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TSTA602 - Quantitative Methods For Accounting And Finance Answer

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Introduction: TSTA602– Quantitative Methods For Accounting And Finance

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The data set analysis segmentation for Nobel laureates, going to be segmented into three parts. Its part has been going to justify the data linking proportion aspects with the available Nobel laureates ' data. This report is going to involve the graphical representation of variables and also consider the statistical implication for justifying the link between Nobel laureates' dates.

Part 1:

Question 1:

The percentage of Nobel laureates in female categories is 98% among all the categories and has 943 prizes. These prizes have been categorized based on 962 total Nobel laureates.

Female

Percentages

943

98%

Male

Percentages

961

100%

Pie chart of male and female

Figure 1: Pie chart of male and female

The pie chart explains the percentages of the male and female Nobel laureates. In this aspect, out of 962 prize allocations, 99.90% are being won by the male and 98% are won by the female (Woods et al. 2019). Hence, the prize distribution is being mostly acquired within near about 50% of the total male and female participants.

Question 2:

There are 962 prizes for 6 categories, prize allocation for each category has been described in the below section.

Chemistry

186

Economics

86

Literature

117

Medicine

222

Peace

135

Physics

216

Grand Total

962

Table 1: Price Category

Categorical price distribution

Figure 2: Categorical price distribution

The overall discussion regarding prizing for each category has been mentioned as physic has 216, peace has 135, medicine has 222, Literature has 117, Economics has 86, and lastly, Chemistry has 186 (Mueller and Hancock, 2019). Among them, physics is allocated by the higher number of the Nobel prize acquisition and economics has the lowest prize acquisition acquired by the people.

Count of prize

Column Labels

Row Labels

Female

Male

NA

Grand Total

Chemistry

7

179

186

Economics

2

84

86

Literature

16

101

117

Medicine

12

210

222

Peace

17

90

28

135

Physics

4

212

216

Grand Total

58

876

28

962

Table 2: Splitting

Split among male and female

Figure 3: Split among male and female

Part 2:

Question 1:

Prize allocation

Figure 4: Prize allocation

The price allocation can be used by following the birth country current rather than the birth country. As this has helped to categorize the prize allocation data effectively and it has been also going to show the recent prize allocation.

Australia

10

Austria

15

Belgium

9

Canada

20

China

11

Denmark

11

France

54

Germany

65

Italy

17

Japan

27

NA

28

Netherlands

18

Norway

12

Russia

17

Scotland

9

South Africa

9

Sweden

29

Switzerland

19

United Kingdom

91

United States of America

281

Grand Total

752

Table 3: Price allocation

Row Labels

Count of prize

Australia

10

Chemistry

1

Medicine

7

Physics

2

Austria

15

Chemistry

3

Economics

1

Literature

2

Medicine

5

Peace

1

Physics

3

Belgium

9

Chemistry

1

Literature

1

Medicine

3

Peace

3

Physics

1

Canada

20

Chemistry

4

Economics

3

Literature

2

Medicine

4

Peace

1

Physics

6

China

11

Chemistry

1

Literature

2

Medicine

2

Peace

1

Physics

5

Denmark

11

Chemistry

1

Literature

4

Medicine

3

Peace

1

Physics

2

France

54

Chemistry

10

Economics

4

Literature

11

Medicine

12

Peace

9

Physics

8

Germany

65

Chemistry

21

Economics

1

Literature

4

Medicine

16

Peace

5

Physics

18

Italy

17

Chemistry

1

Economics

1

Literature

5

Medicine

5

Physics

5

Japan

27

Chemistry

7

Literature

3

Medicine

5

Peace

1

Physics

11

NA

28

Peace

28

Netherlands

18

Chemistry

4

Economics

2

Medicine

2

Peace

1

Physics

9

Norway

12

Chemistry

2

Economics

3

Literature

2

Medicine

2

Peace

2

Physics

1

Russia

17

Chemistry

3

Economics

2

Literature

4

Medicine

1

Peace

1

Physics

6

Scotland

9

Chemistry

2

Economics

1

Medicine

3

Peace

2

Physics

1

South Africa

9

Chemistry

1

Literature

2

Medicine

3

Peace

3

Sweden

29

Chemistry

4

Economics

2

Literature

7

Medicine

7

Peace

5

Physics

4

Switzerland

19

Chemistry

3

Literature

1

Medicine

6

Peace

3

Physics

6

United Kingdom

91

Chemistry

25

Economics

7

Literature

6

Medicine

25

Peace

5

Physics

23

United States of America

281

Chemistry

55

Economics

49

Literature

10

Medicine

78

Peace

19

Physics

70

Table 4: Cross classification

Relationship between country and category

Figure 5: Relationship between country and category

In the peace category Germany and UK's same prize allocation valuation but, the UK has a higher valuation in winning more prizes than Germany. In the literature and Peace categories, France has a higher prize allocation than Germany. In every 6 categories, Germany and Japan are lower than the US, as their winning capability is better in aspects of any other country.

Row Labels

Count of prize

France

54

Chemistry

10

Economics

4

Literature

11

Medicine

12

Peace

9

Physics

8

Germany

65

Chemistry

21

Economics

1

Literature

4

Medicine

16

Peace

5

Physics

18

NA

28

Peace

28

Sweden

29

Chemistry

4

Economics

2

Literature

7

Medicine

7

Peace

5

Physics

4

United Kingdom

91

Chemistry

25

Economics

7

Literature

6

Medicine

25

Peace

5

Physics

23

United States of America

281

Chemistry

55

Economics

49

Literature

10

Medicine

78

Peace

19

Physics

70

Grand Total

548

Table 5: Categorical classification

Question 2:

As per the details of the prize categorized situation, it has been addressed that the youngest person mentioned Malala Yousafzai having born the year of 1997. The Nobel was acquired the “The Nobel peace prize, 2014”. On the other hand, Christian Matthias Theodor Mommsen is the oldest prize winner, of The Nobel Prize in Literature published in 1902. The average age of an award winner is mentioned as more than 30 to 40 years.

Year

Category

Name

Prize

1997

Youngest

Malala Yousafzai

The Nobel Peace Prize 2014

1817

Oldest

Christian Matthias Theodor Mommsen

The Nobel Prize in Literature 1902

Table 6: Classification

Part 3:

Question 1:

The winning age of each category are being mentioned as 40 years. However, the descriptive statistics discussion regarding winning age in different categories is being explained. Such as,

Summary statistics

Mean

8.24138

Standard Error

0.28653

Median

7.5

Mode

6

Standard Deviation

3.08607

Sample Variance

9.52384

Kurtosis

-0.97029

Skewness

0.12863

Range

14

Minimum

1

Maximum

15

Sum

956

Count

116

Confidence Level(95.0%)

0.56757

Table 7: Descriptive statistics

The mean determination of winning prizes in different categories has been mentioned as 8.24 as the mean, with a standard error of 0.28. On the other hand, it has a median of 7.5 and mode of mode of 6. However, the standard deviation is mentioned as 3.08 and has a sample variance of 9.52. This proportion has been mentioned that the winning prize are being significant at the level of 95% with a confidence level of 0.56 (Athey et al. 2021). On the other hand, it has been noticed that there is a range of 14 at having 15 maximum values with one minimum value proportion of the entire dataset.

Box and Whisker plot

Figure 6: Box and Whisker plot

It has been referred to that there are slight differences acquired in the median valuation and among mean valuation. The median of this considerable data set is 135, which has been followed by 133 as the mean percentile.

Question 2:

The average age for physics is lower than the average age for chemistry, which describes that there will be going differences in the count of Nobel prizes between chemistry and physics.

Summary statistics

Mean

155.2

Standard Error

27.21653909

Median

135

Mode

0

Standard Deviation

60.85803152

Sample Variance

3703.7

Kurtosis

-2.641503562

Skewness

0.235644302

Range

136

Minimum

86

Maximum

222

Sum

776

Count

5

Confidence Level(95.0%)

75.56522673

t-Test: Two-Sample Assuming Equal Variances

186

186

Mean

155.2

155.2

Variance

3703.7

3703.7

Observations

5

5

Pooled Variance

3703.7

Hypothesized Mean Difference

0.05

df

8

t Stat

-0.0013

P(T<=t) one-tail

0.499498

t Critical one-tail

1.859548

P(T<=t) two-tail

0.998995

t Critical two-tail

2.306004

Table 8: Hypothesis equal variances

The description regarding the sample of the equal variances data set describes that it should have the same mean proportion of 155.02, along with having the pooled variances of 3703.70. It has been noted that there are mean differences of 0.05, and have t stat -0.0013. The sample assumption has been referred to that there is one tail t critical justification for this equal variances are 1.85 and have t critical two-tailed equal variances of 2.30 (Arridge et al. 2019). The result has been confirming the equal variances in hypothetical situations for age and category of winning Nobel prizes.

Question 3:

Trend evaluation

Figure 7: Trend evaluation

The trend among categories and years is mentioned as, people having the birth year of 1930 won the Nobel Prize in 2013, for chemistry. On the other hand, in aspects of physics won the Nobel in 2000 having the same year of birth that is 1930 (Xu et al. 2019). Additionally, chemistry and literature have the same year f winning the Nobel award which is 2005.

General conclusion

This database analysis proportion has covered the categorical significance among the Nobel prize-winning aspect considering the of the winner, category country, and other related variables. This entire analysis aspect has created the link among those multiple variables related to the Nobel Laureate.

References:

  • Arridge, S., Maass, P., Öktem, O. and Schönlieb, C.B., 2019. Solving inverse problems using data-driven models. Acta Numerica, 28, pp.1-174.
  • Athey, S., Bayati, M., Doudchenko, N., Imbens, G. and Khosravi, K., 2021. Matrix completion methods for causal panel data models. Journal of the American Statistical Association, 116(536), pp.1716-1730.
  • Mueller, R.O. and Hancock, G.R., 2019. Structural equation modeling. Routledge/Taylor & Francis Group.
  • Woods, S.A., Wille, B., Wu, C.H., Lievens, F. and De Fruyt, F., 2019. The influence of work on personality trait development: The demands-affordances TrAnsactional (DATA) model, an integrative review, and research agenda. Journal of Vocational Behavior, 110, pp.258-271.
  • Xu, L., Skoularidou, M., Cuesta-Infante, A. and Veeramachaneni, K., 2019. Modeling tabular data using conditional gan. Advances in Neural Information Processing Systems, 32.
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