Report

Report

The file P03_63.xlsx contains financial data on 85 U.S. companies in the Computer and Electronic Product Manufacturing sector (NAICS code 334) with 2009 earnings before taxes of at least $10,000. Each of these companies listed R&D (research and development) expenses on its income statement.

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I need to do a report for 1000words using data provided in excel

1 [Date]

 

 

 

 

 

 

2 [Date]

Table of Contents

1.0 Introduction ………………………………………………………………………………………………….3

2.0 Descriptive analysis ………………………………………………………………………………………3

2.1 Gender……………………………………………………………………………………………………..4

2.2 Age ………………………………………………………………………………………………………….5

2.3 Children ……………………………………………………………………………………………………6

2.4 State ………………………………………………………………………………………………………..7

2.5 Salary ………………………………………………………………………………………………………8

2.6 Respondents Opinion ……………………………………………………………………………….12

3.0 Regression analytics ……………………………………………………………………………………13

3.1 Correlation between Gender and environmental policy opinion……………………….14

3.2 Correlation between age differences and environmental policy opinions ………….14

3.3 Correlation between number of children and environmental policy opinion ……….15

3.4 Correlation between salary and environmental policy opinion …………………………16

4.0 Managerial interpretation and implications ………………………………………………………17

5.0 Conclusion …………………………………………………………………………………………………18

References ……………………………………………………………………………………………………..20

 

 

 

 

 

3 [Date]

1.0 Introduction

This report analyses the opinion of respondents from 10 states in the USA in respect to

environmental policy. The data was collected from 399 respondents, and the analyses is

carried out using Microsoft Excel tools. The analysis is based on a number of research in

the past. First, study by Sundstrom and McCright (2014) based on multivariate order

logistic regression indicates that women are more likely to be concerned with

environmental issues than men in Sweden. Torgler et al. (2008) in-depth study on the

‘Differences in preferences towards the environment’ found out that age and

environmental preferences have negative correlation. Moreover, Wynes and Nicholas

(2017) argued that having fewer children can help fight environmental changes. While

Mair et al. (2019) argue that high salary represents a good but not sufficient step towards

dealing with environmental issues. Thus, based on these, this study sought to find out the

following:

a) Are women more likely to support environmental policy in the USA than men?

b) Is there a negative correlation between Age differences and environmental policy

opinion in the USA?

c) Is there a correlation between number of Children and environmental policy

opinion in the USA?

d) Is there a correlation between Salary differences and environment policy opinion

in the USA?

2.0 Descriptive analysis

According to Trochim and Donnelly (2001), descriptive statistics entails explanation of

data in terms what it is and what it shows. It is about describing or summarizing large

 

 

 

4 [Date]

volume of data into meaningful form for decision making purposes (Van Der Aalst, 2016).

However, this statistics is not useful in making conclusions past the data analysed

(Statistics.laerd.com, 2018). But, it is important to perform a descriptive analysis so as to

understand the data gathered in this research.

2.1 Gender

Analysis of Gender is carried out in this report based on the available data consisting of

399 respondents. As show in Table 1 below, Excel COUNTIF function was used to

summarize this data in terms of Gender, and then the data was converted into percentage

using Excel division function. The findings shows that out of the 399 respondents, 59%

of them are Female (assuming 2 represented Female) and the rest (41%) are Male

(assuming that 1 represented Male) as shown in Figure 1 below;

 

Figure 1: Illustration of respondents’ Gender by Number and Percentage (Source;

Author).

165

234

41%

59%

0%

10%

20%

30%

40%

50%

60%

70%

0

50

100

150

200

250

1=Male 2=Female

Respondents by Gender

Count Percentage

 

 

 

5 [Date]

Table 1: Computation of number and percentage of respondents’ Gender (Source;

Author).

 

2.2 Age

The Age is summarized in terms of Young, Middle-aged, and Elderly. As shown in Figure

2 below, 55% of the respondents were Middle-aged, 24% of the respondents were Elderly

and rest (22%) of them were Young.

 

Figure 2: Illustration of the number and percentage of respondents by Age (Source;

Author).

87

218

94 22%

55%

24%

0%

10%

20%

30%

40%

50%

60%

0

50

100

150

200

250

Young Middle-aged Elderly

Respondents by Age

Count Percentage

 

 

 

6 [Date]

Table 2: Computation of the number and percentage of respondents by Age (Source;

Author).

 

2.3 Children

Analysis using Excel COUNTIF function shows that the majority (42%) of the respondents

had 2 children. This is followed by 31% who said they had no child. 17% of them said

they had 1 child and only 10% of the respondents had 3 children as shown in Figure 3

below;

 

Figure 3: Respondents by number of children (Source; Author).

124

67

167

41

31%

17%

42%

10%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

0 20 40 60 80

100 120 140 160 180

0 1 2 3

Respondents by children Count Percentage

 

 

 

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Table 3: Computation of respondents in terms of number of children (Source; Author).

 

2.4 State

Analysing of the State of the respondents using the COUNTIF function seems to indicate

that the respondents were relative balanced across the 10 States of the USA as shown

in Figure 4 below. The top 4 State by number of respondents were Texas (12%), New

York and Michigan with 11.5% respectively, and then Florida at number 4 with 10.3% of

the respondents. The State with least number of respondents is Minnesota with only 7.8%

of them.

 

Figure 4: Illustration of Respondents by State (Source; Author).

48

33 38 41

46 46

33

44 39

31

12.0%

8.3% 9.5% 10.3%

11.5% 11.5%

8.3%

11.0% 9.8%

7.8%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

0

10

20

30

40

50

60

Te xa

s

Ca lifo

rni a

Illi no

is

Flo rid

a

Ne w

Yo rk

Mi ch

iga n

Ar izo

na Oh

io

Vir gin

ia

Mi nn

eso ta

Respondents by State

Count Percentage

 

 

 

8 [Date]

Table 4: Computation of respondents by State (source; author).

 

2.5 Salary

Analysis of Salary is done using Microsoft Excel Pivot Tables as shown in Figure 5, 6 and

7 below. Figure 5 indicates the average Salary of respondents in respect to their Age

group. The findings indicate that Middle-aged respondents are the most paid, with an

average Salary of $94,398.46, followed by Elderly respondents with an average salary of

$74,110.15. Young people are the least paid, with only an average salary of $45,434.79,

 

 

 

9 [Date]

 

Figure 5: Illustration of respondents’ average salary by age (Source; Author).

Figure 6 below illustrates the average salary by gender among the 399 respondents. The

insights indicates that Male (represented by 1) are the most paid, with an average salary

of $80,733.20 compared to Female who are paid have an average salary of $77,679.78.

 

Figure 6: Illustration of average salary by gender (Source; Author).

Also, Figure 7 illustrates the average salary by State among the respondents. The

findings clearly shows that respondents from Texas are the most paid, with an average

 

 

 

10 [Date]

of $84,970.65. They are followed by New York and Illinois, with $83,897.46 and

$82,723.76 respectively. The least paid respondents come from Virginia State with only

an average salary of $70,860.49.

 

Figure 7: Illustration of average salary by State (Source; Author).

Moreover, Figure is shows an Histogram of the Salary of the respondents, which was

developed based on the ‘Histogram’ tool under the ‘Data Analysis’ tool box of Microsoft

Excel. The Figure shows that salary of the respondents is skewed to the left and majority

of respondents earn between $72,062 and $86,741.

 

Figure 8: Illustration of respondents’ salary using Histogram (Source; Author).

54 48 40 38 31 26 26 25 24 23 21

10 8 6 5 4 4 4 1 1 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

0 10 20 30 40 50 60

86 74

0.8 42

11

79 40

1.5 26

32

50 04

4.2 63

16

94 08

0.1 57

89

72 06

2.2 10

53

42 70

4.9 47

37

10 14

19 .47

37

10 87

58 .78

95

64 72

2.8 94

74

57 38

3.5 78

95

11 60

98 .10

53

13 07

76 .73

68

13 81

16 .05

26

12 34

37 .42

11

14 54

55 .36

84

28 02

6.3 15

79

35 36

5.6 31

58

15 27

94 .68

42 20

68 7

Mo re

Fr eq

ue nc

y

Bin

Histogram

Frequency Cumulative %

 

 

 

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Table 5: Data used in the creation of the Histogram (Source; Author).

 

Lastly, the salary of the respondents was subjected to ‘Descriptive Statistics’ under the

‘Data Analysis’ tool box of Microsoft Excel. The summary of the finding is shown in Table

6 below. The data confirms the Histogram finding that the salary is skewed to the left

since it has a positive Skewness of 0.30602321 (Kim, 2013). However, the negative

Kurtosis of 0.218380955 indicates that the respondents’ salary is less extreme than

 

 

 

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anticipated under normal distribution (Bhatt, 2015). Moreover, the summary shows that

the smallest salary is $20,687 and the highest is $169,134.

Table 6: Summary of Descriptive Statistics (Source; Author).

 

2.6 Respondents Opinion

Figure 9 indicates the respondents Opinion in respect to the environmental policy. The

findings indicate that majority (22%) of the respondents Strongly agree to it, followed by

21% who also agree to it. However, 21% of the respondent disagree about the policy and

20% strongly disagree about it.

 

 

 

13 [Date]

 

Figure 9: Illustration of respondents by Opinion (Source; Author).

Table 7: Computation of respondents Opinion (Source; Author).

 

3.0 Regression analytics

Regression analytics entails inferential statistics that is carried out with the view of finding

solutions to research questions (Trochim and Donnelly, 2001). They establish the

correlation between variables, and unlike descriptive statistics, they are capable of

providing conclusions beyond the data (Statistics.laerd.com, 2018). That is, they can be

used to predict future outcomes. The following regression analysis were carried out to

find answers to the questions developed in this study.

78 85

68

82 86

20% 21%

17%

21% 22%

0%

5%

10%

15%

20%

25%

0 10 20 30 40 50 60 70 80 90

100

Strongly disagree

Disagree Neutral Agree Strongly agree

Respondents Opionion

Count Percentage

 

 

 

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3.1 Correlation between Gender and environmental policy opinion

Findings summarized in Figure 10 below done using Excel ‘Regression’ tool under the

‘Data Analysis’ tool box shows there is a negative correlation between Gender and

Environmental Policy opinion. R Squared shows that 78.15% of the variation in

environmental policy opinion can be explained by Gender.

 

Figure 10: Illustration Gender Line of fit plot (Source; Author)

3.2 Correlation between age differences and environmental policy opinions

Figure 11 below shows also that there is insignificant positive correlation between age

differences and environmental policy opinion. R Squared shows that less than 1% of the

changes in environmental policy opinions that can be explained by Age differences.

y = -0.1615x + 3.2887 R² = 0.0031

y = -0.1615x + 3.2887 R² = 0.7815

0

1

2

3

4

5

6

0 0.5 1 1.5 2 2.5

O pi ni on

Gender

Gender Line Fit Plot

Opinion

Predicted Opinion

Linear (Opinion)

Linear (Predicted Opinion)

 

 

 

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Figure 11: Illustration of Age Line Fit plot (Source; Author).

3.3 Correlation between number of children and environmental policy opinion

The findings summarized in Figure 12 below indicates that there is relatively significant

negative correlation between number of children and the environmental policy opinion in

the USA. R Squared shows 27.1% of the variation in environmental policy opinion is

affected by number of children.

 

Figure 12: Illustration of Children line fit plot (Source; Author).

y = 0.0004x + 3.0187 R² = 2E-05

y = 0.0004x + 3.0187 R² = 0.0054

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70

O pi ni on

Age

Age Line Fit Plot

Opinion

Predicted Opinion

Linear (Opinion)

Linear (Predicted Opinion)

y = -0.0459x + 3.0928 R² = 0.0011

y = -0.0459x + 3.0928 R² = 0.271

0

1

2

3

4

5

6

0 0.5 1 1.5 2 2.5 3 3.5

O pi ni on

Children

Children Line Fit Plot

Opinion

Predicted Opinion

Linear (Opinion)

Linear (Predicted Opinion)

 

 

 

16 [Date]

3.4 Correlation between salary and environmental policy opinion

Figure 13 shows that there is insignificant positive correlation between salary and

environmental policy opinion in the USA. R Squared indicates less than 1% of changes

in environmental policy opinion can be explained by changes in salary.

 

Figure 13: Illustration of Salary line fit plot (Source; Author).

 

y = 2E-07x + 3.0138 R² = 2E-05

y = 2E-07x + 3.0138 R² = 0.0052

0

1

2

3

4

5

6

$0 $50,000 $100,000 $150,000 $200,000

O pi ni on

Salary

Salary Line Fit Plot

Opinion

Predicted Opinion

Linear (Opinion)

Linear (Predicted Opinion)

 

 

 

17 [Date]

Overall summary of the regression output discussed above is shown below;

 

Figure 14: Summary of Regression output (Source; Author).

4.0 Managerial interpretation and implications

The finding of this report has establish that there is a disparity between the existing

literature and the current situation in the USA. First, it is interesting to note that there is

insignificant correlation between age differences and environmental policy opinion in the

USA, contrary to the findings of Torgler et al. (2008), who had argued that a negative

correlation existed between these two variables. Secondly, it is also important to note that

there is no correlation between salary and environmental policy opinion in the USA,

contrary to Mair et al. (2019) findings that suggested high salary could influence

environmental concerns. The implication of these two findings is that increasing the salary

of people in organisation cannot lead to improved support for environment policies.

 

 

 

18 [Date]

Secondly, age is not important in making policy about environment and therefore no

attention is needed as far as this is concerned.

However, the findings establish that Gender has a negative correlation with environmental

policies, with an R Squared of 78.15%. Moreover, descriptive statistics indicated that

majority (59%) of the respondents were Female. Thus, this suggests that Male (41%) are

more likely to be concerned with environment policy than women, contrary to the findings

of Sundstrom and McCright (2014), who claimed a positive correlation existed between

women and environmental concerns. The implications is that women should be educated

more on the need to engage in environmental policy so as to enhance environment

protection and conservations.

Moreover, the findings indicated that a negative correlation exists between number of

children and environmental policy opinion. This is consistent with findings of Wynes and

Nicholas (2017) that suggested fewer children can enhance environmental protection.

The implication of this is that USA and organisations can promote environment policies if

they encourage people to have fewer children.

5.0 Conclusion

This report has analysed how Gender, Age, Number of children and Salary differences

affects environmental policies in the USA. The findings indicate that both Age and Salary

did not have any effect on environmental policy opinion. However, the findings indicated

that Gender affected negatively environmental concerns, especially women. A negative

correlation is also found in respect to Number of children. It is therefore concluded that

USA and organisations in this country can promote environmental policy by encouraging

 

 

 

19 [Date]

people to have fewer children and educating women more about environment and how it

affects them and people across the globe.

 

 

 

 

20 [Date]

References

Bhatt, R. (2015) What is the meaning of negative coefficient of kurtosis obtained in my

specific AFM sample? [online]. Available at:

https://www.researchgate.net/post/What_is_the_meaning_of_negative_coefficient_of

_kurtosis_obtained_in_my_specific_AFM_sample (Accessed 11th July 2019).

Kim, H. Y. (2013). Statistical notes for clinical researchers: assessing normal distribution

(2) using skewness and kurtosis. Restorative dentistry & endodontics, 38(1), 52-54.

Mair, S., Druckman, A., & Jackson, T. (2019) Higher Wages for Sustainable

Development? Employment and Carbon Effects of Paying a Living Wage in Global

Apparel Supply Chains. Ecological economics, 159, 11-23.

Statistics.laerd.com, (2018) Descriptive and inferential statistics. [online]. Available at:

https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php

(Accessed 11th July 2019).

Sundström, A., & McCright, A. M. (2014) Gender differences in environmental concern

among Swedish citizens and politicians. Environmental Politics, 23(6), 1082-1095.

Torgler, B., Garcia-Valiñas, M. A., & Macintyre, A. (2008) Differences in preferences

towards the environment: The impact of a gender, age and parental effect.

Trochim, W. M., & Donnelly, J. P. (2001). Research methods knowledge base (Vol. 2).

Cincinnati, OH: Atomic Dog Publishing.

Van Der Aalst, W. (2016). Data science in action. In Process Mining (pp. 3-23). Springer,

Berlin, Heidelberg.

 

 

 

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Wynes, S., & Nicholas, K. A. (2017) The climate mitigation gap: education and

government recommendations miss the most effective individual

actions. Environmental Research Letters, 12(7), 074024.