## Question 1

Dataset代写 Through the above analysis, an econometric model can be established to explain the value of the team. The expression of the model is as follows.

#### a) This dataset contains 13 variables, each with 400 observations.Specific variable names and descriptions are given in Table 1.

##### Table 1 Variable Names and Descriptions          Dataset代写
 ID Variable Name Variable Description 1 tsales Annual sales in Dutch guilders 2 sales sales per square meter 3 margin Gross-profit-margin 4 nown Number of owners (managers) 5 nfull Number of full-timers 6 npart Number of part-timers 7 naux Number of helpers (temporary workers) 8 hoursw Total number of hours worked 9 hourspw Number of hours worked per worker 10 inv1 Investment in shop-premises 11 inv2 Investment in automation 12 ssize Sales floorspace of the store (in m2) 13 start year start of business

Table 2 below is a statistical description of 13 variables. The mean, median, standard deviation, minimum and maximum values of each variable are listed in the table. Each variable in the dataset is a continuous numerical variable.

##### Table 2 Summary Statistics, using the observations 1 – 400          Dataset代写
 Variable Mean Median S.D. Min Max tsales 833584.21 694227 583463.84 50000 5000000 sales 6334.75 5278.93 3739.34 300 27000 margin 38.8 39.0 5.22 16.0 66.0 nown 1.28 1.00 0.640 1.00 10.0 nfull 2.07 1.96 1.02 1.00 8.00 npart 1.57 1.28 0.706 1.00 9.00 naux 1.39 1.37 0.395 1.00 4.00 hoursw 121 104 64.4 32.0 582 hourspw 19.0 17.7 7.34 5.71 43.3 inv1 58257.25 22207.03 107558.76 1000 1500000 inv2 27829.22 22859.85 41501.94 350 400000 ssize 151 120 112 16.0 1214 start 42.8 40.0 13.3 16.0 90.0

Table 3 is the correlation coefficient between the variables. As can be seen, all the correlation coefficients are less than 0.8, so there is no strong correlation between the variables.

##### 5% critical value (two-tailed) = 0.0981 for n = 400
 tsales sales margin nown nfull 1.0000 0.4699 0.2410 0.1284 0.5650 tsales 1.0000 0.1373 0.1479 0.2372 sales 1.0000 0.0524 0.1094 margin 1.0000 0.0621 nown 1.0000 nfull npart naux hoursw hourspw inv1 0.3908 0.1810 0.7092 0.5523 0.1905 tsales 0.0501 -0.0143 0.2630 0.2051 0.0009 sales 0.1837 -0.1013 0.2961 0.2950 0.0475 margin 0.0571 0.0066 0.4025 0.1363 0.0735 nown 0.2888 0.0842 0.5313 0.1251 0.1966 nfull 1.0000 0.0373 0.2491 -0.0241 0.2336 npart 1.0000 0.2097 0.0400 -0.0533 naux 1.0000 0.8078 0.1940 hoursw 1.0000 0.1176 hourspw 1.0000 inv1 inv2 ssize start 0.2052 0.5336 0.1808 tsales -0.0139 -0.2938 0.0656 sales 0.0515 0.2018 0.4784 margin 0.1166 0.0898 -0.0416 nown 0.1832 0.3497 0.0814 nfull 0.0943 0.3665 0.1773 npart 0.0002 0.2192 -0.0065 naux 0.2311 0.5759 0.2064 hoursw 0.1754 0.4350 0.2477 hourspw 0.4492 0.2322 -0.0124 inv1 1.0000 0.2424 0.0753 inv2 1.0000 0.1658 ssize 1.0000 start

#### b) Table 4 shows the regression results with tsales as dependent variable and nown,          Dataset代写

nfull, npart, and ssizeas independent variables.It can be seen from the results that the p value of nown is greater than 0.05, so the variable is not significant at the confidence level of 5%, and the p values of nfull, npart and ssize are all less than 0.05, so the three variables are significant at the confidence level of 5%.

The coefficients of these four variables are all positive,so their impacts on tsales are all For each additional person in number of owners, annual sales can be increased by 59,314.4 guilders. For each additional person in number of full-timers, annual sales can be increased by 230,689 guilders. For each additional person in number of part-timers, annual sales can be increased by 123,353 guilders. For each additional square meter in sales floorspace of the store, annual sales can be increased by 1,725.27 guilders.

##### Table 4 Model 1: OLS, using observations 1-400

Dependent variable: tsales            Dataset代写

 Coefficient Std. Error t-ratio p-value const −173662 71913.0 −2.415 0.0162 ** nown 59314.4 33533.2 1.769 0.0777 * nfull 230689 22840.0 10.10 <0.0001 *** npart 123353 33076.8 3.729 0.0002 *** ssize 1725.27 212.510 8.119 <0.0001 ***

 Mean dependent var 833584.2 S.D. dependent var 583464 Sum squared resid 7.18e+13 S.E. of regression 426485 R-squared 0.471063 Adjusted R-squared 0.465707 F(4, 395) 87.94534 P-value(F) 2.22e-53 Log-likelihood −5750.393 Akaike criterion 11510.8 Schwarz criterion 11530.74 Hannan-Quinn 11518.7

### Question 2          Dataset代写

#### a) The factors that affect the happiness of team fans can be divided into performanceson the fieldand off-field factors of The better a player does on the field,

the happier the fans will be when he is brought in. At the same time, the off- field factors of a player, such as the appearance and life behavior of the player, the frequency of communication with the fans through the Internet social platform, will affect the happiness of the fans.

##### The better a player’s appearance,          Dataset代写

the more positive his life behavior is, and the more frequently he communicates with fans through the Internet social platform, the happier the team’s fans are.

Therefore,these five variables in Table 5 can be selected to measure the happiness of fans. The data of Score, Top3 and Fansonline can be easily got from the Internet. The data of Appearance can be got from a network questionnaire, in which 0 can be set to indicate satisfaction, and 1 can be set to indicate dissatisfaction.

The data of Livebe can be got by judging whether the player had scandals in the past five years, in which 0 can be set to indicate no scandal, and 1 can be set to indicate having scandals.

##### Table 5Variable Names and Descriptions
 ID Variable Name Variable Description 1 Score The average post-match score in the past year 2 Top3 The number of the top3 in international class A games in the past year 3 Appearance The appearance of the player (0 or 1) 4 Livebe The living behavior (0 or 1) 5 Fansonline The number of fans on online social platforms

#### b) The factors that affect the value of the team are the performance of the team on the fieldand the influence of the team off-field.

##### The performance of the team can be measured by two variables,

one is the team’s ranking in the league in the last three years, the other is the team’s winning rate in the last three years.

The off-field influence of the team can be measured by two variables, one is the total value of all players, the other is the number of fans on the team’s online social platform. The specific variable description is shown in Table 6.

##### Table 6Variable Names and Descriptions          Dataset代写
 ID Variable Name Variable Description 1 Rank The team’s ranking in the league in the last three years 2 Winr The team’s winning rate in the last three years 3 Tvalue The total value of all players 4 Nfans The number of fans on the team’s online social platform

#### c) Through the above analysis,    Dataset代写

an econometric modelcan be establishedto explain the value of the team. The expression of the model is as follows.

The regression analysis of this model can be done by importing the data collected online into Gretl. is a constant term are the coefficients of these four variables, and  is a residual term.

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