educational year of the buyer. Considering the case study, it can be seen that there is much amount of ambiguity regarding the consumer preference of buying Mercedes and for this purpose Logistics regression has been performed and it has tested, whether older people who have high income and higher level of education prefer Mercedes over other luxury car brands or not (Chatterjee and Hadi 2015). Apart from this, in order to mitigate the question of relation regarding income, average education years with the car purchasing, the report has considered hypothesis testing that will allow the researcher to provide concrete evidence and consumer profiling.
Different age group statistical output analysis:
As it can be seen from the table 1 and figure 1, ages of the buyers have been divided into seven groups ranging from 35 to 70. As the table 1 highlights, most of the people who prefers the given luxury cars are aged between 45 and 49 because people within this age range has highest number of population. When it comes to BMW, then people aging from 40 to 49 prefers the brand most and when it comes to Lexus, then people within the age range from 45 to 49 prefers the brand most. Contrary to this, from the table 1, it can be seen that people who are 50 to 54 years old, prefer the Mercedes brand.

Count of Age (Years)

Column Labels
 
 

 

Row Labels

1

2

3

Grand Total

35-39

6

6

4

16

40-44

52

14

20

86

45-49

52

46

26

124

50-54

14

36

48

98

55-59

6

28

34

68

60-64

 

6

16

22

65-70

 

4

2

6

Grand Total

130

140

150

420

Table 1: Buyer’s age of different luxury cars
Figure 1, depicts the same thing graphically and utilising the figure, it can be seen that most of the people of different ages prefer Mercedes over other given brands. On the other hand, figure 1, also depicts that people who are older than 59 years do not prefer BMW at all thus, the figure does not shows any upward bar diagram.
 
Figure 1: Buyer’s age of different luxury cars
Descriptive statistics of different age groups showcase that out of 130 people who prefer BWM has mean age of 45. On the other hand median age is 45 and the mode value being 46 depicting that most people within the population of BMW courtesan is aged between 45 and 46. Minimum age of the people who prefer BMW over other brand is 36 years and the maximum aged people who prefer BWM over other brand is 57 years. With lower standard deviation of 4.35, it can be entailed from the descriptive statistics that age distribution among the people who prefer BWM is low. Skewness with 0.51 positive value shows that the distribution of the age is positively skewed that define few smaller values of age cannot shift the mean value of ages leftward leading it to fall (Desmond and Weeks 2014).

Age (Years) (1)

 

 

Mean

45.21538

Standard Error

0.381908

Median

45

Mode

46

Standard Deviation

4.354423

Sample Variance

18.961

Kurtosis

0.04742

Skewness

0.508655

Range

21

Minimum

36

Maximum

57

Sum

5878

Count

130

Table 2: Descriptive Statistics for ages with preference for BMW
From the figure 2, it can also be seen that people who are aged between 40 and 47 prefer BMW most, whereas, with rise in the age number of people preferring BMW over other brand has been falling. 

Age (Years) (2)

 

 

Mean

50.45714

Standard Error

0.515472

Median

50

Mode

55

Standard Deviation

6.099147

Sample Variance

37.19959

Kurtosis

0.611214

Skewness

0.360262

Range

32

Minimum

36

Maximum

68

Sum

7064

Count

140

Table 3: Descriptive Statistics for ages with preference for Lexus
Moving forward, if the age distribution of people who prefer Lexus is observed, then it can be seen that people who are 46 to 55 years old prefer the Lexus most. As the table 3 showcase, mean value of the population who prefer Lexus is 50 years and the median is also same depicting symmetric distribution of ages. If mean and median are same, then it also implies that Skewness will also be lower and considering the descriptive statistics table (table 3), it can be seen that Skewness is 0.36 depicting the symmetric distribution of ages. People who are 36 years old and aged less than 69 years, prefer Lexus over other brands. Out of the given data, it can be seen that total 140 people prefer Lexus over any other brand and it showcase that range within the age distribution is 32.
figure 3, depicts that people who are aged between 46 and 55, prefer Lexus most and the range of people who prefer the same car brand lies within 36 years to 70 years with central spike at 46 to 55. This depicts that standard deviation will be lower and as the table 2 depicts it is only 6.09.  

Age (Years)(3)

 

 

Mean

51.98667

Standard Error

0.55037

Median

53

Mode

53

Standard Deviation

6.740628

Sample Variance

45.43606

Kurtosis

-0.0195

Skewness

-0.02894

Range

35

Minimum

35

Maximum

70

Sum

7798

Count

150

Table 4: Descriptive Statistics for ages with preference for Mercedes
Considering the table 4 it can be seen that out of total sample population, 150 people prefer Mercedes over any other brand. People who are aged between 50 and 54, prefer the Mercedes most, whereas minimum aged people who prefer Mercedes over other brand is 35 and the maximum aged population who prefer the same car brand is 70 years old. Table 4 depicts that, average age of the people who prefer Mercedes is 52 and the modal as well as median age group is 53. This showcase than age distribution is almost symmetric, however, negative Skewness of -0.02894 depict that there is leftward central tendency. Standard deviation is 6.74 and it showcase low amount of volatility among the different age groups who prefer Mercedes over other brands (Hannagan and Morduch 2015).
As the figure 4 depicts, people are aged more than 50 and less than 54 prefer Mercedes more, whereas, with rise in the age there has been subsequent fall in the preference.
Analysis of statistical data of different income groups:
Considering the table 5, it can be seen that total 420 people were chosen for the survey and when it comes to analysis of statistical data of different income groups, then it can be seen that lowest income group earns 46068 to 96067 dollars as their annual income. People with lowest income prefer BMW most and no one from the lowest income prefer Lexus. As per the table 5, highest income groups earns 296068 to 346067 dollar annually.

Count of Annual Income ($)

Column Labels

 

 

 

Row Labels

1

2

3

Grand Total

46068-96067

10

 

4

14

96068-146067

62

58

30

150

146068-196067

54

72

62

188

196068-246067

4

8

44

56

246068-296067

 

2

8

10

296068-346067

 

 

2

2

Grand Total

130

140

150

420

Table 5: Income distribution of buyers of different luxury cars
People who earn highest neither prefer BMW nor prefer Lexus, they only want to obtain Mercedes. Considering the second and third lowest income group, it can be seen that 150 and 188 people respectively earns second and third tier annual income. As per table 5, second tier earners prefer BMW most and the third tier income earner prefer Lexus most. If the fourth tier income earner is considered, then it can be seen that most people prefer Mercedes over any other brand.
As per the figure 5, it can be seen that most of the population lies within the second and third tier income group who prefer type 1 and type 2 cars and with rise in income preference of Mercedes eventually expands.

Annual Income ($)(1)

 

 

Mean

139271.3

Standard Error

2907.846

Median

138512

Mode

109568

Standard Deviation

33154.54

Sample Variance

1.1E+09

Kurtosis

-0.22439

Skewness

-0.03855

Range

170652

Minimum

46068

Maximum

216720

Sum

18105274

Count

130

Table 6: Descriptive Statistics for income group preferring BMW
As per the table 6, it can be seen that people who prefer BMW possess a mean income of 139271 dollars and the median being 138512 depicts half of the population of the group earns the mean income. Mode being 109568 dollars depicts people who prefer BMW over other have this much or higher income and that standard deviation being 33154.54 means there is a smaller variation in the income distribution. Negative Skewness of -0.03855 depicts that central tendency provides a left ward inclines (Ho and Yu 2015).
As per the figure 6, it can be seen that people who have income higher than 46068 dollar annually prefer BMW and people who have income less than 216720 dollars prefers the same car brand.

Annual Income ($) (2)

Mean

154186.9

Standard Error

2556.425

Median

154492

Mode

179617

Standard Deviation

30248.02

Sample Variance

9.15E+08

Kurtosis

0.963641

Skewness

0.693685

Range

152065

Minimum

96069

Maximum

248134

Sum

21586160

Count

140

Table 7: Descriptive Statistics for income group preferring Lexus
As per the table 7, it can be seen that people who prefer Lexus possess a mean income of 154186 dollars and the median being 154492 depicts half of the population of the group earns the mean income. Mode being 179617 dollars depicts people who prefer Lexus over other have this much or higher income and that standard deviation being 30248 means there is a smaller variation in the income distribution. Negative Skewness of -0.06937 depicts that central tendency provides a left ward inclines (Cain et al. 2017).
As per the figure 7, it can be seen that people who have income higher than 49941 dollar annually prefer Lexus and people who have income less than 27663592 dollars prefers the same car brand.
 

Annual Income ($) (3)

 

 

Mean

184423.9

Standard Error

3845.333

Median

186070

Mode

161590

Standard Deviation

47095.52

Sample Variance

2.22E+09

Kurtosis

0.987178

Skewness

0.273966

Range

284882

Minimum

49941

Maximum

334823

Sum

27663592

Count

150

Table 8: Descriptive Statistics for income group preferring Mercedes
As per the table 8, it can be seen that people who prefer Mercedes possess a mean income of 184423 dollars and the median being 186070 depicts half of the population of the group earns the mean income. Mode being 161590 dollars depicts people who prefer Mercedes over other have this much or higher income and that standard deviation being 47095.52 means there is a smaller variation in the income distribution. Skewness of 0.2739 depicts that central tendency provides a rightward inclines.
As per the figure 7, it can be seen that people who have income higher than 49941 dollar annually prefer Mercedes and people who have income less than 27663592 dollars prefers the same car brand.
Analysis of different education years:
As far as education of the owners is concerned, it can be seen that out of total sample population, most educated people prefer Lexus and Mercedes by a large number. Lowest educated people prefer Lexus over other brands and in the second and third tier of education level most people prefers all the three brands with slight difference. Second tier education level people prefer BMW most and as it can be seen from table 9, third tier education level people prefer Merced most.

Count of Education (Years)

Column Labels

 

 

 

Row Labels

1

2

3

Grand Total

11-13

12

34

2

48

14-16

66

52

38

156

17-19

52

44

94

190

20-22

 

10

16

26

Grand Total

130

140

150

420

Table 9: Income of buyers of different luxury cars
As figure 9 depicts, people with lowest education level prefer Lexus most, people with second tier of education level prefer BMW and people with third and higher level of education level prefer Mercedes most.

Education (Years) (1)

Mean

15.83077

Standard Error

0.160923

Median

16

Mode

16

Standard Deviation

1.834799

Sample Variance

3.366488

Kurtosis

-0.17288

Skewness

-0.4345

Range

8

Minimum

11

Maximum

19

Sum

2058

Count

130

Table 10: Descriptive Statistics for education years showing preference for BMW
As it can be seen from the table 10, out of 130 people who prefer BMW within the sample population most of them have mean education level of 15. Median and mode being same at 16 depicts there education distribution among the people who love BMW is symmetric that leads to the negative Skewness within the data that highlight central tendency being leftward skewed. Figure 10 depicts that, people who prefer BWM have lowest education level of 11 and highest education level of 19 and the median education level of 16 highlights that half of the people have more education than 16th level.
As it can be seen from the table 11, out of 130 people who prefer BMW within the sample population most of them have mean education level of 15. Median and mode being same at 16 depicts there education distribution among the people who love BMW is symmetric that leads to the negative Skewness within the data that highlight central tendency being leftward skewed. Figure 10 depicts that, people who prefer BWM have lowest education level of 11 and highest education level of 19 and the median education level of 16 highlights that half of the people have more education than 16th level.

Education (Years) (2)

 

 

Mean

15.8

Standard Error

0.204069593

Median

16

Mode

16

Standard Deviation

2.414583986

Sample Variance

5.830215827

Kurtosis

-0.977282698

Skewness

0.169719918

Range

9

Minimum

12

Maximum

21

Sum

2212

Count

140

Table 11: Descriptive Statistics for education years showing preference for Lexus
As it can be seen from the table 11, out of 140 people who prefer Lexus within the sample population most of them have mean education level of 16. Median and mode being same at 16 depicts there education distribution among the people who love Lexus is symmetric that leads to the Skewness of 0.1697 within the data that highlight central tendency being rightward slightly skewed. Figure 11 depicts that, people who prefer Lexus have lowest education level of 12 and highest education level of 21 and the median education level of 16 highlights that half of the people have more education than 16th level.
As it can be seen from the table 12, out of 150 people who prefer Mercedes within the sample population most of them have mean education level of 17. Median and mode being same at 17 depicts there education distribution among the people who love Mercedes is symmetric that leads to the negative Skewness within the data that highlight central tendency being leftward skewed. Figure 12 depicts that, people who prefer Mercedes have lowest education level of 13 and highest education level of 22 and the median education level of 17 highlights that half of the people have more education than 17th level.

Education (Years) (3)

 

 

Mean

17.29333

Standard Error

0.142067

Median

17

Mode

17

Standard Deviation

1.739963

Sample Variance

3.027472

Kurtosis

0.039633

Skewness

0.081676

Range

9

Minimum

13

Maximum

22

Sum

2594

Count

150

Table 12: Descriptive Statistics for education years showing preference for Mercedes
Figure 12 additionally showcase that people who have education level at 17th, prefer Mercedes most and with rise in the education level preference of Mercedes falls gradually. 
Significant difference of the average ages of the buyers:
To test whether there exists any significant difference of average ages of buyers of three different luxury car buyers here ANOVA is performed.
Null hypothesis: No significant difference within the average ages of people who prefer different luxury car.
Alternative hypothesis: There is a statistically significant difference within the average ages of people who prefer different luxury car.
Table 13.1: Summary of different ages of different buyers’ group
As per the statistical theories, if the computed F value is larger than the critical value, then null hypothesis need to be rejected and the alternative will be accepted (Bretz et al. 2016).

ANOVA

Source of Variation

SS

df

MS

F

P-value

F crit

Between Groups

3436.362

2

1718.181

49.80171

4.02787E-20

3.017357

Within Groups

14386.69

417

34.50044

 

 

 

Total

17823.05

419

 

 

 

 

Table 13.2: Hypothesis testing for of different ages of different buyers’ group
Considering the ANOVA, it can be seen than obtained Ft value is 3.017357 and the computed Fc value is 49.8017. Thus, Fc>Ft that determine null hypothesis need to be rejected. Under this situation, it can be entailed that average age of the buyers who prefer different luxury cars are not equal. To be specific, there is at least one group who’s mean age differ from two other age groups (Greenland et al. 2016).
Significant difference in mean household income:
To test whether there exists any significant difference of average income of buyers of three different luxury car buyers here ANOVA is performed.
Null hypothesis: No significant difference within the average income of people who prefer different luxury car.
Alternative hypothesis: There is a statistically significant difference within the average income of people who prefer different luxury car.

SUMMARY

Groups

Count

Sum

Average

Variance

1

130

18105274

139271.3

1.1E+09

2

140

21586160

154186.9

9.15E+08

3

150

27663592

184423.9

2.22E+09

Table 14.1: Summary of different income of different buyers’ group

ANOVA

Source of Variation

SS

df

MS

F

P-value

F crit

Between Groups

1.5E+11

2

7.5E+10

52.1761

5.98E-21

3.017357

Within Groups

5.99E+11

417

1.44E+09

 

 

 

Total

7.49E+11

419

 

 

 

 

Table 14.2: Hypothesis testing of different income of different buyers’ group
Considering the ANOVA, it can be seen than obtained Ft value is 3.017357 and the computed Fc value is 52.1761. Thus, Fc>Ft that determine null hypothesis need to be rejected (Montgomery 2017). Under this situation, it can be entailed that average income of the buyers who prefer different luxury cars are not equal. To be specific, there is at least one group who’s mean income differ from two other age groups.
Significant difference in average years of education: 
To test whether there exists any significant difference of average education years of buyers of three different luxury car buyers here ANOVA is performed.
Null hypothesis: No significant difference within the average education years of people who prefer different luxury car.
Alternative hypothesis: There is a statistically significant difference within the average education years of people who prefer different luxury car.

SUMMARY

Groups

Count

Sum

Average

Variance

1

130

2058

15.83077

3.366488

2

140

2212

15.8

5.830216

3

150

2594

17.29333

3.027472

Table 15.1: Summary of average income of different buyers’ group

ANOVA

Source of Variation

SS

df

MS

F

P-value

F crit

Between Groups

210.8583

2

105.4292

25.92566

2.44085E-11

3.017357

Within Groups

1695.77

417

4.066595

 

 

 

Total

1906.629

419

 

 

 

 

Table 15.2: Hypothesis testing for average income of different buyers’ group
Considering the ANOVA, it can be seen than obtained Ft value is 3.017357 and the computed Fc value is 25.92566. Thus, Fc>Ft that determine null hypothesis need to be rejected. Under this situation, it can be entailed that average income of the buyers who prefer different luxury cars are not equal. To be specific, there is at least one group who’s mean income differ from two other age groups.
Preference of car of older people: 
In order to test that claim that older people prefer Mercedes over other two brands regression analysis will be done where categorical logistic regression has been utilised.

Variable

Categories

Frequencies

%

Car

0

270

64.286

 

1

150

35.714

Table 16.1: Frequency distribution
Table 16.1 depicts 1 as Mercedes and the other as 0. Table 16.2 showcase that there is positive impact on car model selection by the age, education and income. If Chi-squares value is less than significance level of 0.05, then null hypothesis will be rejected.

Model parameters (Variable Car)

Source

Value

Standard error

Wald Chi-Square

Pr > Chi²

Wald Lower bound (95%)

Wald Upper bound (95%)

Intercept

-14.857

1.677

78.523

< 0.0001 -18.143 -11.571 Age (Years) 0.098 0.020 24.641 < 0.0001 0.059 0.136 Annual Income ($) 0.000 0.000 47.145 < 0.0001 0.000 0.000 Education (Years) 0.326 0.064 26.170 < 0.0001 0.201 0.451 Table 16.2: Model parameters   Considering the table 16.2, it can be seen that chi-square probability is less than the significance and thus null hypothesis will be rejected while accepting the alternative. Considering the goodness of fit statistics, it can be seen that chi-square of the log ratio is lower than 0.0001 and it can be depicted that overall significance of the independent variables bring in important information regarding the selection of luxury cars (D'Agostino 2017). Goodness of fit statistics (Variable Car) Statistic Independent Full Observations 420 420 Sum of weights 420.000 420.000 DF 419 416 -2 Log(Likelihood) 547.476 405.453 R²(McFadden) 0.000 0.259 R²(Cox and Snell) 0.000 0.287 R²(Nagelkerke) 0.000 0.394 AIC 549.476 413.453 SBC 553.516 429.614 Iterations 0 6 Table 16.3: Goodness of fit statistics Test of the null hypothesis H0: Y=0.357 (Variable Car) Statistic DF Chi-square Pr > Chi²

-2 Log(Likelihood)

3

142.023

< 0.0001 Score 3 121.512 < 0.0001 Wald 3 86.750 < 0.0001 Table 16.4: Test of null hypothesis   Equation of the model with car as variable: Pred (Car) = 1/ (1 + exp (-(-14.8572933879954+0.097839878084683*Age (Years) +2.41610940803203E-05*Annual Income ($) +0.326147090262717*Education (Years)))) Standardized coefficients (Variable Car) Source Value Standard error Wald Chi-Square Pr > Chi²

Wald Lower bound (95%)

Wald Upper bound (95%)

Age (Years)

0.351

0.071

24.641

< 0.0001 0.213 0.490 Annual Income ($) 0.563 0.082 47.145 < 0.0001 0.402 0.723 Education (Years) 0.383 0.075 26.170 < 0.0001 0.236 0.530 Conclusion:  From the analysis, it can be seen that the Mercedes is one of the most favoured car among all the three alternative of luxury cars. Purchasing of the same is influenced by the age, income and education level. Whereas, the above analysis has also showcased that there is considerable amount if importance of education, age and income on the selection of luxury car. For instance, the report has showcased that, it with rise in age people prefer Mercedes rather than going for BMW, whereas, people who have lower education, goes for Lexus. On the other hand, BMW is preferred by the people who have low income. When it comes to the recommendations, then it would be ideal for the Mercedes to bring in competitive price so that it can be purchased large amount of population and on the other hand, BMW need to bring in more amount of models that are old age friendly and smooth to drive so that older people also can purchase the same. When it comes to Lexus, then it can be seen that they are doing well, however, price need to be revised so that lower income group people can also afford the same. Reference: Bretz, F., Westfall, P. and Hothorn, T., 2016. Multiple comparisons using R. Chapman and Hall/CRC. Cain, M.K., Zhang, Z. and Yuan, K.H., 2017. Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior research methods, 49(5), pp.1716-1735. Chatterjee, S. and Hadi, A.S., 2015. Regression analysis by example. John Wiley & Sons. D'Agostino, R., 2017. Goodness-of-fit-techniques. Routledge. Desmond, K.W. and Weeks, E.R., 2014. Influence of particle size distribution on random close packing of spheres. Physical Review E, 90(2), p.022204. Dotsch, R., Hassin, R.R. and Todorov, A., 2017. Statistical learning shapes face evaluation. Nature Human Behaviour, 1(1), p.0001. Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.B., Poole, C., Goodman, S.N. and Altman, D.G., 2016. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), pp.337-350. Hannagan, A. and Morduch, J., 2015. Income gains and month-to-month income volatility: Household evidence from the US Financial Diaries. Ho, A.D. and Yu, C.C., 2015. Descriptive statistics for modern test score distributions: Skewness, kurtosis, discreteness, and ceiling effects. Educational and Psychological Measurement, 75(3), pp.365-388. Montgomery, D.C., 2017. Design and analysis of experiments. John wiley & sons. Zhang, B. and Kim, J.H., 2013. Luxury fashion consumption in China: Factors affecting attitude and purchase intent. Journal of Retailing and Consumer Services, 20(1), pp.68-79. Free Membership to World's Largest Sample Bank To View this & another 50000+ free samples. Please put your valid email id. E-mail Yes, alert me for offers and important updates Submit  Download Sample Now Earn back the money you have spent on the downloaded sample by uploading a unique assignment/study material/research material you have. After we assess the authenticity of the uploaded content, you will get 100% money back in your wallet within 7 days. UploadUnique Document DocumentUnder Evaluation Get Moneyinto Your Wallet Total 16 pages PAY 10 USD TO DOWNLOAD *The content must not be available online or in our existing Database to qualify as unique. Cite This Work To export a reference to this article please select a referencing stye below: APA MLA Harvard OSCOLA Vancouver My Assignment Help. (2021). Statistics For Decision Making. Retrieved from https://myassignmenthelp.com/free-samples/econ940-statistics-for-decision-making/educational-and-psychological-measurement.html. "Statistics For Decision Making." My Assignment Help, 2021, https://myassignmenthelp.com/free-samples/econ940-statistics-for-decision-making/educational-and-psychological-measurement.html. My Assignment Help (2021) Statistics For Decision Making [Online]. Available from: https://myassignmenthelp.com/free-samples/econ940-statistics-for-decision-making/educational-and-psychological-measurement.html[Accessed 18 December 2021]. My Assignment Help. 'Statistics For Decision Making' (My Assignment Help, 2021) accessed 18 December 2021.

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Introduction
The process of developing a successful business entity requires a multidimensional analysis of several factors that relate to the internal and external environment in commerce. The areas covered in this current unit are essential in transforming the business perspective regarding the key commerce factors such as ethics, technology, culture, entrepreneurship, leadership, culture, and globalization (Nzelibe, 1996; Barza, 2…
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SNM660 Evidence Based Practice
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8

Course Code: SNM660
University: The University Of Sheffield

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Country: United Kingdom

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Critical reflection on the objective, design, methodology and outcome of the research undertaken Assessment-I
Smoking and tobacco addiction is one of the few among the most basic general restorative issues, particularly to developed nations such as the UK. It has been represented that among all risk segments smoking is the fourth driving purpose behind infections and other several ailments like asthma, breathing and problems in the l…
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Australia Maidstone Management Business management with marketing University of New South Wales Masters in Business Administration 

BSBHRM513 Manage Workforce Planning
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20

Course Code: BSBHRM513
University: Tafe NSW

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Country: Australia

Answer:
Task 1
1.0 Data on staff turnover and demographics
That includes the staffing information of JKL industries for the fiscal year of 2014-15, it can be said that the company is having problems related to employee turnover. For the role of Senior Manager in Sydney, the organization needs 4 managers; however, one manager is exiting. It will make one empty position which might hurt the decision making process. On the other hand, In Brisba…
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MKT2031 Issues In Small Business And Entrepreneurship
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5

Course Code: MKT2031
University: University Of Northampton

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Country: United Kingdom

Answer:
Entrepreneurial ventures
Entrepreneurship is the capacity and willingness to develop, manage, and put in order operations of any business venture with an intention to make profits despite the risks that may be involved in such venture. Small and large businesses have a vital role to play in the overall performance of the economy. It is, therefore, necessary to consider the difference between entrepreneurial ventures, individual, and c…
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Turkey Istanbul Management University of Employee Masters in Business Administration 

MN506 System Management
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7

Course Code: MN506
University: Melbourne Institute Of Technology

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Country: Australia

Answer:
Introduction
An operating system (OS) is defined as a system software that is installed in the systems for the management of the hardware along with the other software resources. Every computer system and mobile device requires an operating system for functioning and execution of operations. There is a great use of mobile devices such as tablets and Smartphones that has increased. One of the widely used and implemented operating syste…
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Australia Cheltenham Computer Science Litigation and Dispute Management University of New South Wales Information Technology 

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