With increasing market competition and the arrival of technological innovation, the automobile-manufacturing industry faces great changes. In this situation, the quality of internal control becomes an important part that affects the growth and development of enterprises. According to the theories of internal control and enterprise growth, this paper selects data of Chinese listed-companies in the automobile manufacturing industry to analyse the relationship between the internal control index and enterprise growth. Further, it verifies the research conclusion using multiple regression analysis that the improvement of internal control quality can play a positive role in promoting the growth of automobile manufacturing enterprises.

#### Keywords

- internal control
- enterprise growth
- automobile manufacturing enterprises

According to the information released by the Traffic Management Bureau of the Ministry of Public Security in early 2019, national car ownership had reached 240 million, and in 61 cities across the country, car ownership had exceeded 1 million by the end of 2018. In addition, the data from the Chinese Automobile Industry Association show that the automobile industry in China has operated steadily in recent years, and its production and sales volume has ranked first in the world for 10 consecutive years. In 2018, the automobile production and sales volume reached 27.809 million and 28.081 million, respectively. Meanwhile, the total retail sales of automobile consumer goods in China in 2018 was 3894.8 billion yuan, accounting for 10.22% of the total retail sales of social consumer goods. Based on the above data, we can fully see that the automobile industry plays an important role in China's national economy and the lives of its residents.

As far as Chinese automobile manufacturing enterprises are concerned, the growth and development of enterprises are accompanied by many factors, but it is undeniable fact that internal control, as the original driving force of enterprise development, plays a relatively important role during the current period. This paper selects the data of automobile manufacturing enterprises listed in China as the research sample and uses empirical research method to examine the relationship between the internal control index and the growth of automobile manufacturing enterprises. In the process of growth and development, this paper discusses whether automobile manufacturing enterprises can effectively measure the growth of enterprises through an internal control index by analysing the results of the empirical analysis; in other words, whether internal control plays a positive role in studying and predicting the development trend of the industry, which carries certain theoretical and practical significance.

In 1992, the Committee of Sponsoring Organization of the Tread-way Commission, which refers to COSO, was established. In the same year, COSO released the internal control integrated framework, a programmatic document on internal control, which mainly summarised the main concepts of internal control and formed a basic internal control framework, namely, the control environment, risk assessment, control activities, information and communication supervision which consists of five parts.

Koutoupis and Malisiovas [1] investigated whether and how the components of internal control systems affect the US banking sector based on COSO. Khersiat [2] promoted the adoption of internal control components based on the COSO framework and found that internal audits based on the COSO framework had a high positive impact on performing tasks transparently. Using the analytic hierarchy process, Hanwen et al. [3] constructed a comprehensive model based on the COSO framework. Taking the five elements of internal control as the logical framework, Guohua et al. [4] constructed a set of scientific and operable internal control quality evaluation index systems of listed companies based on theoretical analysis from the perspective of the ‘integration view’. Studying the root causes of corporate governance, internal control and risk management, Weian et al. [5] deeply analysed the differences and relations among the three components and constructed their relationship framework from the perspective of strategic management. Froese [6] examines the relationship between internal control and earnings management in Japanese listed firms. The Dibo internal control index is a comprehensive evaluation model established from the perspective of risk control. All the above methods can carry out internal control quality evaluation for companies in different stages and adopt different methods for different stages.

According to classical economic theories, the emergence of enterprises is the result of the division of labour and the growth of enterprises is also positively correlated with the division of labour. The growth process of larger enterprises replacing small enterprises is the inevitable effect of economies of scale. On the issue of enterprise growth, the theories of neoclassical economics not only look at enterprise growth from the perspective of the internal division of labour and the corresponding scale effect but also analyse enterprise growth from the perspective of enterprise growth motivation and enterprise performance. The growth of enterprises is actually the process of expanding enterprise boundaries.

Anand [7] first linked related concepts with enterprise growth and found that the benefits of horizontal expansion M&A in a recession are far better than those of mixed M&A. Foss et al. [8] offered four types of theories of growth to predict the corporate growth. By studying the relationship between the enterprise life cycle and M&A intention and the M&A mode, Sian Owen et al. [9] pointed out that there is a certain correlation between the enterprise life cycle and the M&A intention, M&A payment mode and M&A mode. Shubin et al. [10] analysed the exit risk in the market due to the scale growth of enterprises in the start-up period due to the reduction of trust. Ninghua et al. [11] proposed that the growth and development of enterprises are mainly accompanied by expansion activities in the product market and in the various geographical locations. Min et al. [12], studied the impact of the effectiveness of internal control on growth by studying 498 GEM listed companies in Shenzhen with empirical methods as the research object and found that the effectiveness of internal control has a positive role in promoting their growth, and there are differences between the effectiveness of internal control and the positive role in promoting growth among different industries.

The role of internal control is to ensure the long-term and stable development of relevant automobile manufacturing enterprises. An enterprise must be more careful to consider and plan establishing and improving internal control, particularly after it has reached a certain scale in its development journey. The impact of internal controls on enterprises is essentially reflected in the strategic layout and long-term planning of enterprises. With the rapid development of the automobile manufacturing industry in China over the years, many advanced foreign production technologies have been introduced and the overall industrial scale has also surged. However, it is undeniable that while the industry is developing rapidly, many internal hidden dangers are also accumulating in various spheres. For example, the internal management of automobile manufacturing enterprises relies more on experience of management and lacks long-term strategic considerations. For example, production technology tends to be obtained through joint ventures, mergers and acquisitions, but the overall technical level is lacking and the industrial technological innovation ability is not solid. For example, in the process of operation and production, enterprise risk assessment is insufficient and the scientific nature of financial management needs to be strengthened. All of the above are caused by defects in the internal control.

In summary, based on the correlation analysis of the previous theoretical basis, this paper focuses on the correlation between the internal control index and the growth of automobile manufacturing enterprises and puts forward the following research assumptions:

The relevant quality of internal control as variable ICQ is set. Referring to the relevant research conclusions of existing studies, the internal control quality of a company usually selects the type of internal control audit opinion in the audit report, whether the enterprise actively discloses internal control defects, etc., as a relatively simple and direct way to evaluate the internal control quality of the enterprise. Considering the necessary requirements for data accuracy in model analysis, as well as the scientific, reliability and acquisition of relevant data, the internal control quality index in the Dibo internal control database is adopted as the internal control index of this paper.

When the comprehensive growth of an automobile manufacturing enterprise is set as growth, the explained variable mainly reflects the growth of the enterprise. A more common and simple way is to reflect the relevant situation by considering mainly the growth of the enterprise's total assets and profitability. In this paper, the method of factor analysis is used to describe the relationship between relevant indicators and several main factors, and the comprehensive growth index growth is used as the index to evaluate the growth of enterprises.

This paper holds that the reasonableness and accuracy of judging whether the growth of automobile manufacturing enterprises is good or not is an important prepositive problem for carrying out relevant research. Whether the growth is good or not depends not only on the change of asset scale but also on other benefits brought by growth to see whether it has corresponding profits and losses on the overall value of the enterprise. Therefore, this paper mainly studies the comprehensive growth of relevant enterprises from four aspects, namely, the solvency, operation ability, profitability and development ability of automobile manufacturing enterprises; the paper also selects indicators and adopts the method of factor analysis, as shown in Table 1.

Description of comprehensive growth indicators

Solvency | Current ratio | Current assets/Current liabilities |

Quick ratio | (Current assets − Inventory)/Current liabilities | |

Cash ratio | Cash and cash equivalents closing balance/Current liabilities | |

Asset liability ratio | Total liabilities/Total assets | |

Operational Capability | Inventory turnover | Operating cost/Average inventory occupancy |

Turnover rate of current assets | Operating income/Usual occupation of current assets | |

Turnover rate of fixed assets | Operating income/Average net fixed assets | |

Turnover rate of total assets | Operating income/Total assets at ordinary times | |

Profitability | Net profit margin of total assets | Net profit/Average balance of total assets |

Return on net assets | Net profit/Average balance of shareholders’ equity | |

Net operating interest rate | Net profit/Operating income | |

Operating profit per share | Current value of operating profit/Paid in capital at the end of the current period | |

Development Capacity | Growth rate of total assets | (Ending value of total assets in the current period – Ending value of total assets in the same period of last year)/Ending value of total assets in the same period of last year |

Operating profit growth rate | (Current amount of operating profit – Amount of operating profit in the same period of last year)/Amount of operating profit in the same period of last year | |

Growth rate of operating revenue | (Current amount of operating revenue – Amount of operating revenue in the same period of last year)/Amount of operating revenue in the same period of last year | |

Growth rate of owner's equity | (Closing value of owner's equity in the current period – Closing value of owner's equity in the same period of last year) Closing value of owner's equity in the same period of last year |

The growth is related to various factors in the growth process of Chinese automobile manufacturing enterprises. Combined with the relevant conclusions of the existing research, this paper selects three control variables related to the growth of the enterprise, namely, the P/E ratio, company size and employee salary growth rate.

P/E ratio = Price Per Share/Earnings Per Share

Size = Total assets

Salary = Staff salaries growth rate

The multiple regression model constructed in this paper is as follows:

Growth is the growth index of automobile manufacturing enterprises, which mainly includes the comprehensive situation of 16 indicators in four aspects: solvency, operation ability, profitability and development ability. ICQ is the internal control index of automobile manufacturing enterprises, which mainly reflects the internal control quality of enterprises. PE is the P/E ratio of automobile manufacturing enterprises, which mainly reflects the profitability of enterprises. Size is the total assets of automobile manufacturing enterprises, which mainly reflects the size of the enterprise. Salary is the salary growth rate payable by automobile manufacturing enterprises, which mainly reflects the salary growth rate of employees to be paid by enterprises.

During the development period of China's automobile manufacturing industry, a time node that cannot be ignored is the 2008 financial crisis. After that, the automobile industry has ushered in rapid development. Therefore, to carefully analyse the relationship between the enterprise internal control index and the growth of automobile manufacturing enterprises, this paper selects 32 relatively representative automobile industry enterprises listed in China from 2008 to 2018. A total of 352 groups of sample data met the requirements.

The internal control indices used in this paper are from the Dibo internal control database and other relevant data are from the CSMAR database.

The Kaiser–Meyer–Olkin (KMO) test is used to check the partial correlation between variable indicators. According to Table 2, the test value of KMO is 0.621, the approximate chi squared value of the Bartlett sphericity test is 6569.612 and the value of SIG is 0.000. The above results show that the samples and index data selected passed the KMO and Bartlett's test and hence can be used for factor analysis.

KMO and Bartlett's test

KMO measure of sampling adequacy | 0.621 | |

Bartlett’ test of sphericity | Approx. chi-squared | 6569.612 |

df |
120 |

KMO, Kaiser–Meyer–Olkin

Using the factor analysis method and SPSS 24.0 software, this paper evaluates the comprehensive growth reflected in 352 sample data and analyses the eigenvalues, contribution rate and cumulative contribution rate of each factor, as well as the interpretation of total variance.

It can be seen from Table 3 that when extracting the main factor by considering only the factors with eigenvalues >1, the initial eigenvalues of Factors G1, G2, G3 and G4 are >1. The contribution rate of Factor G1 is 28.142%, G2 is 21.901%, G3 is 18.443% and G4 is 7.551%, and the cumulative contribution rate of the four factors is 76.038%. These four factors can evaluate the comprehensive growth of enterprises to a certain extent.

Total variance table of factors

1 | 4.503 | 28.142 | 28.142 | 4.503 | 28.142 | 28.142 | 3.479 | 21.745 | 21.745 |

2 | 3.504 | 21.901 | 50.043 | 3.504 | 21.901 | 50.043 | 3.051 | 19.070 | 40.815 |

3 | 2.951 | 18.443 | 68.487 | 2.951 | 18.443 | 68.487 | 2.998 | 18.740 | 59.554 |

4 | 1.208 | 7.551 | 76.038 | 1.208 | 7.551 | 76.038 | 2.637 | 16.483 | 76.038 |

5 | 0.939 | 5.868 | 81.906 | ||||||

6 | 0.867 | 5.419 | 87.325 | ||||||

7 | 0.640 | 3.997 | 91.322 | ||||||

8 | 0.438 | 2.740 | 94.063 | ||||||

9 | 0.386 | 2.414 | 96.476 | ||||||

10 | 0.205 | 1.284 | 97.760 | ||||||

11 | 0.182 | 1.074 | 98.834 | ||||||

12 | 0.074 | 0.462 | 99.296 | ||||||

13 | 0.057 | 0.354 | 99.651 | ||||||

14 | 0.046 | 0.291 | 99.941 | ||||||

15 | 0.007 | 0.045 | 99.986 | ||||||

16 | 0.002 | 0.014 | 100.000 |

Then, after analysis of the component score coefficient matrix (Table 4), we can express the key influencing factors as follows:

G1 = 0.334 × 1 + 0.348 × 2 + 0.314 × 3 − 0.086 × 4 + 0.093 × 5 − 0.007 × 6 + 0.138 × 7 + 0.027 × 8 − 0.040×9 − 0.071 × 10 + 0.053 × 11 + 0.006 × 12 + 0.012 × 13 − 0.077 × 14 + 0.011 × 15 + 0.007 × 16

G2 = 0.119 × 1 + 0.141 × 2 + 0.120 × 3 + 0.170 × 4 + 0.288 × 5 + 0.233 × 6 + 0.334 × 7 + 0.299 × 8 − 0.057 × 9 + 0.004 × 10 − 0.155 × 11 + 0.097 × 12 + 0.004 × 13 − 0.117 × 14 + 0.001 × 15 − 0.005 × 16

G3 = 0.008 × 1 + 0.005 × 2 + 0.000 × 3 + 0.000 × 4 − 0.028 × 5 − 0.010 × 6 + 0.021 × 7 − 0.006 × 8 − 0.015 ×9 − 0.010 × 10 + 002 × 11 − 0.022 × 12 + 0.332 × 13 + 0.092 × 14 + 0.329 × 15 + 0.326 × 16

G4 = −0.122 × 1 − 0.131 × 2 − 0.084 × 3 − 0.151 × 4 − 0.087 × 5 − 0.034 × 6 − 0.099 × 7 − 0.044 × 8 + 0.377 × 9 + 0.355 × 10 + 0.264 × 11 + 0.225 × 12 − 0.016 × 13 + 0.179 × 14 − 0.020 × 15 + 0.008 × 16

Component score coefficient matrix

Current ratio | 0.334 | 0.119 | 0.008 | −0.122 |

Quick ratio | 0.348 | 0.141 | 0.005 | −0.131 |

Cash ratio | 0.314 | 0.120 | 0.000 | −0.084 |

Asset liability ratio | −0.086 | 0.170 | 0.000 | −0.151 |

Inventory turnover | 0.093 | 0.288 | −0.028 | −0.087 |

Turnover rate of current assets | −0.007 | 0.233 | −0.010 | −0.034 |

Turnover rate of fixed assets | 0.138 | 0.334 | 0.021 | −0.099 |

Turnover rate of total assets | 0.027 | 0.299 | −0.006 | −0.044 |

Net profit margin of total assets | −0.040 | −0.057 | −0.015 | 0.377 |

Return on net assets | −0.071 | 0.004 | −0.010 | 0.335 |

Net operating interest rate | 0.053 | −0.155 | 0.002 | 0.264 |

Operating profit per share | 0.006 | 0.097 | −0.022 | 0.225 |

Growth rate of total assets | 0.012 | 0.004 | 0.332 | −0.016 |

Operating profit growth rate | −0.077 | −0.117 | 0.092 | 0.179 |

Growth rate of operating revenue | 0.011 | 0.001 | 0.329 | −0.020 |

Growth rate of owner's equity | 0.007 | −0.005 | 0.326 | 0.008 |

Descriptive statistics

ICQ | 352 | 689.832 | 90.226 | 960.340 | 192.111 |

PE | 352 | 79.406 | 168.848 | 1508.165 | 4.128 |

Size | 352 | 2.622e^{10} |
8.163e^{10} |
7.832e^{11} |
5.011e^{8} |

Salary | 352 | 3.552 | 62.197 | 1167.081 | −0.905 |

Growth | 352 | 1.747 | 1.759 | 30.251 | −5.829 |

From Table 3, in the sum of squares of the rotating load, we can say that the contribution rates of G1 G2 G3 and G4 are 21.745%, 19.070, 18.740% and 16.483%, respectively, and the total contribution rate of the four factors is 76.038%. Therefore, the comprehensive growth index equation of automobile manufacturing enterprises is:

Stata15.0 is used for the descriptive statistics of the data, as follows:

As shown in Table 6, according to the results of Pearson correlation analysis, all variables are statistically significant and there is less possibility of multicollinearity problems. The enterprise internal control index, scale and the growth rate of employee compensation payable. When the comprehensive growth index of the enterprise is >0, it indicates that it is positively correlated with the growth of the enterprise. It shows that with the internal control quality, asset scale and the growth of employee compensation payable of automobile manufacturing enterprises, the growth of the enterprise will be better. When the comprehensive growth index of enterprise P/E ratio is <0, it shows that it is negatively correlated with the growth of enterprises, indicating that when the P/E ratio of automobile manufacturing enterprises decreases, the growth of enterprises increases, which is also consistent with the fact that the lower the P/E ratio represents the stronger profitability of enterprises.

Correlation analysis

Growth | 1.000 | ||||

ICQ | 0.261*** | 1.000 | |||

PE | −0.151*** | −0.325*** | 1.000 | ||

Size | 0.238*** | 0.310*** | −0.102* | 1.000 | |

Salary | 0.867*** | 0.049 | −0.015 | 0.032 | 1.000 |

According to the regression analysis in Table 7, the estimated explanatory variable coefficients in the table are 0.003, −0.001, 3.40e−12 and 0.024. For the internal control index sample, the

Regression analysis

(Constant) | −0.329 | 0.3381 | −0.97 | 0.331 |

ICQ | 0.003 | 0.0005 | 5.89 | 0.000 |

PE | −0.001 | 0.0002 | −3.14 | 0.002 |

Size | 3.40e^{−12} |
5.07e^{−13} |
6.70 | 0.000 |

Salary | 0.024 | 0.0006 | 38.12 | 0.000 |

Sample: 352 | Adjusted ^{2}: 0.825 |

This paper selects 32 domestic listed automobile manufacturing enterprises with continuous data of the period 2008 to 2018 to study the relationship between the internal control index and enterprise growth. During the whole research process, based on reading relevant literature at home and abroad and considering the actual situation of China's automobile manufacturing enterprises in terms of internal control, this paper adopts the Dibo internal control index of Chinese listed companies as the research data in terms of the internal control index in addition to relevant data of the CSMAR database in terms of enterprise growth; hence, considering the solvency, operation ability, profitability and development ability of automobile manufacturing enterprises. The selected indicators are studied by factor analysis.

During the process of concrete empirical analysis of the internal control index and enterprise growth of China's automobile manufacturing enterprises, the corresponding model is established by using the method of multiple regression analysis to analyse the relationship between the comprehensive growth of enterprises and the internal control index, with P/E ratio, total assets of enterprises and the growth rate of employee compensation payable as variables. Through the analysis of the relevant empirical results of automobile manufacturing enterprises, it can be shown that the internal control index, P/E ratio, company size and the growth rate of employee compensation have a related impact on the growth of relevant enterprises to some extent.

Considering the development/situation of China's automobile manufacturing industry, the introduction of relevant policies of various departments has set the main tone for the development of the industry, with alternative energy being the general current trend. However, in terms of industrial support, there is still a lack of relevant more flexible and sustainable policies. For example, the alternative energy vehicle purchase subsidy policy has played a positive role in activating the alternative energy vehicle market but it has also brought problems such as enterprises cheating on subsidies. Therefore, it is necessary for government departments to issue supporting policies for the future development stages of alternative energy vehicles and to promote the overall development progress of the industry from all angles to providing a relatively fair development environment.

To further encourage the China's automobile manufacturing industries to pay close attention to the development direction of alternative energy vehicles and ensure the stable development of relevant industries, in addition to putting forward high requirements for relevant enterprises, government departments should do a good job in market supervision. By issuing rules, regulations and legal requirements, we can eliminate local protectionism and implement effective market supervision through the establishment of a law enforcement inspection team. Through government performance audits and standardising the operation of power departments, we can provide a good external environment for the development of the alternative energy vehicle industry and push China's automobile manufacturing industry to step to a new level. Of course, in this process, in addition to the efforts of government departments, we should also set up corresponding platforms to enable the mass media to participate, and actively enhance the social supervision force to promote the healthy development of the industry.

Due to the existence of principal-agent relationships in the automobile manufacturing enterprises, the objectives expected by the owners and managers of the enterprises are often inconsistent. Many large-scale vehicle manufacturing enterprises belong to the category of state-owned holding or joint ventures, which may lead the management of such enterprises to consider self-serving when carrying out operations. To obtain certain enterprise control or remuneration, they may practice favouritism and fraud within the enterprise, which constrains further growth and development of the enterprise. To avoid this situation, enterprises need to further improve their own internal control mechanism based on relevant national regulations. They need to effectively improve and refine the internal control system from all aspects and departments of the enterprise to be meticulous and comprehensive, to cover all management behaviours of the enterprise, and to put in system checks and balances for the management and operation of the enterprise. At the same time, everyone in the enterprise must realise the importance of internal control implementation to the operation and development of the enterprise and all personnel must be guided to participate and cooperate towards the long-term growth of the enterprise.

#### KMO and Bartlett's test

KMO measure of sampling adequacy | 0.621 | |

Bartlett’ test of sphericity | Approx. chi-squared | 6569.612 |

df |
120 |

#### Correlation analysis

Growth | 1.000 | ||||

ICQ | 0.261*** | 1.000 | |||

PE | −0.151*** | −0.325*** | 1.000 | ||

Size | 0.238*** | 0.310*** | −0.102* | 1.000 | |

Salary | 0.867*** | 0.049 | −0.015 | 0.032 | 1.000 |

#### Description of comprehensive growth indicators

Solvency | Current ratio | Current assets/Current liabilities |

Quick ratio | (Current assets − Inventory)/Current liabilities | |

Cash ratio | Cash and cash equivalents closing balance/Current liabilities | |

Asset liability ratio | Total liabilities/Total assets | |

Operational Capability | Inventory turnover | Operating cost/Average inventory occupancy |

Turnover rate of current assets | Operating income/Usual occupation of current assets | |

Turnover rate of fixed assets | Operating income/Average net fixed assets | |

Turnover rate of total assets | Operating income/Total assets at ordinary times | |

Profitability | Net profit margin of total assets | Net profit/Average balance of total assets |

Return on net assets | Net profit/Average balance of shareholders’ equity | |

Net operating interest rate | Net profit/Operating income | |

Operating profit per share | Current value of operating profit/Paid in capital at the end of the current period | |

Development Capacity | Growth rate of total assets | (Ending value of total assets in the current period – Ending value of total assets in the same period of last year)/Ending value of total assets in the same period of last year |

Operating profit growth rate | (Current amount of operating profit – Amount of operating profit in the same period of last year)/Amount of operating profit in the same period of last year | |

Growth rate of operating revenue | (Current amount of operating revenue – Amount of operating revenue in the same period of last year)/Amount of operating revenue in the same period of last year | |

Growth rate of owner's equity | (Closing value of owner's equity in the current period – Closing value of owner's equity in the same period of last year) Closing value of owner's equity in the same period of last year |

#### Component score coefficient matrix

Current ratio | 0.334 | 0.119 | 0.008 | −0.122 |

Quick ratio | 0.348 | 0.141 | 0.005 | −0.131 |

Cash ratio | 0.314 | 0.120 | 0.000 | −0.084 |

Asset liability ratio | −0.086 | 0.170 | 0.000 | −0.151 |

Inventory turnover | 0.093 | 0.288 | −0.028 | −0.087 |

Turnover rate of current assets | −0.007 | 0.233 | −0.010 | −0.034 |

Turnover rate of fixed assets | 0.138 | 0.334 | 0.021 | −0.099 |

Turnover rate of total assets | 0.027 | 0.299 | −0.006 | −0.044 |

Net profit margin of total assets | −0.040 | −0.057 | −0.015 | 0.377 |

Return on net assets | −0.071 | 0.004 | −0.010 | 0.335 |

Net operating interest rate | 0.053 | −0.155 | 0.002 | 0.264 |

Operating profit per share | 0.006 | 0.097 | −0.022 | 0.225 |

Growth rate of total assets | 0.012 | 0.004 | 0.332 | −0.016 |

Operating profit growth rate | −0.077 | −0.117 | 0.092 | 0.179 |

Growth rate of operating revenue | 0.011 | 0.001 | 0.329 | −0.020 |

Growth rate of owner's equity | 0.007 | −0.005 | 0.326 | 0.008 |

#### Regression analysis

(Constant) | −0.329 | 0.3381 | −0.97 | 0.331 |

ICQ | 0.003 | 0.0005 | 5.89 | 0.000 |

PE | −0.001 | 0.0002 | −3.14 | 0.002 |

Size | 3.40e^{−12} |
5.07e^{−13} |
6.70 | 0.000 |

Salary | 0.024 | 0.0006 | 38.12 | 0.000 |

Sample: 352 | Adjusted ^{2}: 0.825 |

#### Descriptive statistics

ICQ | 352 | 689.832 | 90.226 | 960.340 | 192.111 |

PE | 352 | 79.406 | 168.848 | 1508.165 | 4.128 |

Size | 352 | 2.622e^{10} |
8.163e^{10} |
7.832e^{11} |
5.011e^{8} |

Salary | 352 | 3.552 | 62.197 | 1167.081 | −0.905 |

Growth | 352 | 1.747 | 1.759 | 30.251 | −5.829 |

#### Total variance table of factors

1 | 4.503 | 28.142 | 28.142 | 4.503 | 28.142 | 28.142 | 3.479 | 21.745 | 21.745 |

2 | 3.504 | 21.901 | 50.043 | 3.504 | 21.901 | 50.043 | 3.051 | 19.070 | 40.815 |

3 | 2.951 | 18.443 | 68.487 | 2.951 | 18.443 | 68.487 | 2.998 | 18.740 | 59.554 |

4 | 1.208 | 7.551 | 76.038 | 1.208 | 7.551 | 76.038 | 2.637 | 16.483 | 76.038 |

5 | 0.939 | 5.868 | 81.906 | ||||||

6 | 0.867 | 5.419 | 87.325 | ||||||

7 | 0.640 | 3.997 | 91.322 | ||||||

8 | 0.438 | 2.740 | 94.063 | ||||||

9 | 0.386 | 2.414 | 96.476 | ||||||

10 | 0.205 | 1.284 | 97.760 | ||||||

11 | 0.182 | 1.074 | 98.834 | ||||||

12 | 0.074 | 0.462 | 99.296 | ||||||

13 | 0.057 | 0.354 | 99.651 | ||||||

14 | 0.046 | 0.291 | 99.941 | ||||||

15 | 0.007 | 0.045 | 99.986 | ||||||

16 | 0.002 | 0.014 | 100.000 |

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model in the development of Wushu market Study on audio-visual family restoration of children with mental disorders based on the mathematical model of fuzzy comprehensive evaluation of differential equation Difference-in-differences test for micro effect of technological finance cooperation pilot in China Application of multi-attribute decision-making methods based on normal random variables in supply chain risk management Exploration on the collaborative relationship between government, industry, and university from the perspective of collaborative innovation The impact of financial repression on manufacturing upgrade based on fractional Fourier transform and probability AtanK-A New SVM Kernel for Classification Validity and reliability analysis of the Chinese version of planned happenstance career inventory based on mathematical statistics Visual positioning system for marine industrial robot assembly based on complex variable function Mechanical behaviour of continuous girder bridge 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based on motion functionalisation The Control Relationship Between the Enterprise's Electrical Equipment and Mechanical Equipment Based on Graph Theory Financial Accounting Measurement Model Based on Numerical Analysis of Rigid Normal Differential Equation and Rigid Functional Equation Mathematical Modeling and Forecasting of Economic Variables Based on Linear Regression Statistics Nonlinear Differential Equations in Cross-border E-commerce Controlling Return Rate Differential equation model of financial market stability based on Internet big data 3D Mathematical Modeling Technology in Visualized Aerobics Dance Rehearsal System Children’s cognitive function and mental health based on finite element nonlinear mathematical model Fractional Differential Equations in Electronic Information Models BIM Engineering Management Oriented to Curve Equation Model Mathematical Method to Construct the Linear Programming of Football Training The Size of Children's Strollers of Different Ages Based on Ergonomic Mathematics Design Stiffness Calculation of Gear Hydraulic System Based on the Modeling of Nonlinear Dynamics Differential Equations in the Progressive Method Relationship Between Enterprise Talent Management and Performance Based on the Structural Equation Model Method Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Video adaptive watermark embedding and detection algorithm based on phase function equation Evaluation of ecosystem health in Futian mangrove wetland based on the PSR-AHP model Towards more efficient control of the ironmaking blast furnace: modelling gaseous reduction of iron ores in H _{2}-N_{2}atmosphereAlgorithm of overfitting avoidance in CNN based on maximum pooled and weight decay Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Cross-border E-commerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Fractional Differential Equations in Sports Training in Universities Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model