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Data Visualisation

University: Arden University

  • Unit No: 12
  • Level: High school
  • Pages: 26 / Words 6457
  • Paper Type: Assignment
  • Course Code: DAT7002
  • Downloads: 289
Question :

This sample will let you know about:

  • What is data visualization?
  • Discuss the importance of data visualization.
Answer :
Organization Selected : N/A

The term data visualisation can be can be defined as a type of technique which is related to graphic presentation of data. It consists preparation of images so that relationship among presented data can be created. The key objective of data visualisation is to communicating effectively, efficiently information. This technique is widely used for those aspects in which there are large number of data are used. Under it, different types of analysed financial data are used in order to find out key findings. In the further part of report, different types of graphs such as pie chart, histogram, bar chart etc. are presented that displays the outcome of different SPSS test. Along with interpretation of these presented tables and graphs is also done. Basically, presentation data in the form of graph is too crucial because by help of it users can understand easily about variances.

The project report is based on a survey which is done on usage of E-mail. For this purpose, 75 questions are used to with a sample size of 1010. These 75 questions are asked in both descriptive and multiple choice questions. In addition, different techniques are also used in the report such as descriptive analysis, correlation, regression and many more.

Research question

A research question is an accountable enquiry into a specific issue or problem. This is the first move toward a research project. The essential step indicates the research topic is the first successful step in the research study, once you have an understanding of what is the actual requirement of the test. In the context of collected sample some of the research question are described underneath which would give detail understanding about the importance of data visualization and interpretation. Such as:

What are main benefits and drawback of using emails?

Descriptive Statistics

 

N

Minimum

Maximum

Mean

Std. Deviation

Variance

What do you believe are the main drawbacks to using email?

1010

1

99

11.58

21.508

462.601

What do you believe are the main benefits of using email?

1010

1

99

3.66

13.303

176.977

Valid N (listwise)

1010

 

 

 

 

 

             

Correlations

 

What do you believe are the main drawbacks to using email?

What do you believe are the main benefits of using email?

What do you believe are the main drawbacks to using email?

Pearson Correlation

1

.325

Sig. (2-tailed)

 

.000

N

1010

1010

What do you believe are the main benefits of using email?

Pearson Correlation

.325

1

Sig. (2-tailed)

.000

 

N

1010

1010

Correlation is significant at the 0.01 level (2-tailed).

Regression analysis:

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.325

.105

.105

20.353

a. Predictors: (Constant), [6] What do you believe are the main benefits of using email?

b. Dependent Variable: [5] What do you believe are the main drawbacks to using email?

 

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig

1

Regression

49222.837

1

49222.837

118.830

.000

Residual

417541.792

1008

414.228

 

 

Total

466764.630

1009

 

 

 

a. Dependent Variable: [5] What do you believe are the main drawbacks to using email?

b. Predictors: (Constant), [6] What do you believe are the main benefits of using email?

 

Coefficients

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

9.656

.664

 

14.538

.000

[6] What do you believe are the main benefits of using email?

.525

.048

.325

10.901

.000

 

 

 

 

 

 

 

a. Dependent Variable: [5] What do you believe are the main drawbacks to using email?

What is average of volume of email send and received in a day within a year?

Descriptive Statistics

 

N

Minimum

Maximum

Mean

Std. Deviation

Variance

[7] On average, how many emails do you send in a day?

1006

1

9

2.27

1.448

2.098

[8] How has the volume of sent messages changed in recent years?

986

1

3

1.29

.686

.471

[9] On average, how many emails do you receive in a day?

1004

1

9

3.06

1.866

3.481

[10] How has the volume of received messages changed in recent years?

992

1

3

1.92

.347

.120

Valid N (listwise)

968

 

 

 

 

 

 

Correlations

 

[7] On average, how many emails do you send in a day?

[8] How has the volume of sent messages changed in recent years?

[9] On average, how many emails do you receive in a day?

[10] How has the volume of received messages changed in recent years?

[7] On average, how many emails do you send in a day?

Pearson Correlation

1

-.149

.735

.042

Sig. (2-tailed)

 

.000

.000

.192

N

1006

986

1001

989

[8] How has the volume of sent messages changed in recent years?

Pearson Correlation

-.149

1

-.126

-.429

Sig. (2-tailed)

.000

 

.000

.000

N

986

986

982

972

[9] On average, how many emails do you receive in a day?

Pearson Correlation

.735

-.126

1

.089

Sig. (2-tailed)

.000

.000

 

.005

N

1001

982

1004

987

[10] How has the volume of received messages changed in recent years?

Pearson Correlation

.042

-.429

.089

1

Sig. (2-tailed)

.192

.000

.005

 

N

989

972

987

992

Correlation is significant at the 0.01 level (2-tailed).

Chi- square test

Test Statistics

 

[7] On average, how many emails do you send in a day?

[8] How has the volume of sent messages changed in recent years?

[9] On average, how many emails do you receive in a day?

[10] How has the volume of received messages changed in recent years?

Chi-Square

1377.748

1158.635

765.594

1313.845

df

8

2

8

2

Asymp. Sig.

.000

.000

.000

.000

a. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 111.8.

b. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 328.7.

c. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 111.6.

d. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 330.7.

[7] On average, how many emails do you send in a day?

 

 

Observed N

Expected N

Residual

 

0 - 10

371

111.8

259.2

 

11 - 20

301

111.8

189.2

 

21 - 30

168

111.8

56.2

 

31 - 40

83

111.8

-28.8

 

41 - 50

53

111.8

-58.8

 

51 - 60

14

111.8

-97.8

 

61 - 70

5

111.8

-106.8

 

71 - 80

4

111.8

-107.8

 

81 +

7

111.8

-104.8

 

Total

1006

 

 

 

 

[8] How has the volume of sent messages changed in recent years?

 

Observed N

Expected N

Residual

Increased

829

328.7

500.3

Decreased

27

328.7

-301.7

Stayed the same

130

328.7

-198.7

Total

986

 

 

 

[9] On average, how many emails do you receive in a day?

 

Observed N

Expected N

Residual

0 - 10

174

111.6

62.4

11 - 20

310

111.6

198.4

21 - 30

219

111.6

107.4

31 - 40

116

111.6

4.4

41 - 50

81

111.6

-30.6

51 - 60

40

111.6

-71.6

61 - 70

23

111.6

-88.6

71 - 80

14

111.6

-97.6

81 +

27

111.6

-84.6

Total

1004

 

 

 

[10] How has the volume of received messages changed in recent years?

 

Observed N

Expected N

Residual

Stayed the same

101

330.7

-229.7

Increased

867

330.7

536.3

Decreased

24

330.7

-306.7

Total

992

 

 

How do you consider email to be time wasting with example?

Descriptive Statistics

 

N

Range

Minimum

Maximum

Mean

Std. Deviation

[17] Do you waste any time using email?

1003

1

1

2

1.60

.491

[19a] Please provide an example of how time is wasted

1010

98

1

99

39.40

46.456

Valid N (listwise)

1003

 

 

 

 

 

 

Correlations

 

[17] Do you waste any time using email?

[19a] Please provide an example of how time is wasted

[17] Do you waste any time using email?

Pearson Correlation

1

-.831**

Sig. (2-tailed)

 

.000

N

1003

1003

[19a] Please provide an example of how time is wasted

Pearson Correlation

-.831**

1

Sig. (2-tailed)

.000

 

N

1003

1010

Correlation is significant at the 0.01 level (2-tailed).

Regression analysis

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.161

.026

.025

.485

2

.831

.691

.690

.274

3

.831

.691

.690

.274

a. Predictors: (Constant), [9] On average, how many emails do you receive in a day?

b. Predictors: (Constant), [9] On average, how many emails do you receive in a day?, [19a] Please provide an example of how time is wasted

c. Predictors: (Constant), [9] On average, how many emails do you receive in a day?, [19a] Please provide an example of how time is wasted, [10] How has the volume of received messages changed in recent years?

d. Dependent Variable: [17] Do you waste any time using email?

 

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

6.107

1

6.107

25.919

.000

Residual

230.443

978

.236

 

 

Total

236.550

979

 

 

 

2

Regression

163.378

2

81.689

1090.718

.000

Residual

73.172

977

.075

 

 

Total

236.550

979

 

 

 

3

Regression

163.441

3

54.480

727.308

.000

Residual

73.109

976

.075

 

 

Total

236.550

979

 

 

 

a. Dependent Variable: [17] Do you waste any time using email?

b. Predictors: (Constant), [9] On average, how many emails do you receive in a day?

c. Predictors: (Constant), [9] On average, how many emails do you receive in a day?, [19a] Please provide an example of how time is wasted

d. Predictors: (Constant), [9] On average, how many emails do you receive in a day?, [19a] Please provide an example of how time is wasted, [10] How has the volume of received messages changed in recent years?

 

Coefficients

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

1.463

.030

 

48.953

.000

[9] On average, how many emails do you receive in a day?

.043

.008

.161

5.091

.000

2

(Constant)

1.930

.020

 

98.013

.000

[9] On average, how many emails do you receive in a day?

.004

.005

.016

.866

.387

[19a] Please provide an example of how time is wasted

-.009

.000

-.828

-45.825

.000

3

(Constant)

1.885

.052

 

35.907

.000

[9] On average, how many emails do you receive in a day?

.004

.005

.014

.798

.425

[19a] Please provide an example of how time is wasted

-.009

.000

-.827

-45.544

.000

[10] How has the volume of received messages changed in recent years?

.023

.025

.016

.917

.359

a. Dependent Variable: [17] Do you waste any time using email?

Methods

Data visualisation- As above stated that, this technique plays a key role in the context of understanding data in most effective manner. It contributes effectively in order to making better decisions. Herein, underneath key objectives of this method are mentioned in such manner:

  • Determination of frequency- This is one of the key benefit of data visualisation which is related to determination of level of frequency so that complexity of data can be reduced. As well as in the case when time factor is included in the data set then by help data visualisation technique, measurement of frequency becomes easier.
  • Determination of relationships (Correlations) – In the context of huge number of data set this becomes difficult to measure relationship between two variables. But by help of above mentioned method, this becomes easier to find out relationship between two different range of data set.
  • Examining of a network- In addition, data visualisation technique is helpful for examining network. This is very useful for market research because marketing professionals want to know about targeted audience so that they can analyse whole market. It this aspect, the above mentioned technique is too useful because by help of it marketing teams can do better analysis of their target market and customers. Ask for assignment help from our experts!
  • Analysing value and risk- Another benefit of data visualisation technique is that it contributes in assessing value and risk involved in a particular segment of data. For example, determination of complex metrics need different types of variables and due to which it becomes impossible to see accuracy with a plain spreadsheet.

In order to visualise given data of 1010 respondents, a computer software has been used in the report that is Statistical Package for the Social Sciences. This is a type of software package which is used effective and interactive statistical analysis. Under this application, relationship between two variables is calculated. In the aspect of given data set, different types of tests are performed under Statistical Package for the Social Sciences. These tests are done in order to find out relation between two variables, to calculate mean, mode, median etc. Herein, underneath name of these tests is mentioned in such manner:

  • Descriptive statistics- This can be defined as a type of technique which is related to summarising a given data range that can be either a presentation of complete or sample of population. This statistic can be break down into measures of central tendency and measures of spread. The calculation of central tendency consists mean, mode and median.  As well as measurement of variability consists standard deviation, variances etc.
  • Regression analysis- It can be defined as a type of statistical method which is beneficial to examine the relation between two or more than two variables of interest. There are different types of regression analysis. The objective of this technique is to find out relationship between dependent and independent variables. In the aspect of above data set, this test has been applied in order to find out relationship between two variables.
  • Correlation analysis- It is type of statistical method that is applied to evaluate the strength of relation between two quantitative variables. In this type of analysis, higher correlation indicates that two or more than two variables have a stronger relation with each other. On the other hand, if there is weak correlation then it shows that variables are related with each other hardly. In other words, this may be defined as a type of procedure which is related to studying the strength of relation with available statistical data. It is stringently aligned to linear regression analysis which is an approach for modelling of association between dependent and independent variables. Basically, the key objective of this tool is to give a common overview of correlation analysis in order to apply it biomedical application. Get Assignment Examples?Talk to our Experts!
  • Chi square test- It is a type of test which is applicable to perform when the test is chi-squared distributed in the null hypothesis. This is used to person's chi-squared test. In the aspect of above data set of 1010 respondents, this test has been applied in order to find out difference between the observed value and expected values. For instance, in the absence of this test there will be lack of understanding between different types of variables. 

Discussion

A structured, step-by-step approach is used to create an effective training course. Many stand-alone educational programs (one-off events) fails to meet corporate goals and objectives of the learners. We detail the five required steps in today's post to build effective training initiatives that will generate meaningful financial impact. In the aspect of above survey for enhancing e-mail usage, there is a systematic process that consists different types of steps and some of them are mentioned in such manner:

  • Assessment of need of training- Identifying requirements is the first step to creating a training programme. Learning requirements of workers may already be established in financial, human capital or individual growth strategies of the company. If individuals are designing the learning program from scratch (without fixed goals) they will need to carry out evaluations of the training needs. Such as in the aspect of above training program of enhancing usage of e-mail, this is necessary to identify need of training. This is so because on the basis of it, further steps can be applied in an effective manner.
  • Set organizational training objectives- Assessments of the training requirements (organizational, role & individual) will recognize the holes in current training plans and skill sets for workers. Such differences should be identified and evaluated, then translated into the performance priorities of the organisation. The ultimate objective is to bridge the gap between current performance and ideal output by creating a training programme. The curriculum at the workplace level will suit the areas for improvement found by 360 degree tests. Such as in regards with above training program, this is necessary to set organisational objectives.

Apart from it, in regards to enhance the e-mail usage by users there are some key point which may contribute in an effective manner. The explanation of those points is done below in such manner:

  • It’s all about format- It is one of the key aspect for e-mail system which needed to be consider before mailing any message. This is so important because of formatting of mail will not be so effective then it may put a negative impact on receiver as well as on sender. There are some key rules for creating a suitable e-mail such as do not write a million paragraph as well as sender should include proper formatting like punctuation, capitalisation and many more. These all small things put a significant impact on the receivers and these should be considering while sending a mail to a person. In the aspect of improving usage of e-mail, consideration of this point may help in improving mail system.
  • Proof before send- This is also an another aspect for improvement of e-mail. It is necessary for senders to make proper analysis of content which they wrote. After making proof reading all the content, senders should click on the send button. In the absence of proper proof reading there can be a lot of issues such as possibility of a lot of grammatical errors, lack of effective punctuations in the written content and many more. To avoid these mistakes, it is essential for senders that they should do a proof read of all content. In the context of above training plan for improving e-mail usage, this step can be more effective and useful. For this purpose, this is important that users should be aware about importance of this point. In addition, this is important to know that users are needed to be provided a proper amount of training about this so that they can assure about steps that are needed to be followed before sending a mail. Management seems to have managerial objectives such as increased efficiency, profitability, consistency as well as employee satisfaction, to mention several. They will develop tailored initiatives until which help to recognize the targets.
  • Get specific knowledge about target audience: It is observed that when people are even more busy reply to their email without paying much attention towards the format of email which might create difficulties for the person reading on the other side.One aspect which really takes a longer time to examine is particularly when sending an email to the people with a high reputation is that a quick or small email will not imply that they are angry, irritated, or annoyed. The first need to consider the email is to set the target to whom the email is needed to be delivered. So people with higher profile are required to be send specific message through email which shows that sender have respect for their time. Ask to do my assignment from our professional experts!
  • Have a fix signature: It is important to have a fix signature or a separate icon which do not confuse the reader as many time different emails are received form a single sender. In training program specific guidelines are provided to each user to set their legal and identical signature which makes easier for the individual to read the message as per their convenient. As people use to read email which have a legal proof and respond accordingly either to revert the email or not.
  • Be proactive: So many individuals see email primarily as something to keep on top above all other activity in a day. Thus sender must always be concerned that each and every single individual that emails us will provide replies. Email communication is a powerful tool for meeting loyal customers and keeping an eye out. Link users to the network and material and honestly believe that they would like to experience the services which are provided to these people.

CONCLUSION

In the end of report, it is concluded that data visualisation is an effective technique which is beneficial to display a large amount of data in the graphical form that help to ease the process of interpretation. This also support to make meaningful decision and make proper steps which are valuable to develop new training program for increasing the effectiveness of a specific task. With the usage of different statistical tool which are important to determine the average mean between different variable and evaluate the relationship among these variable.

 Also Read- Analysis of Political System

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