BSOM084 Practical Data Analysis for Business Assignment Sample
Introduction
Mostly during the coronavirus epidemic, citizens throughout the UK are banded collectively in activities which have an influence on community while also altering commitments. Nearly half, 48 to 50 percent of respondents inside the UK claimed they helped or supported individuals beyond their home during the first months of the shutdown in the year 2020 of April, from preparing an extra portion to purchasing necessities. As a consultant of “Windsor data solution limited”, the researcher analyses the data which is collected via secondary sources and tries to show the visual result of the support of UK people.
Analysis
Dataset: The below dataset is used by the researchers to analyse the human support for the outside of the family members who were old, then sick, relatives, friends who do not live in the same home is included (C. and Abrams, 2021). For each type of feelings or activities which are performed by the public in category of yes or no is analysed by the researchers with the help of SPSS as well as Tableau software’s.
Figure 1: Dataset
Results
Frequency: With the help of frequency analysis researchers produce the histogram output which helps them to visually represent the impact of the feelings factors. In the below table the mean, median, mode and standard deviation is obtained in case of “Yes” of the friends as well as relatives. The lower limit and upper limit both are considered while the analysis is performed.
| Statistics | ||||
| yes_LCL_yong | yes_UCL_yong | answer | ||
| N | Valid | 3 | 3 | 3 |
| Missing | 7 | 7 | 7 | |
| Mean | 32.00 | 35.00 | 1.00 | |
| Median | 29.00 | 32.00 | 1.00 | |
| Mode | 7a | 9a | 0a | |
| Std. Deviation | 26.627 | 27.622 | 1.000 | |
| a. Multiple modes exist. The smallest value is shown | ||||
Table
| yes_LCL_yong | |||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | 7 | 1 | 10.0 | 33.3 | 33.3 |
| 29 | 1 | 10.0 | 33.3 | 66.7 | |
| 60 | 1 | 10.0 | 33.3 | 100.0 | |
| Total | 3 | 30.0 | 100.0 | ||
| Missing | System | 7 | 70.0 | ||
| Total | 10 | 100.0 | |||
|
yes_UCL_yong |
|||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | 9 | 1 | 10.0 | 33.3 | 33.3 |
| 32 | 1 | 10.0 | 33.3 | 66.7 | |
| 64 | 1 | 10.0 | 33.3 | 100.0 | |
| Total | 3 | 30.0 | 100.0 | ||
| Missing | System | 7 | 70.0 | ||
| Total | 10 | 100.0 | |||
|
answer |
|||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | hardly ever | 1 | 10.0 | 33.3 | 33.3 |
| Some of the time | 1 | 10.0 | 33.3 | 66.7 | |
| Often or Always | 1 | 10.0 | 33.3 | 100.0 | |
| Total | 3 | 30.0 | 100.0 | ||
| Missing | System | 7 | 70.0 | ||
| Total | 10 | 100.0 | |||
Figure 2: The visual representation of Feelings
(Source: Self-Created in SPSS)
The above diagram is about the total or actual feelings of hardly ever, sometimes and always or often, where mean is 1, standard deviation is 1.
Figure 3: The visual representation of LCL of Yes for friends
(Source: Self-Created in SPSS)
Above result is the representation of LCL of friends in case of yes and mean is 32, standard deviation is 26.627.
Figure 4: The visual representation of UCL of Yes for friends
(Source: Self-Created in SPSS)
Above result is the representation of UCL of friends as well as relatives that are staying outside of the family, in the case of yes and mean is 35, standard deviation is 27.622.
The below tableau result is obtained to show the visual representation as well as numerical data analysis of the support in case of “No” feelings for the friends and relatives (Teece, et al. 2021). Here also all the required factors like mean, median, etc. calculated by the researchers.
| Statistics | |||||
| answer | No_percent_Yong | No_UCL_yong | No_LCL_yong | ||
| N | Valid | 3 | 3 | 3 | 3 |
| Missing | 7 | 7 | 7 | 7 | |
| Mean | 1.00 | 33.00 | 34.67 | 32.00 | |
| Median | 1.00 | 29.00 | 31.00 | 28.00 | |
| Mode | 0a | 9a | 10a | 8a | |
| Std. Deviation | 1.000 | 26.230 | 26.690 | 26.230 | |
| a. Multiple modes exist. The smallest value is shown | |||||
| answer | |||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | hardly ever | 1 | 10.0 | 33.3 | 33.3 |
| Some of the time | 1 | 10.0 | 33.3 | 66.7 | |
| Often or Always | 1 | 10.0 | 33.3 | 100.0 | |
| Total | 3 | 30.0 | 100.0 | ||
| Missing | System | 7 | 70.0 | ||
| Total | 10 | 100.0 | |||
| No_percent_Yong | |||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | 9 | 1 | 10.0 | 33.3 | 33.3 |
| 29 | 1 | 10.0 | 33.3 | 66.7 | |
| 61 | 1 | 10.0 | 33.3 | 100.0 | |
| Total | 3 | 30.0 | 100.0 | ||
| Missing | System | 7 | 70.0 | ||
| Total | 10 | 100.0 | |||
|
No_UCL_yong |
|||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | 10 | 1 | 10.0 | 33.3 | 33.3 |
| 31 | 1 | 10.0 | 33.3 | 66.7 | |
| 63 | 1 | 10.0 | 33.3 | 100.0 | |
| Total | 3 | 30.0 | 100.0 | ||
| Missing | System | 7 | 70.0 | ||
| Total | 10 | 100.0 | |||
|
No_LCL_yong |
|||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | 8 | 1 | 10.0 | 33.3 | 33.3 |
| 28 | 1 | 10.0 | 33.3 | 66.7 | |
| 60 | 1 | 10.0 | 33.3 | 100.0 | |
| Total | 3 | 30.0 | 100.0 | ||
| Missing | System | 7 | 70.0 | ||
| Total | 10 | 100.0 | |||
Figure 5: The visual representation is % of No for friends
(Source: Self-Created in SPSS)
Above result is the representation of the total percentage of friends as well as relatives who do not lived in the same house, in the case of “No” and mean is 33, standard deviation is 26.23.
Figure 6: The visual representation of UCL of No for friends
(Source: Self-Created in SPSS)
Above result is the representation of UCL of friends in the case of “No” and mean is34.67, standard deviation is 26.69.
Figure 7: The visual representation of LCL of No for friends
(Source: Self-Created in SPSS)
Above result is the representation of LCL of friends as well as relatives that are staying outside of the family, in the case of “No” and mean is 32, standard deviation is 26.23.
Regression
The regression analysis is performed to understand the correlation of the dataset or the feelings and the response of “Yes” or “No”. Here researchers produce the Durbin Watson result which helps to identify the correlation result (Longhurst,et al. 2020). Along with this in the 95 % confidence interval also analysed by them and the result is present in the below.
|
Variables Entered/Removeda |
|||
| Model | Variables Entered | Variables Removed | Method |
| 1 | Yes_percent_old, yes_UCL_oldb | . | Enter |
| a. Dependent Variable: answer | |||
| b. Tolerance = .000 limits reached. | |||
|
Model Summaryb |
|||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Durbin-Watson |
| 1 | 1.000a | 1.000 | . | . | 2.959 |
| a. Predictors: (Constant), Yes_percent_old, yes_UCL_old | |||||
| b. Dependent Variable: answer | |||||
The above table shows that the Durbin Watson result in case of “Yes” for elder and sick person’s is 2.959, it means here negative autocorrelation is present.
| Coefficientsa | |||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | 95.0% Confidence Interval for B | ||||
| B | Std. Error | Beta | Lower Bound | Upper Bound | |||||
| 1 | (Constant) | 2.618 | .000 | . | . | 2.618 | 2.618 | ||
| yes_UCL_old | -.382 | .000 | -10.788 | . | . | -.382 | -.382 | ||
| Yes_percent_old | .353 | .000 | 9.796 | . | . | .353 | .353 | ||
| a. Dependent Variable: answer | |||||||||
| Variables Entered/Removeda | |||
| Model | Variables Entered | Variables Removed | Method |
| 1 | No_percent_old, No_UCL_oldb | . | Enter |
| a. Dependent Variable: answer | |||
| b. Tolerance = .000 limits reached. | |||
| Model Summaryb | |||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Durbin-Watson |
| 1 | 1.000a | 1.000 | . | . | 2.610 |
| a. Predictors: (Constant), No_percent_old, No_UCL_old | |||||
| b. Dependent Variable: answer | |||||
The above result states that in case of “No” response also the negative autocorrelation is available because the result of Durbin Watson is 2.610.
| Coefficientsa | |||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | 95.0% Confidence Interval for B | ||||
| B | Std. Error | Beta | Lower Bound | Upper Bound | |||||
| 1 | (Constant) | 2.555 | .000 | . | . | 2.555 | 2.555 | ||
| No_UCL_old | -.360 | .000 | -9.506 | . | . | -.360 | -.360 | ||
| No_percent_old | .319 | .000 | 8.515 | . | . | .319 | .319 | ||
| a. Dependent Variable: answer | |||||||||
Descriptive
In this project researchers obtain the result of descriptive analysis because the result provides a complete summary of the dataset and helps the analysers to conclude their research work. The result is present in the below.
|
Descriptive Statistics |
|||||||
| N | Minimum | Maximum | Mean | Std. Deviation | Kurtosis | ||
| Statistic | Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | |
| answer | 3 | 0 | 2 | 1.00 | 1.000 | . | . |
| Yes_percent_yong | 3 | 8 | 62 | 33.33 | 27.154 | . | . |
| yes_LCL_yong | 3 | 7 | 60 | 32.00 | 26.627 | . | . |
| yes_UCL_yong | 3 | 9 | 64 | 35.00 | 27.622 | . | . |
| Valid N (listwise) | 3 | ||||||
ANOVA
This analysis is performed because assessment of variances, or ANOVA throughout SPSS, is being performed to investigate changes inside the mean amounts of the dependent measure that are connected with the impact of the controllable independent parameters after controlling for the impact of the unregulated independent factors.
| Descriptives | |||||||||
| answer | |||||||||
| N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | Minimum | Maximum | |||
| Lower Bound | Upper Bound | ||||||||
| 9 | 1 | 2.00 | . | . | . | . | 2 | 2 | |
| 29 | 1 | 1.00 | . | . | . | . | 1 | 1 | |
| 61 | 1 | .00 | . | . | . | . | 0 | 0 | |
| Total | 3 | 1.00 | 1.000 | .577 | -1.48 | 3.48 | 0 | 2 | |
|
ANOVA |
|||||||
| answer | |||||||
| Sum of Squares | df | Mean Square | F | Sig. | |||
| Between Groups | (Combined) | 2.000 | 2 | 1.000 | . | . | |
| Linear Term | Contrast | 1.965 | 1 | 1.965 | . | ||
| Deviation | .035 | 1 | .035 | . | |||
| Within Groups | .000 | 0 | . | ||||
| Total | 2.000 | 2 | |||||
Two step cluster
This analysis helps the researchers to understand the quality of the analysis. The result of this particular dataset is present in the below.
Figure 8: The visual representation of Two step Clustering
(Source: Self-Created in SPSS)
T test
This analysis helps researchers to obtain the statistical analysis between two means and the result is shown in the below.
| One-Sample Statistics | ||||
| N | Mean | Std. Deviation | Std. Error Mean | |
| answer | 3 | 1.00 | 1.000 | .577 |
| Yes_percent_old | 3 | 33.3333 | 27.75488 | 16.02429 |
| yes_LCL_old | 3 | 31.3333 | 27.75488 | 16.02429 |
| yes_UCL_old | 3 | 35.0000 | 28.21347 | 16.28906 |
| One-Sample Test | ||||||
| Test Value = 0 | ||||||
| t | df | Sig. (2-tailed) | Mean Difference | 95% Confidence Interval of the Difference | ||
| Lower | Upper | |||||
| answer | 1.732 | 2 | .225 | 1.000 | -1.48 | 3.48 |
| Yes_percent_old | 2.080 | 2 | .173 | 33.33333 | -35.6136 | 102.2803 |
| yes_LCL_old | 1.955 | 2 | .190 | 31.33333 | -37.6136 | 100.2803 |
| yes_UCL_old | 2.149 | 2 | .165 | 35.00000 | -35.0861 | 105.0861 |
Means
The means analysis is performed to show the product result of “ Yes” and the result is present in the below.
| Case Processing Summary | ||||||
| Cases | ||||||
| Included | Excluded | Total | ||||
| N | Percent | N | Percent | N | Percent | |
| Yes_percent_yong * yes_LCL_yong | 3 | 30.0% | 7 | 70.0% | 10 | 100.0% |
| Yes_percent_yong * yes_UCL_yong | 3 | 30.0% | 7 | 70.0% | 10 | 100.0% |
Yes_percent_yong * yes_LCL_yong
| Report | |||||||
| Yes_percent_yong | |||||||
| yes_LCL_yong | Mean | Std. Deviation | Std. Error of Mean | Minimum | Maximum | Variance | Median |
| 7 | 8.00 | . | . | 8 | 8 | . | 8.00 |
| 29 | 30.00 | . | . | 30 | 30 | . | 30.00 |
| 60 | 62.00 | . | . | 62 | 62 | . | 62.00 |
| Total | 33.33 | 27.154 | 15.677 | 8 | 62 | 737.333 | 30.00 |
Yes_percent_yong * yes_UCL_yong
| Report | |||||||
| Yes_percent_yong | |||||||
| yes_UCL_yong | Mean | Std. Deviation | Std. Error of Mean | Minimum | Maximum | Variance | Median |
| 9 | 8.00 | . | . | 8 | 8 | . | 8.00 |
| 32 | 30.00 | . | . | 30 | 30 | . | 30.00 |
| 64 | 62.00 | . | . | 62 | 62 | . | 62.00 |
| Total | 33.33 | 27.154 | 15.677 | 8 | 62 | 737.333 | 30.00 |
Figure 9: The result of Lower Control Limit Vs Feelings for elders and sick
(Source: Self-Created in Tableau)
The result obtained where the researchers consider only the LCL in case of “No” response and the feelings.
Figure 10: The result of Yes Vs Feelings for elders and sick
(Source: Self-Created in Tableau)
The above result is produced to show the each feelings effect in case of “Yes” response for the elders.
Figure 11: The result of No Vs Feelings for friends and relatives
(Source: Self-Created in Tableau)
The above result clearly specifies the impact of the “No” responses to the support of friends.
Figure 12: The result of No and Yes Vs Feelings for elders and sick with friends
(Source: Self-Created in Tableau)
Figure 13: The result of No Vs Feelings for elders and sick
(Source: Self-Created in Tableau)
Conclusion
The complete analysis is performed based on the human’s support for the outsiders and the impact of the pandemic on the UK people. In this section researchers concluded the analysis where they successfully reach the aim of the researchers which is to visually produce each responses in case of “yes” as well as “no”.
Reference List
Journal
Teece, L., Gray, L.J., Melbourne, C., Orton, C., Ford, D.V., Martin, C.A., McAllister, D., Khunti, K., Tobin, M., John, C. and Abrams, K.R., 2021. United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers (UK-REACH): a retrospective cohort study using linked routinely collected data, study protocol. BMJ open, 11(6), p.e046392.
Teece, L., Gray, L.J., Melbourne, C., Orton, C., Ford, D.V., Martin, C.A., McAllister, D., Khunti, K., Tobin, M., John, C. and Abrams, K.R., 2021. Protocol: United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers (UK-REACH): a retrospective cohort study using linked routinely collected data, study protocol. BMJ Open, 11(6).
Longhurst, G.J., Stone, D.M., Dulohery, K., Scully, D., Campbell, T. and Smith, C.F., 2020. Strength, weakness, opportunity, threat (SWOT) analysis of the adaptations to anatomical education in the United Kingdom and Republic of Ireland in response to the Covid‐19 pandemic. Anatomical sciences education, 13(3), pp.301-311.
Costantini, C., Joyce, A. and Britez, Y., 2021. Breastfeeding Experiences During the COVID-19 Lockdown in the United Kingdom: An Exploratory Study Into Maternal Opinions and Emotional States. Journal of Human Lactation, 37(4), pp.649-662.
Schneiders, M.L., Mackworth-Young, C.R. and Cheah, P.Y., 2022. Between division and connection: a qualitative study of the impact of COVID-19 restrictions on social relationships in the United Kingdom. Wellcome Open Research, 7(6), p.6.
Ratschen, E., Shoesmith, E., Shahab, L., Silva, K., Kale, D., Toner, P., Reeve, C. and Mills, D.S., 2020. Human-animal relationships and interactions during the Covid-19 lockdown phase in the UK: Investigating links with mental health and loneliness. PloS one, 15(9), p.e0239397.
Huang, J., Wang, H., Fan, M., Zhuo, A., Sun, Y. and Li, Y., 2020, August. Understanding the impact of the COVID-19 pandemic on transportation-related behaviors with human mobility data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3443-3450).
Williams, J.M., Randle, H. and Marlin, D., 2020. COVID-19: Impact on United Kingdom horse owners. Animals, 10(10), p.1862.
Qian, Y. and Hu, Y., 2021. Couples’ changing work patterns in the United Kingdom and the United States during the COVID‐19 pandemic. Gender, Work & Organization, 28, pp.535-553.
Nguyen, L.H., Joshi, A.D., Drew, D.A., Merino, J., Ma, W., Lo, C.H., Kwon, S., Wang, K., Graham, M.S., Polidori, L. and Menni, C., 2022. Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom. Nature communications, 13(1), pp.1-9.
Nibali, L., Ide, M., Ng, D., Buontempo, Z., Clayton, Y. and Asimakopoulou, K., 2020. The perceived impact of Covid-19 on periodontal practice in the United Kingdom: A questionnaire study. Journal of dentistry, 102, p.103481.
Bu, F., Steptoe, A. and Fancourt, D., 2020. Loneliness during a strict lockdown: Trajectories and predictors during the COVID-19 pandemic in 38,217 United Kingdom adults. Social Science & Medicine, 265, p.113521.
………………………………………………………………………………………………………………………..
Know more about UniqueSubmission’s other writing services:
