# Analysis of Infection Rate of COVID-19 in White and BAME Patients

VerifiedAdded on 2023/01/04

|16

|2302

|76

AI Summary

This report analyzes the infection rate of COVID-19 in white and BAME patients in the UK. It includes a detailed data set and hypothesis testing using statistical tests.

## Contribute Materials

Your contribution can guide someone’s learning journey. Share your
documents today.

ACCB4002 Data Analysis and Visualization

## Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.

Contents

INTRODUCTION.......................................................................................................................................3

MAIN BODY..............................................................................................................................................3

Analysis...................................................................................................................................................3

Practical application and deployment......................................................................................................9

CONCLUSION.........................................................................................................................................15

REFERENCES..........................................................................................................................................16

INTRODUCTION.......................................................................................................................................3

MAIN BODY..............................................................................................................................................3

Analysis...................................................................................................................................................3

Practical application and deployment......................................................................................................9

CONCLUSION.........................................................................................................................................15

REFERENCES..........................................................................................................................................16

INTRODUCTION

The report is based on analysis of infection rate of COVID– 19 are varied between white people

and BAME in the UK. The data is based on 50 different white and BAME patients. The report

contains detailed given data set in order to determine which types of patients affected more from

COVID 19. In order to do so various kinds of hypothesis are prepared that are tested from a

suitable statistical testes.

MAIN BODY

Analysis

Description of data set.

The given data set is about 50 people who are from different age group. The age is between 20 to

50 years. In the data set, infection rate of various kinds of people who are of white and BAME is

mentioned. The objective of data set is to assess ratio of infection rate of both types of people

including white and BAME (Black, Asian and Minority Ethnic) people. Below descriptive

analysis of given data is done in such manner:

The analysis of historical data to help explain improvements that have happened in an

organization is descriptive analytics (Borrill, Ramirez-Gonzalez and Uauy, 2016). The use of a

set of historical data to make parallels is represented in Descriptive analytics.

Descriptive statistics:

Statistics

White

patient BAME

N Valid 50 50

Missing 0 0

Mean 36.180 36.740

Median 36.000 34.500

The report is based on analysis of infection rate of COVID– 19 are varied between white people

and BAME in the UK. The data is based on 50 different white and BAME patients. The report

contains detailed given data set in order to determine which types of patients affected more from

COVID 19. In order to do so various kinds of hypothesis are prepared that are tested from a

suitable statistical testes.

MAIN BODY

Analysis

Description of data set.

The given data set is about 50 people who are from different age group. The age is between 20 to

50 years. In the data set, infection rate of various kinds of people who are of white and BAME is

mentioned. The objective of data set is to assess ratio of infection rate of both types of people

including white and BAME (Black, Asian and Minority Ethnic) people. Below descriptive

analysis of given data is done in such manner:

The analysis of historical data to help explain improvements that have happened in an

organization is descriptive analytics (Borrill, Ramirez-Gonzalez and Uauy, 2016). The use of a

set of historical data to make parallels is represented in Descriptive analytics.

Descriptive statistics:

Statistics

White

patient BAME

N Valid 50 50

Missing 0 0

Mean 36.180 36.740

Median 36.000 34.500

Mode 20.0a 50.0

Std. Deviation 9.4192 9.3171

Variance 88.722 86.809

a. Multiple modes exist. The smallest

value is shown

White patient

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid 20.0 4 8.0 8.0 8.0

22.0 2 4.0 4.0 12.0

25.0 2 4.0 4.0 16.0

26.0 1 2.0 2.0 18.0

27.0 2 4.0 4.0 22.0

28.0 1 2.0 2.0 24.0

29.0 1 2.0 2.0 26.0

30.0 2 4.0 4.0 30.0

32.0 1 2.0 2.0 32.0

33.0 4 8.0 8.0 40.0

34.0 2 4.0 4.0 44.0

35.0 3 6.0 6.0 50.0

37.0 2 4.0 4.0 54.0

38.0 2 4.0 4.0 58.0

40.0 4 8.0 8.0 66.0

42.0 1 2.0 2.0 68.0

43.0 4 8.0 8.0 76.0

44.0 3 6.0 6.0 82.0

45.0 1 2.0 2.0 84.0

Std. Deviation 9.4192 9.3171

Variance 88.722 86.809

a. Multiple modes exist. The smallest

value is shown

White patient

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid 20.0 4 8.0 8.0 8.0

22.0 2 4.0 4.0 12.0

25.0 2 4.0 4.0 16.0

26.0 1 2.0 2.0 18.0

27.0 2 4.0 4.0 22.0

28.0 1 2.0 2.0 24.0

29.0 1 2.0 2.0 26.0

30.0 2 4.0 4.0 30.0

32.0 1 2.0 2.0 32.0

33.0 4 8.0 8.0 40.0

34.0 2 4.0 4.0 44.0

35.0 3 6.0 6.0 50.0

37.0 2 4.0 4.0 54.0

38.0 2 4.0 4.0 58.0

40.0 4 8.0 8.0 66.0

42.0 1 2.0 2.0 68.0

43.0 4 8.0 8.0 76.0

44.0 3 6.0 6.0 82.0

45.0 1 2.0 2.0 84.0

## Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser

48.0 2 4.0 4.0 88.0

49.0 1 2.0 2.0 90.0

50.0 4 8.0 8.0 98.0

55.0 1 2.0 2.0 100.0

Total 50 100.0 100.0

BAME

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid 20.0 2 4.0 4.0 4.0

22.0 1 2.0 2.0 6.0

23.0 1 2.0 2.0 8.0

24.0 1 2.0 2.0 10.0

26.0 1 2.0 2.0 12.0

27.0 2 4.0 4.0 16.0

28.0 2 4.0 4.0 20.0

29.0 2 4.0 4.0 24.0

30.0 6 12.0 12.0 36.0

31.0 1 2.0 2.0 38.0

32.0 3 6.0 6.0 44.0

33.0 1 2.0 2.0 46.0

34.0 2 4.0 4.0 50.0

35.0 1 2.0 2.0 52.0

39.0 1 2.0 2.0 54.0

40.0 3 6.0 6.0 60.0

41.0 1 2.0 2.0 62.0

43.0 1 2.0 2.0 64.0

44.0 4 8.0 8.0 72.0

49.0 1 2.0 2.0 90.0

50.0 4 8.0 8.0 98.0

55.0 1 2.0 2.0 100.0

Total 50 100.0 100.0

BAME

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid 20.0 2 4.0 4.0 4.0

22.0 1 2.0 2.0 6.0

23.0 1 2.0 2.0 8.0

24.0 1 2.0 2.0 10.0

26.0 1 2.0 2.0 12.0

27.0 2 4.0 4.0 16.0

28.0 2 4.0 4.0 20.0

29.0 2 4.0 4.0 24.0

30.0 6 12.0 12.0 36.0

31.0 1 2.0 2.0 38.0

32.0 3 6.0 6.0 44.0

33.0 1 2.0 2.0 46.0

34.0 2 4.0 4.0 50.0

35.0 1 2.0 2.0 52.0

39.0 1 2.0 2.0 54.0

40.0 3 6.0 6.0 60.0

41.0 1 2.0 2.0 62.0

43.0 1 2.0 2.0 64.0

44.0 4 8.0 8.0 72.0

45.0 3 6.0 6.0 78.0

46.0 1 2.0 2.0 80.0

47.0 3 6.0 6.0 86.0

50.0 7 14.0 14.0 100.0

Total 50 100.0 100.0

46.0 1 2.0 2.0 80.0

47.0 3 6.0 6.0 86.0

50.0 7 14.0 14.0 100.0

Total 50 100.0 100.0

Development of appropriate hypothesis:

Hypothesis- A research hypothesis is a real, direct, and verifiable premise or statistical assertion

on the possible consequence of an academic research analysis based on a community specific

resource, such as assumed discrepancies between communities on a single factor or correlations

(Hammersley, 2016).

H0: There is significance difference between infection rate in white patients and BAME.

Hypothesis- A research hypothesis is a real, direct, and verifiable premise or statistical assertion

on the possible consequence of an academic research analysis based on a community specific

resource, such as assumed discrepancies between communities on a single factor or correlations

(Hammersley, 2016).

H0: There is significance difference between infection rate in white patients and BAME.

## Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.

H1: There is no significance difference between infection rate in white patients and BAME.

H0: There is relation between infection rate in white patients and BAME.

H1: There is no relation between infection rate in white patients and BAME.

Techniques to perform the analysis:

There are range of methods and techniques to perform the analysis and ANOVA test is one of

the useful methods to measure significant difference in a particular data set. Apart from this,

correlation analysis is also an essential method to assess relation between given data set of white

patients and BAME.

One sample T-test-A one-sample t-test is being used to check if a data obtained is

considerably different from any significance level. Every makes statements on how the

confidence interval meaning μ is linked to some significance level M. A t-test is a type of

descriptive analysis that is used to assess whether there is a substantial contrast between

the different measures that can be attributed to certain characteristics (McCormick and

Salcedo, 2017). It is used if the data sets live up to expectations and may have unexpected

differences, such as the sample group recorded as a consequence of 100 times spinning

the dice. A t-test was used as a principle assessment tool, which enables testing of even

an assumption unique to a community.

Correlation- In statistics, any statistical association, whether reciprocal or not, between

two or more variables or multivariate data is association or dependency. Correlation is

any clear relationship in the broadest terms, although it generally refers to the extent with

which a set of parameters are linearly connected (Zuo, Carranza and Wang, 2016).

Correlations are useful since a statistical association that can be manipulated in action can

be suggested. For instance, show the correlation between generation capacity and

weather, an electricity utility could generate less capacity on a mild day. There is indeed

a causal correlation in this case, since severe weather induces individuals to use more

H0: There is relation between infection rate in white patients and BAME.

H1: There is no relation between infection rate in white patients and BAME.

Techniques to perform the analysis:

There are range of methods and techniques to perform the analysis and ANOVA test is one of

the useful methods to measure significant difference in a particular data set. Apart from this,

correlation analysis is also an essential method to assess relation between given data set of white

patients and BAME.

One sample T-test-A one-sample t-test is being used to check if a data obtained is

considerably different from any significance level. Every makes statements on how the

confidence interval meaning μ is linked to some significance level M. A t-test is a type of

descriptive analysis that is used to assess whether there is a substantial contrast between

the different measures that can be attributed to certain characteristics (McCormick and

Salcedo, 2017). It is used if the data sets live up to expectations and may have unexpected

differences, such as the sample group recorded as a consequence of 100 times spinning

the dice. A t-test was used as a principle assessment tool, which enables testing of even

an assumption unique to a community.

Correlation- In statistics, any statistical association, whether reciprocal or not, between

two or more variables or multivariate data is association or dependency. Correlation is

any clear relationship in the broadest terms, although it generally refers to the extent with

which a set of parameters are linearly connected (Zuo, Carranza and Wang, 2016).

Correlations are useful since a statistical association that can be manipulated in action can

be suggested. For instance, show the correlation between generation capacity and

weather, an electricity utility could generate less capacity on a mild day. There is indeed

a causal correlation in this case, since severe weather induces individuals to use more

energy for central heating. In particular, although the existence of a connection is not

likely to be sufficient that a unidirectional causality is present.

Techniques to present the result of analysis:

In order to present data analysis in a report there is no specific method or process. Herein, it is

crucial to consider that there should be appropriate tables and graphs in presentation of particular

outcome from various kinds of tests (Nelson, Barnes, and Minnier, 2017). As in the above part,

there will be two types of tests which are ANOVA and correlation and these should be presented

by help of appropriate tables and graphs in the project report so that users can understand about

nature of infection rate between white patients and BAME in United Kingdom. Below a range of

tables and graphs are presented in order to produce a detailed analysis of inflation rate of white

people and BAME.

Practical application and deployment.

Application of techniques:

Testing of hypothesis one-

At the level of 0.10

One sample T-test

One-Sample Statistics

Statistic

Bootstrapa

Bias Std. Error

90% Confidence

Interval

Lower Upper

White

patient

N 50

Mean 36.180 .017 1.309 33.941 38.239

Std. Deviation 9.4192 -.1335 .7203 8.1647 10.4345

likely to be sufficient that a unidirectional causality is present.

Techniques to present the result of analysis:

In order to present data analysis in a report there is no specific method or process. Herein, it is

crucial to consider that there should be appropriate tables and graphs in presentation of particular

outcome from various kinds of tests (Nelson, Barnes, and Minnier, 2017). As in the above part,

there will be two types of tests which are ANOVA and correlation and these should be presented

by help of appropriate tables and graphs in the project report so that users can understand about

nature of infection rate between white patients and BAME in United Kingdom. Below a range of

tables and graphs are presented in order to produce a detailed analysis of inflation rate of white

people and BAME.

Practical application and deployment.

Application of techniques:

Testing of hypothesis one-

At the level of 0.10

One sample T-test

One-Sample Statistics

Statistic

Bootstrapa

Bias Std. Error

90% Confidence

Interval

Lower Upper

White

patient

N 50

Mean 36.180 .017 1.309 33.941 38.239

Std. Deviation 9.4192 -.1335 .7203 8.1647 10.4345

Std. Error

Mean 1.3321

BAME N 50

Mean 36.740 -.009 1.333 34.441 38.880

Std. Deviation 9.3171 -.1038 .5523 8.3032 10.1280

Std. Error

Mean 1.3176

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

One-Sample Test

Test Value = 0

t df

Sig. (2-

tailed)

Mean

Difference

90% Confidence Interval of

the Difference

Lower Upper

White

patient 27.161 49 .000 36.1800 33.947 38.413

BAME 27.883 49 .000 36.7400 34.531 38.949

Bootstrap for One-Sample Test

Mean

Difference

Bootstrapa

Bias Std. Error

Sig. (2-

tailed)

90% Confidence

Interval

Lower Upper

White

patient 36.1800 .0175 1.3088 .001 33.9410 38.2390

BAME 36.7400 -.0092 1.3328 .001 34.4410 38.8800

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Mean 1.3321

BAME N 50

Mean 36.740 -.009 1.333 34.441 38.880

Std. Deviation 9.3171 -.1038 .5523 8.3032 10.1280

Std. Error

Mean 1.3176

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

One-Sample Test

Test Value = 0

t df

Sig. (2-

tailed)

Mean

Difference

90% Confidence Interval of

the Difference

Lower Upper

White

patient 27.161 49 .000 36.1800 33.947 38.413

BAME 27.883 49 .000 36.7400 34.531 38.949

Bootstrap for One-Sample Test

Mean

Difference

Bootstrapa

Bias Std. Error

Sig. (2-

tailed)

90% Confidence

Interval

Lower Upper

White

patient 36.1800 .0175 1.3088 .001 33.9410 38.2390

BAME 36.7400 -.0092 1.3328 .001 34.4410 38.8800

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

## Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser

At the level of 0.01:

One-Sample Statistics

Statistic

Bootstrapa

Bias Std. Error

90% Confidence

Interval

Lower Upper

White

patient

N 50

Mean 36.180 .029 1.322 34.041 38.499

Std. Deviation 9.4192 -.1412 .7189 8.1234 10.4846

Std. Error

Mean 1.3321

BAME N 50

Mean 36.740 -.016 1.314 34.561 38.820

Std. Deviation 9.3171 -.1231 .5529 8.2384 10.0995

Std. Error

Mean 1.3176

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

One-Sample Test

Test Value = 0

t df

Sig. (2-

tailed)

Mean

Difference

99.9% Confidence Interval

of the Difference

Lower Upper

White

patient 27.161 49 .000 36.1800 31.517 40.843

BAME 27.883 49 .000 36.7400 32.128 41.352

One-Sample Statistics

Statistic

Bootstrapa

Bias Std. Error

90% Confidence

Interval

Lower Upper

White

patient

N 50

Mean 36.180 .029 1.322 34.041 38.499

Std. Deviation 9.4192 -.1412 .7189 8.1234 10.4846

Std. Error

Mean 1.3321

BAME N 50

Mean 36.740 -.016 1.314 34.561 38.820

Std. Deviation 9.3171 -.1231 .5529 8.2384 10.0995

Std. Error

Mean 1.3176

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

One-Sample Test

Test Value = 0

t df

Sig. (2-

tailed)

Mean

Difference

99.9% Confidence Interval

of the Difference

Lower Upper

White

patient 27.161 49 .000 36.1800 31.517 40.843

BAME 27.883 49 .000 36.7400 32.128 41.352

Bootstrap for One-Sample Test

Mean

Difference

Bootstrapa

Bias Std. Error

Sig. (2-

tailed)

90% Confidence

Interval

Lower Upper

White

patient 36.1800 .0294 1.3218 .001 34.0410 38.4990

BAME 36.7400 -.0160 1.3143 .001 34.5610 38.8200

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

At the level of 0.02

One-Sample Statistics

Statistic

Bootstrapa

Bias Std. Error

90% Confidence

Interval

Lower Upper

White

patient

N 50

Mean 36.180 .004 1.258 34.140 38.299

Std. Deviation 9.4192 -.1164 .6726 8.1665 10.4398

Std. Error

Mean 1.3321

BAME N 50

Mean 36.740 -.008 1.305 34.501 38.840

Std. Deviation 9.3171 -.1215 .5635 8.1801 10.1315

Mean

Difference

Bootstrapa

Bias Std. Error

Sig. (2-

tailed)

90% Confidence

Interval

Lower Upper

White

patient 36.1800 .0294 1.3218 .001 34.0410 38.4990

BAME 36.7400 -.0160 1.3143 .001 34.5610 38.8200

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

At the level of 0.02

One-Sample Statistics

Statistic

Bootstrapa

Bias Std. Error

90% Confidence

Interval

Lower Upper

White

patient

N 50

Mean 36.180 .004 1.258 34.140 38.299

Std. Deviation 9.4192 -.1164 .6726 8.1665 10.4398

Std. Error

Mean 1.3321

BAME N 50

Mean 36.740 -.008 1.305 34.501 38.840

Std. Deviation 9.3171 -.1215 .5635 8.1801 10.1315

Std. Error

Mean 1.3176

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

One-Sample Test

Test Value = 0

t df

Sig. (2-

tailed)

Mean

Difference

98% Confidence Interval of

the Difference

Lower Upper

White

patient 27.161 49 .000 36.1800 32.976 39.384

BAME 27.883 49 .000 36.7400 33.571 39.909

Bootstrap for One-Sample Test

Mean

Difference

Bootstrapa

Bias Std. Error

Sig. (2-

tailed)

90% Confidence

Interval

Lower Upper

White

patient 36.1800 .0043 1.2584 .001 34.1400 38.2990

BAME 36.7400 -.0078 1.3048 .001 34.5010 38.8400

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Testing of hypothesis two:

Correlation analysis-

Mean 1.3176

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

One-Sample Test

Test Value = 0

t df

Sig. (2-

tailed)

Mean

Difference

98% Confidence Interval of

the Difference

Lower Upper

White

patient 27.161 49 .000 36.1800 32.976 39.384

BAME 27.883 49 .000 36.7400 33.571 39.909

Bootstrap for One-Sample Test

Mean

Difference

Bootstrapa

Bias Std. Error

Sig. (2-

tailed)

90% Confidence

Interval

Lower Upper

White

patient 36.1800 .0043 1.2584 .001 34.1400 38.2990

BAME 36.7400 -.0078 1.3048 .001 34.5010 38.8400

a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Testing of hypothesis two:

Correlation analysis-

## Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.

Correlations

White

patient BAME

White

patient

Pearson

Correlation 1 .072

Sig. (2-tailed) .621

N 50 50

BAME Pearson

Correlation .072 1

Sig. (2-tailed) .621

N 50 50

Interpretation of results.

Descriptive analysis- In terms of above done descriptive analysis of above data set, this can be

inferred that value of mean is of 36.18 while standard deviation is of 9.42. Thus, this can be

stated that there is no relation between infection rate of white patients and BAME. This is so

because if value of mean and standard deviation varies from each other than it can be inferred

that data sets are not closed to each other.

Hypothesis one:

At level of 0.10- On the basis of above performed one sample t – test this can be stated that value

of significance difference or P is of 0.00 which is lower than 0.05. Thus, it can be interpreted that

null hypothesis is true and there is significance difference between infection rate in white

patients and BAME at the level of 0.10.

At level of 0.01- Similar to this, at the level of 0.01 it can be stated that value of p is same which

is of 0.001 that is less than 0.05. Hence, this can be assessed that null hypothesis is correct and

White

patient BAME

White

patient

Pearson

Correlation 1 .072

Sig. (2-tailed) .621

N 50 50

BAME Pearson

Correlation .072 1

Sig. (2-tailed) .621

N 50 50

Interpretation of results.

Descriptive analysis- In terms of above done descriptive analysis of above data set, this can be

inferred that value of mean is of 36.18 while standard deviation is of 9.42. Thus, this can be

stated that there is no relation between infection rate of white patients and BAME. This is so

because if value of mean and standard deviation varies from each other than it can be inferred

that data sets are not closed to each other.

Hypothesis one:

At level of 0.10- On the basis of above performed one sample t – test this can be stated that value

of significance difference or P is of 0.00 which is lower than 0.05. Thus, it can be interpreted that

null hypothesis is true and there is significance difference between infection rate in white

patients and BAME at the level of 0.10.

At level of 0.01- Similar to this, at the level of 0.01 it can be stated that value of p is same which

is of 0.001 that is less than 0.05. Hence, this can be assessed that null hypothesis is correct and

there is significance difference between infection rate in white patients and BAME at the level of

0.01.

At the level of 0.02- In contrast to this, only at point of 0.02, the measured value is 0.00 at the

level of 0.02, it is less than 0.05, which indicates there is no assumption. It may also be reported

there is a substantial discrepancy between the prevalence of disease in white people and the rate

of BAME at 0.02.

Testing of hypothesis two:

In the second principle, inference was carried out. On the grounds of the figures provided

mentioned, it can be assumed that the Value of correlation coefficient is 0.72, which is much

more than 0.6. Therefore, the favorable and effective association between the infection rate

between patients treated and BAME can be reported. Besides this, the sum of the discrepancy in

magnitude is 0.621, which indicates that there is no null hypothesis and the value is greater than

0.05.

CONCLUSION

On the basis of above project report this can be concluded that there is significance difference

between infection rate of white patients and BAME. As well as there is correlation between both

infection rate of white people and BAME. This shows that ratio of infection rate is closer to each

other aspect of both white people and BAME.

0.01.

At the level of 0.02- In contrast to this, only at point of 0.02, the measured value is 0.00 at the

level of 0.02, it is less than 0.05, which indicates there is no assumption. It may also be reported

there is a substantial discrepancy between the prevalence of disease in white people and the rate

of BAME at 0.02.

Testing of hypothesis two:

In the second principle, inference was carried out. On the grounds of the figures provided

mentioned, it can be assumed that the Value of correlation coefficient is 0.72, which is much

more than 0.6. Therefore, the favorable and effective association between the infection rate

between patients treated and BAME can be reported. Besides this, the sum of the discrepancy in

magnitude is 0.621, which indicates that there is no null hypothesis and the value is greater than

0.05.

CONCLUSION

On the basis of above project report this can be concluded that there is significance difference

between infection rate of white patients and BAME. As well as there is correlation between both

infection rate of white people and BAME. This shows that ratio of infection rate is closer to each

other aspect of both white people and BAME.

REFERENCES

Borrill, P., Ramirez-Gonzalez, R. and Uauy, C., 2016. expVIP: a customizable RNA-seq data

analysis and visualization platform. Plant physiology, 170(4), pp.2172-2186.

Hammersley, A.P., 2016. FIT2D: a multi-purpose data reduction, analysis and visualization

program. Journal of Applied Crystallography, 49(2), pp.646-652.

McCormick, K. and Salcedo, J., 2017. SPSS statistics for data analysis and visualization. John

Wiley & Sons.

Zuo, R., Carranza, E.J.M. and Wang, J., 2016. Spatial analysis and visualization of exploration

geochemical data. Earth-Science Reviews, 158, pp.9-18.

Nelson, J.W., Sklenar, J., Barnes, A.P. and Minnier, J., 2017. The START App: a web-based

RNAseq analysis and visualization resource. Bioinformatics, 33(3), pp.447-449.

Borrill, P., Ramirez-Gonzalez, R. and Uauy, C., 2016. expVIP: a customizable RNA-seq data

analysis and visualization platform. Plant physiology, 170(4), pp.2172-2186.

Hammersley, A.P., 2016. FIT2D: a multi-purpose data reduction, analysis and visualization

program. Journal of Applied Crystallography, 49(2), pp.646-652.

McCormick, K. and Salcedo, J., 2017. SPSS statistics for data analysis and visualization. John

Wiley & Sons.

Zuo, R., Carranza, E.J.M. and Wang, J., 2016. Spatial analysis and visualization of exploration

geochemical data. Earth-Science Reviews, 158, pp.9-18.

Nelson, J.W., Sklenar, J., Barnes, A.P. and Minnier, J., 2017. The START App: a web-based

RNAseq analysis and visualization resource. Bioinformatics, 33(3), pp.447-449.

1 out of 16

### Related Documents

Your All-in-One AI-Powered Toolkit for Academic Success.

##### +13062052269

##### info@desklib.com

Available 24*7 on WhatsApp / Email

Unlock your academic potential

© 2024 | Zucol Services PVT LTD | All rights reserved.