MN7024 Financial Modelling Assignment Sample
Module code and Title: MN7024 Financial Modelling Assignment Sample
Introduction
Quantitative data analysis is an effective tool through which the business or organisation can be able to make an effective decision for the company. On the other hand, the company through the use of effective tools for quantitative data analysis improves its performance in terms of decision-making performance. In the current era of the market, the completion has been increased to a greater extent under this situation an origination can be able to increase its competitiveness through the help of quantitative data analysis.
As this analysis comprises the manipulation of data and its impact on the dependent variable the business can enhance its overall performance. The following study is based on PMC Company which provide consultancy services to its different customer in the UK. It provides asset management services to its, valuable clients. The study is based on determining how the composition of the board of directors and distribution of equity share ownership has an impact on shareholder value creation.
The graphical presentation of data provides a wider range scope for a better understanding of the data and improves learning. The graphics of the data made it easy to understand the data and eliminate barriers such as literacy and language. Hence it is an effective tool through which a better understanding of the phenomena can be possible (Jones and Chantel, 2020, p.178).
Through the study of the data, it has been observed that the company market to book value, the composition of the board of directors and equity concentration have been given. The percentage share of the largest equity share has been also provided through which the existing shareholder and board composition can be evaluated.
Market to book value and equity concentration
The graphical presentation of the PMC market to book value and equity concentration has been highlighted. Based on analysis of the figure it has united that there is a high degree of positive correlation has been situated among the market o books value and equity concentration.
This highlighted that the correlation between these two variables stood more than 0.50 to 1. Hence, it can say that changes in one variable lead to a high positive impact on another variable (Šimenko, 2019, p.14). For instance, the equity concentration of the PMC increases this lead to an increase in the market-to-book value of the company.
Graphical presentation of the proportion of executive directors and equity concentration
There is a low degree of positive correlation between the proportion of executive directors and equity concentration. The low degree of positive correlation highlight that the change in one variable leads to having a positive impact on the other variable but at a low amount.
In this situation, it can say that the equity concentration of PMC can be affected if there is a certain change in the composition of the board of directors. However, this impact is quite low which indicates that the correlation lies between the variable is 0.25 to 0.50.
A descriptive statistic is an effective tool through which a better understanding of the whole data can be possible. It summarises the data in a more systematic manner through which an individual can be able to understand the set data effectively (Amrhein et al. 2019, p.262). The analysis of the set data set provided by the PMC has observed thta the total number of variables stood at 380 where the range of the data set stood at a minimum of 1.4942 and the maximum value stood at 3.36444.
On the other hand, the minimum value of the equity concentration stood at 21.3 at its minimum and 57.3. Its maximum. The range of the data set stood at 31.3. Descriptive statics comprises of the measure central tendency. The mean, standard deviation and kurtosis and skewness value of individual data sets have been highlighted. This increase the understanding of the data set.
Mean
Mean is the average value of the data set which can be calculated by the dividing summation of a variable by the number of variables itself (Ngcobo and Chisasa, 2018, p.15). Analysis of the mean value of the market to books value has found thta average value of this data set stood at 2.29. On the other hand, the mean value of the equity concentration stood at 37.347 which highlight that the average value of the data set stood at 34.34. Analysis of the mean value of the executive or board composition has observed that the mean value stood at 39.369. On the other hand difference between the smallest value and the largest value of the data set reflects at 31.3.
Standard deviation
It is a tool through which how much data set dispersed from the mean value can be measured. Hence the more standard deviation reflects more value of the data set dispersed from the mean value. As cited by Bono et al. (2019, p.19), the standard deviation of the market-to-book value stood at 0.3436 and the standard deviation of data equity concentration stood at 6.61.
This highlight that the standard deviation of the market-to-book values stood low which indicates thta data set is less widely dispersed from the mean value. The standard deviation of the summary and size is quite high which depicts that the data is widely dispersed in relation to its mean value. The high standard deviation reflects a high amount of risk.
Kurtosis
It is a tool through which the tailedness of a distribution can be measured. These tools assist a researcher to evaluate whether the data is heavily tailed or lightly tailed with respect to the normal distribution. As argued by Coope (2022, p.102), there are different categories of kurtosis for instance if the data is heavily tailed then it stood less than 3 and when it is lightly tailed the value of kurtosis stood at more than 3. The medium-tailed kurtosis stood equal to 3. The kurtosis of the data set stood at less than 3 which can be seen through the descriptive statistic where the value of kurtosis stood at -0.374 for the market to books value. This highlights that data is heavily tailed.
Skewness
Asymmetry of the distribution can be measured with the help of skewness. The distribution can be said to be asymmetric when the right of the left side is not a mirror image. There are different kinds of skewness such as positively skewed and negative skew data. When the skew is longer on the right side then it is said to be positive skew (Afyouni et al. 2019, p.605). On the other hand, when the data is left skewed then it is said to be negatively skewed. Analysis of the skewness of the market to books values has found that its skewness stood at 0.303 and the skewewness of the executive board of directors or board composition stood at 058.
Bivariate (two variable) analysis
In order to understand the phenomena and the impact of the data on shareholder value it is quite necessary for the company to assess the relationship between the different variable and their relation. Through the use of Bivariate analysis tools, the relation between the two variables such as market to books value (MBV), equity concentration and other variables and their relationship is highlighted (Saadatmorad et al. 2022, p.2689).
There are three pairs of two variables that have been set. This led to providing a wide range scope for understanding the phenomenon in a better way. The correlation has been done through the help of the Pearson correlation through which a better understanding of the data and its impact on the other hand, how the shareholder value has been impacted can be possible through the help of the type of correlation test.
Correlation between MBV and equity concentration
Through the analysis of the bivariate between the MBV and equity concentration, it has been found that the correlation between the equity concentration and market-to-book value is high. As cited by Pan et al. (2021, p.21), the value of correlation stood at 0.704 which indicates that there is a high positive relationship between these two variables. An increase in the equity concatenation leads to a high impact on the market value PMC.
Correlation between board composition and Dummy
Analysis of the correlation between these two variables indicates that there is a low positive correlation between these two variables. This can be seen through the below figure where the value of collection stood at 0.086. In this situation when the board composition portion has been change it has a low impact on the value of the dummy or 1 if the CEO and chairman of the board are the same (Wan et al. 2019, p.492). This indicates that there is low relation between these two variables and a change in one variable does not have a huge impact on the other variable.
Correlation between size and industry
The analysis of the correlation between the size and industry of PMC has been calculated through the help of bivariate analysis. This analysis depicts that there is a negative correlation between the size of the company and its industry over the market. This highlight that when the size which is the total million value of the company increase it has a negative impact on the different sector of the industry such as transposition, energy and other services.
Analysis of the correlation between these two variables depicts a value of -0.30 which means that the increase in the market size has a negative impact on the company. On the other hand, a decrease in the size of the industry has a positive impact on different sectors of the company. As argued by Shrestha, (2020, p.39), correlation analysis is an effective tool through which an organisation or business can be able to make an effective decision for the company. As per the case study, it has been observed that PMC has a client named assessment Management Company which provides administrative services to its valuable client.
The company has interested in the finding impact of corporate governance practices on shareholder value creation. Under this circumstance, how a company can be increased the value of shareholders through effective corporate governance has been highlighted. On the other hand, the determination of the composition board of directors and its impact on shareholder value creation can be evaluated. In this situation, the performance of the company can be increased through the help of adopting such kinds of tools for the decision-making process.
Multiple variables (three or more variables) analysis
The multiple variable analysis is refer to a situation where there is more than two variable. The multivariable is effective through which an organisation can be able to increase the understanding of the impact of different independent variables on the dependent. Multivariable data analysis can be possible through the help of tools such as regression analysis. The regression analysis highlights the actual impact of different factors or the relationship between the variable (Joseph et al. 2022, p.299).
Through the analysis of the case study, it has been observed that the director the AVV is interested in the calculation of how corporate governance has influenced the performance of the company. The multiple regression model highlight that there are different components of corporate governance such as the board of director, distribution of equity and another factor which has a direct impact on shareholder value creation. As the shareholder is a key aspect of the business on which the growth and development of the company vary. Hence, the regression analysis provides a wider range of scope for evaluating how this factor has impacted the shareholder value in the market.
Model summary
It provides details about the data set which is to be run in the regression analysis. The total number of observations stood at 380 and the value of R stood at 0.934. The value of R in the table represents the correlation between the predictor variable which is the x variable and the response variable which is the Y variable. In order to analyse the variable through the regression it is essential to have an effective correlation between the data.
On the other hand, the value of the square stood at 0.873. The value of the R square highlights the proportion of the dependent variable which is explained by the independent variable. In this situation, it can say thta there are 0.873 dependent variables which are represented by the independent variable of the data set. It is also found that the rate-adjusted R square stood at 0.872. The adjusted R square is the same as the R square but more reliable.
ANOVA table
It is a tool which assists researchers to break down the data into variations between the errors and treatment. In order to conduct the multiple data variables in a more effective manner it is quite essential for the company to break down the data in a more effective way (Pandian et al. 2022, p.04). The ANOVA table s used to assess whether the model is significant or not. In this data set, the dependent variable is market to book value of the company.
The test of the model has been done with the help of the t-test where it has been found that the value of f stood at 0.000 which is less than the significant level of 5% or 1%. In this situation, it can say that the model is significant. Under this circumstance, the change in any other factor such as the composition of the board of directors and the market size has a direct impact on the variable output.
Regression table
The regression table highlights the relationship between the dependent and independent variables. Through the analysis of the table, it has been observed that the constant or the intercept stood at 1.061 which highlights that at this point where the dependent variable cut the dependent variable. On the other hand, the analysis of the relationship between the relation between market to book value and equity concentration has observed that there is a positive relationship between these two variables.
As argued by Velumani et al. (2022, p.2614), the increase of one unit of equity concentration leads to has impact on the market to books vlaue by 0.38. Furthermore, there is also positive relation between the executive or board composition as the increase in the 1 unit of this composition lead to an impact of 0.001 on the market to book value of the company. Analysis of the relationship between the dummy and market-to-book value has found that there is a negative relation between the dummy and market to books value.
This indicates that a change in one unit of the dummy leads to having an inverse impact on the financial position of the market to the book value of the company. It is also noticed that there is a negative relation between the size of the industry and its impact on the market book value. This can be seen as the regression table highlight thta the increase in one unit in the size of the company lead to having an -6.551E impact on the market-to-book vlaue of the company.
Other issues
The analysis of the case study It has been found that there is every equity concentration has a direct and high positive impact on the value of shareholder value. As the company aimed to increase the shareholder value of the company, in this situation adopting that kind of strategy through which the equity concentration can be increased seem more feasible for AVV. This provides the opportunity for AVV to increase its shareholder vlaue and the market o book vlaue of the company.
Other than this, analysis of the impact on the corporate governance of the company has found that corporate governance has a significant impact on the shareholder value of the company. In this situation, the company needs to focus more on the implication of effective corporate governance practice in the company through which the ultimate value of the shareholder in the market (Dehghani et al. 2022, p.10).
Other than this, the performance of the company can be increased through the reduction in the size of the company and the effective composition of the board of directors. Based on the analysis of the correlation between the company’s board composition and market-to-book value it has been observed that there is very less impact on the market-to-book value.
Under this situation, it has been found that the company has a negative impact on the market-to-book value of the company. Furthermore, analysis of the impact of the dummy and its impact on the shareholder value of the company has found that adopting an effective strategy for better corporate governance provides a wider range of scope for the company to increase its performance.
As there is a negative relation between the dummy and the market book vlaue of the company, hence it is essential for AVV to increase its position and minimise the emerging issue of the company in a more systematic manner (Pandian et al. 2022, p.04). Apart from this, the overall performance of the company and its shareholder value can be increased through the help of an effective board and composition.
Conclusion
Based on the whole study of corporate governance and its impact on the shareholder vlaue it can conclude that there is a significant relationship between the corporate governance practice and its impact on the shareholder’s value. Through the analysis of the correlation of the AVV market to book value and its different factors of corporate, it can further be concluded that the increase in equity share concentration leads to a positive impact on the financial health of AVV.
Hence, It can say that the increase in shareholder value can be possible through the increase of equity share concentration. On the other hand, it can further be concluded that AVV through the implication of effective corporate governance practice and strategy provides an opportunity to improve the shareholder value in the market.
References
Bono, R., Arnau, J., Alarcón, R. And Blanca, M.J., 2019. Bias, precision, and accuracy of skewness and kurtosis estimators for frequently used continuous distributions. Symmetry, 12(1), p.19.
Cooper, M., 2022. Longer Males Determined with Positive Skew and Kurtosis in Centrobolus (Diplopoda: Spirobolida: Pachybolidae). New Visions in Biological Science Vol, 8, pp.102-106.
Dehghani, M.H., Hassani, A.H., Karri, R.R., Younesi, B., Shayeghi, M., Salari, M., Zarei, A., Yousefi, M. And Heidarinejad, Z., 2021. Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling. Scientific reports, 11(1), pp.1-15.
Jones, L.A. and Chantel, A.F., 2020. Oil and gas exploitation in the Ghanaian context: The balance of benefits and challenges. African Journal of Environmental Science and Technology, 14(7), pp.177-182.
Joseph, J.K., Jeyanthinathan, R. And Rao, L.B., 2022. Enhancing Fuel Efficiency of a Two-Wheeler Based on Taguchi and ANOVA Method and Regression Analysis. Advances in Thermal Sciences: Select Proceedings of ICFAMMT 2022, p.299.
Ngcobo, L. And Chisasa, J., 2018. The nature and benefits of participating in burial society stokvels in South Africa. Acta Universitatis Danubius. Œconomica, 15(2).
Pan, H., You, X., Liu, S. And Zhang, D., 2021. Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimization. Applied Intelligence, 51(2), pp.752-774.
Pandian, P., Thekkumalai, M., Das, A., Goel, M., Asthana, A. And Ramanaiah, V., 2022. Simulation of phenol and chlorophenol removal using combined adsorption and biodegradation: regression analysis and data-mining approach. Journal of Hazardous, Toxic, and Radioactive Waste, 26(3), p.04022015.
Saadatmorad, M., Talookolaei, R.A.J., Pashaei, M.H., Khatir, S. And Wahab, M.A., 2022. Pearson correlation and discrete wavelet transform for crack identification in steel beams. Mathematics, 10(15), p.2689.
Shrestha, N., 2020. Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), pp.39-42.
Šimenko, J., 2019. The benefits of Functional Movement Screen in judo. Revista de Artes Marciales Asiáticas, 14.
Velumani, P., Priyadharshini, B. And Mukilan, K., 2022. A mass appraisal assessment study of land values using spatial analysis and multiple regression analysis models (MRA). Materials Today: Proceedings, 66, pp.2614-2625.
Wan Mohamed Radzi, C.W.J., Salarzadeh Jenatabadi, H., Alanzi, A.R., Mokhtar, M.I., Mamat, M.Z. and Abdullah, N.A., 2019. Analysis of obesity among Malaysian university students: A combination study with the application of Bayesian structural equation modelling and Pearson correlation. International journal of environmental research and public health, 16(3), p.492.
Appendices
Appendix 1: Output 1
Descriptives
| Notes | ||
| Output Created | 20-DEC-2022 07:19:57 | |
| Comments | ||
| Input | Active Dataset | DataSet1 |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User defined missing values are treated as missing. |
| Cases Used | All non-missing data are used. | |
| Syntax | DESCRIPTIVES VARIABLES=MBV Con Exec1 Size Dummy Industry
/STATISTICS=MEAN STDDEV RANGE MIN MAX SEMEAN KURTOSIS SKEWNESS. |
|
| Resources | Processor Time | 00:00:00.00 |
| Elapsed Time | 00:00:00.00 | |
| Descriptive Statistics | ||||||
| N | Range | Minimum | Maximum | Mean | ||
| Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | |
| MBV | 380 | 1.8702 | 1.4942 | 3.3644 | 2.295081 | .0176263 |
| Con | 380 | 36.0 | 21.3 | 57.3 | 37.347 | .3392 |
| Exec1 | 380 | 31.3 | 23.2 | 54.5 | 39.369 | .3460 |
| Size | 380 | 300121 | 66751 | 366872 | 208995.95 | 3237.609 |
| Dummy | 380 | 1 | 0 | 1 | .53 | .026 |
| Industry | 380 | 3 | 1 | 4 | 2.33 | .057 |
| Valid N (listwise) | 380 | |||||
| Descriptive Statistics | |||||
| Std. Deviation | Skewness | Kurtosis | |||
| Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| MBV | .3436004 | .303 | .125 | -.374 | .250 |
| Con | 6.6120 | .387 | .125 | -.014 | .250 |
| Exec1 | 6.7443 | -.058 | .125 | -.360 | .250 |
| Size | 63112.613 | .040 | .125 | -.533 | .250 |
| Dummy | .500 | -.106 | .125 | -1.999 | .250 |
| Industry | 1.102 | .195 | .125 | -1.294 | .250 |
| Valid N (listwise) | |||||
CORRELATIONS
/VARIABLES=MBV Con
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES
/MISSING=PAIRWISE.
Correlations
| Notes | ||
| Output Created | 20-DEC-2022 07:44:32 | |
| Comments | ||
| Input | Active Dataset | DataSet1 |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics for each pair of variables are based on all the cases with valid data for that pair. | |
| Syntax | CORRELATIONS
/VARIABLES=MBV Con /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE. |
|
| Resources | Processor Time | 00:00:00.02 |
| Elapsed Time | 00:00:00.03 | |
| Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| MBV | 2.295081 | .3436004 | 380 |
| Con | 37.347 | 6.6120 | 380 |
| Correlations | |||
| MBV | Con | ||
| MBV | Pearson Correlation | 1 | .704** |
| Sig. (2-tailed) | .000 | ||
| N | 380 | 380 | |
| Con | Pearson Correlation | .704** | 1 |
| Sig. (2-tailed) | .000 | ||
| N | 380 | 380 | |
| **. Correlation is significant at the 0.01 level (2-tailed). |
CORRELATIONS
/VARIABLES=Exec1 Dummy
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES
/MISSING=PAIRWISE.
Correlations
| Notes | ||
| Output Created | 20-DEC-2022 07:45:16 | |
| Comments | ||
| Input | Active Dataset | DataSet1 |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics for each pair of variables are based on all the cases with valid data for that pair. | |
| Syntax | CORRELATIONS
/VARIABLES=Exec1 Dummy /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE. |
|
| Resources | Processor Time | 00:00:00.00 |
| Elapsed Time | 00:00:00.04 | |
| Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| Exec1 | 39.369 | 6.7443 | 380 |
| Dummy | .53 | .500 | 380 |
| Correlations | |||
| Exec1 | Dummy | ||
| Exec1 | Pearson Correlation | 1 | .086 |
| Sig. (2-tailed) | .094 | ||
| N | 380 | 380 | |
| Dummy | Pearson Correlation | .086 | 1 |
| Sig. (2-tailed) | .094 | ||
| N | 380 | 380 | |
CORRELATIONS
/VARIABLES=Size Industry
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES
/MISSING=PAIRWISE.
Correlations
| Notes | ||
| Output Created | 20-DEC-2022 07:45:39 | |
| Comments | ||
| Input | Active Dataset | DataSet1 |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics for each pair of variables are based on all the cases with valid data for that pair. | |
| Syntax | CORRELATIONS
/VARIABLES=Size Industry /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE. |
|
| Resources | Processor Time | 00:00:00.00 |
| Elapsed Time | 00:00:00.00 | |
| Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| Size | 208995.95 | 63112.613 | 380 |
| Industry | 2.33 | 1.102 | 380 |
| Correlations | |||
| Size | Industry | ||
| Size | Pearson Correlation | 1 | -.030 |
| Sig. (2-tailed) | .563 | ||
| N | 380 | 380 | |
| Industry | Pearson Correlation | -.030 | 1 |
| Sig. (2-tailed) | .563 | ||
| N | 380 | 380 | |
Regression
| Notes | ||
| Output Created | 20-DEC-2022 07:48:19 | |
| Comments | ||
| Input | Data | D:\ankit\december\19\sb1\file.sav |
| Active Dataset | DataSet1 | |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics are based on cases with no missing values for any variable used. | |
| Syntax | REGRESSION
/MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT MBV /METHOD=ENTER Con Exec1 Dummy Size. |
|
| Resources | Processor Time | 00:00:00.00 |
| Elapsed Time | 00:00:00.01 | |
| Memory Required | 2308 bytes | |
| Additional Memory Required for Residual Plots | 0 bytes | |
| Variables Entered/Removeda | |||
| Model | Variables Entered | Variables Removed | Method |
| 1 | Size, Dummy, Con, Exec1b | . | Enter |
| a. Dependent Variable: MBV |
| b. All requested variables entered. |
| Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .934a | .873 | .872 | .1230416 |
| a. Predictors: (Constant), Size, Dummy, Con, Exec1 |
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 39.068 | 4 | 9.767 | 645.144 | .000b |
| Residual | 5.677 | 375 | .015 | |||
| Total | 44.745 | 379 | ||||
| a. Dependent Variable: MBV |
| b. Predictors: (Constant), Size, Dummy, Con, Exec1 |
| Coefficientsa | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 1.061 | .045 | 23.530 | .000 | |
| Con | .038 | .001 | .737 | 32.110 | .000 | |
| Exec1 | .001 | .001 | .020 | .872 | .384 | |
| Dummy | -.424 | .013 | -.616 | -33.347 | .000 | |
| Size | -6.551E-008 | .000 | -.012 | -.652 | .515 | |
| a. Dependent Variable: MBV |
Appendix 2: Output 2
Descriptives
| Notes | ||
| Output Created | 20-DEC-2022 07:19:57 | |
| Comments | ||
| Input | Active Dataset | DataSet1 |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User defined missing values are treated as missing. |
| Cases Used | All non-missing data are used. | |
| Syntax | DESCRIPTIVES VARIABLES=MBV Con Exec1 Size Dummy Industry
/STATISTICS=MEAN STDDEV RANGE MIN MAX SEMEAN KURTOSIS SKEWNESS. |
|
| Resources | Processor Time | 00:00:00.00 |
| Elapsed Time | 00:00:00.00 | |
| Descriptive Statistics | ||||||
| N | Range | Minimum | Maximum | Mean | ||
| Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | |
| MBV | 380 | 1.8702 | 1.4942 | 3.3644 | 2.295081 | .0176263 |
| Con | 380 | 36.0 | 21.3 | 57.3 | 37.347 | .3392 |
| Exec1 | 380 | 31.3 | 23.2 | 54.5 | 39.369 | .3460 |
| Size | 380 | 300121 | 66751 | 366872 | 208995.95 | 3237.609 |
| Dummy | 380 | 1 | 0 | 1 | .53 | .026 |
| Industry | 380 | 3 | 1 | 4 | 2.33 | .057 |
| Valid N (listwise) | 380 | |||||
| Descriptive Statistics | |||||
| Std. Deviation | Skewness | Kurtosis | |||
| Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| MBV | .3436004 | .303 | .125 | -.374 | .250 |
| Con | 6.6120 | .387 | .125 | -.014 | .250 |
| Exec1 | 6.7443 | -.058 | .125 | -.360 | .250 |
| Size | 63112.613 | .040 | .125 | -.533 | .250 |
| Dummy | .500 | -.106 | .125 | -1.999 | .250 |
| Industry | 1.102 | .195 | .125 | -1.294 | .250 |
| Valid N (listwise) | |||||
CORRELATIONS
/VARIABLES=MBV Con
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES
/MISSING=PAIRWISE.
Correlations
| Notes | ||
| Output Created | 20-DEC-2022 07:44:32 | |
| Comments | ||
| Input | Active Dataset | DataSet1 |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics for each pair of variables are based on all the cases with valid data for that pair. | |
| Syntax | CORRELATIONS
/VARIABLES=MBV Con /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE. |
|
| Resources | Processor Time | 00:00:00.02 |
| Elapsed Time | 00:00:00.03 | |
| Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| MBV | 2.295081 | .3436004 | 380 |
| Con | 37.347 | 6.6120 | 380 |
| Correlations | |||
| MBV | Con | ||
| MBV | Pearson Correlation | 1 | .704** |
| Sig. (2-tailed) | .000 | ||
| N | 380 | 380 | |
| Con | Pearson Correlation | .704** | 1 |
| Sig. (2-tailed) | .000 | ||
| N | 380 | 380 | |
| **. Correlation is significant at the 0.01 level (2-tailed). |
CORRELATIONS
/VARIABLES=Exec1 Dummy
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES
/MISSING=PAIRWISE.
Correlation
| Notes | ||
| Output Created | 20-DEC-2022 07:45:16 | |
| Comments | ||
| Input | Active Dataset | DataSet1 |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics for each pair of variables are based on all the cases with valid data for that pair. | |
| Syntax | CORRELATIONS
/VARIABLES=Exec1 Dummy /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE. |
|
| Resources | Processor Time | 00:00:00.00 |
| Elapsed Time | 00:00:00.04 | |
| Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| Exec1 | 39.369 | 6.7443 | 380 |
| Dummy | .53 | .500 | 380 |
| Correlations | |||
| Exec1 | Dummy | ||
| Exec1 | Pearson Correlation | 1 | .086 |
| Sig. (2-tailed) | .094 | ||
| N | 380 | 380 | |
| Dummy | Pearson Correlation | .086 | 1 |
| Sig. (2-tailed) | .094 | ||
| N | 380 | 380 | |
CORRELATIONS
/VARIABLES=Size Industry
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES
/MISSING=PAIRWISE.
Correlations
| Notes | ||
| Output Created | 20-DEC-2022 07:45:39 | |
| Comments | ||
| Input | Active Dataset | DataSet1 |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics for each pair of variables are based on all the cases with valid data for that pair. | |
| Syntax | CORRELATIONS
/VARIABLES=Size Industry /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE. |
|
| Resources | Processor Time | 00:00:00.00 |
| Elapsed Time | 00:00:00.00 | |
| Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| Size | 208995.95 | 63112.613 | 380 |
| Industry | 2.33 | 1.102 | 380 |
| Correlations | |||
| Size | Industry | ||
| Size | Pearson Correlation | 1 | -.030 |
| Sig. (2-tailed) | .563 | ||
| N | 380 | 380 | |
| Industry | Pearson Correlation | -.030 | 1 |
| Sig. (2-tailed) | .563 | ||
| N | 380 | 380 | |
Regression
| Notes | ||
| Output Created | 20-DEC-2022 07:48:19 | |
| Comments | ||
| Input | Data | D:\ankit\december\19\sb1\file.sav |
| Active Dataset | DataSet1 | |
| Filter | <none> | |
| Weight | <none> | |
| Split File | <none> | |
| N of Rows in Working Data File | 380 | |
| Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
| Cases Used | Statistics are based on cases with no missing values for any variable used. | |
| Syntax | REGRESSION
/MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT MBV /METHOD=ENTER Con Exec1 Dummy Size. |
|
| Resources | Processor Time | 00:00:00.00 |
| Elapsed Time | 00:00:00.01 | |
| Memory Required | 2308 bytes | |
| Additional Memory Required for Residual Plots | 0 bytes | |
| Variables Entered/Removeda | |||
| Model | Variables Entered | Variables Removed | Method |
| 1 | Size, Dummy, Con, Exec1b | . | Enter |
| a. Dependent Variable: MBV |
| b. All requested variables entered. |
| Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .934a | .873 | .872 | .1230416 |
| a. Predictors: (Constant), Size, Dummy, Con, Exec1 |
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 39.068 | 4 | 9.767 | 645.144 | .000b |
| Residual | 5.677 | 375 | .015 | |||
| Total | 44.745 | 379 | ||||
| a. Dependent Variable: MBV |
| b. Predictors: (Constant), Size, Dummy, Con, Exec1 |
| Coefficientsa | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 1.061 | .045 | 23.530 | .000 | |
| Con | .038 | .001 | .737 | 32.110 | .000 | |
| Exec1 | .001 | .001 | .020 | .872 | .384 | |
| Dummy | -.424 | .013 | -.616 | -33.347 | .000 | |
| Size | -6.551E-008 | .000 | -.012 | -.652 | .515 | |
| a. Dependent Variable: MBV |
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