Business Statistics Analysis of Deliveroo Assignment Sample

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Introduction

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Task 1

Business analysis is a disciplined approach, which allows an organization to include information to undertake changes. This report is going to focus on the Deliveroo Company in the UK to discuss their business condition and identify the current situation of the business strategy. It has to be mentioned here that deliveroo company is an online food delivery company, which provides food to customers since 2013 (Deliveroo.co.uk, 2020). It has been identified that this company focuses on people and values their decision while making decisions and recruiting people to continue the business operation. This report is going to discuss business growth, statistics, and two data sources and compare among them to conclude the current situation and future business trend of Deliveroo Company.

Analysis of business growth statistics of Deliveroo

This section is going to focus on Delivery and identify its strengths, reliability, validity, and business strategy. Those are discussed as follows.

• Deliveroo is focused on their service and delivery time to attract customers and continue their business operation. It has to be mentioned here that this online food delivery company includes British born technology "Frank" in their business operation to predict orders, restaurants, customers and location of restaurants (Deliveroo.co.uk, 2020). It has been identified that this company has reduced its delivery time by 20% by including data-driven technology (Deliveroo.co.uk, 2020).
• Thai companies are working with flexible and effective salary scales with riders, which allow the company to continue their business operations and ensure business growth (Deliveroo.co.uk, 2020). Therefore, by focusing on the above discussion, it can be stated that the strength of this delivery company ensures its profits and growth.

Now this section is going to focus on two data sources and contrast both of them to identify the current situation of the Deliveroo Company in the UK.

Descriptive statistics

The above descriptive model demotes that in context of revenue mean value is 278.7. The value of mean indicates that on average revenue of this company is 278.7 million. Further, the standard deviation is 299.55. The value of standard deviation indicates that 68% of the revenue of these companies belongs within the range of 278.7±299.5. Value of kurtosis is -0.05 and the skewness value are 0.94. It has to be mentioned here that when the value of kurtosis is smaller than 3. The data set is considered as a platykurtic distribution (Mishra et al. 2019). Therefore, the above descriptive model indicates a smaller kurtosis value and it indicates platykurtic distribution. By focusing on above descriptive model indicates that positive skewness value, therefore, it is indicated that positively skewed the dataset.
In terms of profit, value of the mean is 161.833, which indicates that average profit of this company is 162 million. The standard deviation is 131.53, which indicates that 68% profit of this company belongs within the range of 161.83±131.53. Kurtosis value is -1.5 and skewness value is -0.5. In this descriptive model, both kurtosis and skewness value is negative, which indicates that the data set is negatively skewed and platykurtic distribution.

The above descriptive statistics model indicates that in context of mean value is 52.6. The value of mean indicates that on average active users of this company is around 52.6 million. In addition, standard deviation is 24.78. Therefore, 68% of active customers range in this company 52.6±24.78. Value of skewness is 1.00 and kurtosis value is 0.7. These skewness and kurtosis values indicate the platykurtic and positively skewed distribution of the data set. In terms of growth, value of mean is 1144.8. The value of mean indicates that the average growth of Thai delivery companies is 1144.8 million. In addition, standard deviation is 357.7. The standard deviation value indicates that 68% of growth of this delivery company belongs within the range of 1144.8±357.7. In this context, the kurtosis value is -0.3. In this case, kurtosis value fails to cross the value is 3. Therefore, the data set has platykurtic distribution. In the next stage, the skewness value is 0.45. This skewness value is towards the positive direction, which indicates that the data set is positively skewed. Therefore, by focusing on both data source 1 and data source 2, it has been identified that business growth in data source 2 is high for the deliveroo company compared to data source 1.

Graphical presentation

Above figure shows that in the first quarter of 2020 this company had 28% of active users, which has been increased in the second quarter of 2020 by 35%. Further, the above figure indicates that 49% of active users increased in this company. By the end of 2020, this company have around 60% of active users. In addition, by the first quarter of 2021, this company have 91% of act8ive users. Therefore, it can be stated that active users of the company gradually increased from 2020 to 2021.

Above figure indicates the growth of the food delivery company in UK. It has been identified that in the first quarter of 2020, growth of this company was 715 million, however, by the end of 2020 growth of this company was around 1338 million. Therefore, it can be stated that growth of the company significantly increased.

The above figure indicates that in 2015 revenue of this company was around 18 million; however, by the end of 20018 revenue of this company was 476 million. In 2020, the review of this company was 771 million and by the end of 2021 revenue of this company was 1200. Here, it can be stated that revenue of the company significantly increased.

Correlation

Above correlation, the model finds that Pearson-correlation value is 0.73, which is close to +1. In correlation analysis when the Pearson-correlation value is near to positive 1, it shows a positive association (Hardie et al. 2019). Therefore, valuation and revenue have a unidirectional relationship.

Above correlation, model shows that Pearson-correlation value is 0.97, which is near to +1. This correlation model indicates a positive relationship between two variables. Therefore, a positive relationship was identified among active users and the growth of a business.

Conclusion

This report can be concluded that the revenue, growth, and profit of Deliveroo Company is gradually increased by focusing on its business strategy. Purpose of their report was to identify the current situation of the Deliveroo Company. In this regard, this report has been [performed descriptive statistics, correlation analysis, and graphical representation. Therefore, by focusing on graphical presentation, it has been identified that the growth, revenue, and profit of this company has been increased. Further, graphical presentation indicates that the number of active users simultaneously increased. From correlation analysis, it has been identified that a positive relationship between the valuation of a company with its revenue. Further, a unidirectional relationship was identified among active users and the growth of the food delivery company. Therefore, it can be concluded that the profit and revenue of the food delivery company are upwards.

Task 2

Question 1

The correlation model indicates that Pearson-correlation value is 0.81, which is very near to +1. In correlation, when Pearson-correlation value is +1, it indicates a positive association. Therefore, a positive relationship is recognised among plastic curry dishes with revenue of the food delivery company.
Null hypothesis (H0): Number of plastic curry dishes does not affect revenue of the company (p>0.05)
Alternative Hypothesis (H1): Number of plastic curry dishes affect revenue of the company (p<0.05)

In this regression model, dependent variable is the revenue of the food delivery company and an independent variable is a number of plastic curry dishes. R Squared value is 0.65, which indicates that 65% of predicable is possible for predictor variables among independent variables. Further, p-value is 0.000, which indicates that it failed to achieve the level of significance (0.05). In regression when P-value cannot reach a significance level, it chooses an alternative hypothesis (Brook and Arnold, 2020). Therefore, the above regression model indicates that number of curry dishes create a significant effect on revenue of the company.

Above graph indicates that the linear regression slope is identified, which indicates that increasing the number of curry dishes will ensure revenue for the company. The above graphical representation indicates that number of curry dishes is more than 300 that indicates growth of the dishes number that directly relates with revenue. The revenue amount based on this graph indicates more than £8000 as per increase in number of dishes.

Question 2

Normal distribution indicates an equal value from both sides. On other hand, normal distribution can be recognised as when equal mean, medians and modes value. In this case, the company requires around 15000 jackets, where, this company needs to maintain two size ranges, one large, and one small size. In the context of normal distribution, 50% of value should be less than the mean value ad 50% of the value should be above the mean value (Zhang et al. 2019). In this case, the mean value is 3. Therefore, the nominal distribution has been conducted over 15000 jackets.

The above graphical representation indicates size 3 jackets have the highest number of purchase compared to size of other jackets. The purchase rate of jackets of size 3 is 8549 as per mentioned in the graphical representation where as both the size 2 and size 4 jackets have experienced 3081 number of purchases respectively. However, the data from the analyses along with from the above graph indicates lowest purchase rate of size 1 and size 5 jackets that is 144 for both.

Question 3

a) In order to perform nominal distribution this report has used the function BINOM.DIST in excels to identify probability of getting defective jackets. In this case, the warehouse manager has chosen 220 random jackets where the 5% may identify defective jackets (Nazeer et al. 2019). It has to be mentioned here that a maximum of 2% of defective jackets may identify from the 80000 number of total jackets. In this perspective, the above binomial calculation indicates a 17.1% chance to receive five including defective jackets among 220 random samplings.

b) In order to perform nominal distribution, this report has undertaken the BINOM.DIST function in Excel. In the case of 250 random jackets and 10 events of defective jackets, this report identified a 0.9% chance to get 10 defective jackets.

The above table identifies BINOM.DIST formula in Excel to perform non-cumulative normal distribution.

The above-mentioned graphical representation indicates that number of defects for 4 is highest such as 19.73% compared to others such as defects of 3 is 19.63%. However, other defects are comparatively lower that the defect number 4 based on this graph.

Question 4

Poisson distribution indicates how many times a particular event occurs in a specific period (Hassan and Nassr, 2018). By focusing on above Poisson distribution, it has identified a 100% chance to increase number of orders to 220 orders per hour.

Poisson distribution indicates that the average number of successful events may identify a particular time (Hassan and Nassr, 2018). Above figure indicates a 1.56% chance to increase 210 orders per hour.

The above table indicates probability of various order rates by performing a non-cumulative function in an Excel sheet.

The above graphical representation indicates that order per hour for customer of 180 is highest with value of 2.97% compared to others. Therefore, based on this graphical representation, 180 order is the selected and preferable one for customers.

Reference list

Book
Brook, R.J. and Arnold, G.C., 2018. Applied regression analysis and experimental design. CRC Press.

Journals
Hardie, R.C., Rucci, M.A., Bose-Pillai, S. and Van Hook, R., 2021. Application of tilt correlation statistics to anisoplanatic optical turbulence modeling and mitigation. Applied Optics, 60(25), pp.G181-G198.
Hassan, A.S. and Nassr, S.G., 2018. Power Lomax Poisson distribution: properties and estimation. Journal of Data Science, 18(1), pp.105-128.
Mishra, P., Pandey, C.M., Singh, U., Gupta, A., Sahu, C. and Keshri, A., 2019. Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), p.67.
Nazeer, W., Mehmood, Q., Kang, S.M. and Haq, A.U., 2019. An application of Binomial distribution series on certain analytic functions. J. Comput. Anal. Appl, 26(1), pp.11-17.
Zhang, Y., Jin, Z. and Mirjalili, S., 2020. Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models. Energy Conversion and Management, 224, p.113301.

Website
Deliveroo.co.uk, 2020. about-us. Available at: https://deliveroo.co.uk/about-us [Accessed at 14 January 2022].