Introduction:

This study focuses on firm profitability, a crucial topic of interest for business owners, investors, and financial analysts alike (Stierwald, 2009). The term “profitability” refers to a company’s ability to profit after deducting operating expenses, which is pivotal in decision-making. It affects the firm’s survival, growth, and value and provides a barometer for measuring its performance, efficiency, and risk (Chen and Chen, 2017).

Return on Assets (ROA) is used as the profitability metric for this study. A common financial ratio, return on assets, measures how much money a business makes about how much it spends on resources overall (Petersen and Schoeman, 2008). It is a broad indicator of profitability as it encapsulates the efficiency with which a company is managed and utilises its assets to produce profits.

The report focuses on two specific explanatory variables:

1. 1. Firm Size

1. 1. Sales Growth

(Pagano and Schivardi, 2003) An organisation’s size is often measured by its natural logarithm of total assets, chosen due to its potential influence on the economies of scale, market power, and operational efficiency (Beck, Demirgüç-Kunt and Maksimovic, 2003). On the other hand, Sales Growth is selected as it directly impacts a firm’s revenues and, thus, profitability. Further, it indicates the company’s competitive standing and growth potential (Ghozali, 2018).

The methodologies employed in the report encompass descriptive statistics, correlation analysis, and regression analysis (David, Ezekiel and Fox, 1960). Descriptive statistics provide a summary of the central tendency, dispersion, and distribution shape of the dataset. This helps understand the data’s overall pattern and identify outliers (Al Mutairi, 2018).

A correlation study determines the nature and magnitude of the linear connection between the profitability indicator and the variables of interest (Carstina et al., 2015). A positive correlation implies that as one variable increases, the other does too, and vice versa for a negative correlation (Schober and Schwarte, 2018).

Lastly, regression analysis is utilised to inspect the impact of Corporation Volume and Sales Growth on profitability (Goh et al., 2022). This technique allows us to quantify the relationship’s strength between variables and make predictions. This report’s regression section includes single- and multiple-variable linear regression analyses (Searle, Draper and Smith, 1967).

The research and analyses presented in this report are intended to add to the body of knowledge on corporate profitability, particularly regarding Firm Size and Sales Growth (Goh et al., 2022). Furthermore, the findings could offer corporate finance and strategic management practitioners valuable insights (Puck and Filatotchev, 2018).

Report Structure:

After this introduction, the sample selection process is explained, followed by an outline of the selected variables. The report then delves into the results from the histograms and descriptive statistics, correlation, and regression analyses. Finally, it briefly addresses the study’s outcomes and limitations and advises more research.

Explanation of the Process of Sample Selection:

The Fame database provided the necessary information for this investigation, renowned for its comprehensive and reliable financial information on companies (Eklund et al., 2002; Gupta and Mehta, 2021). The search strategy was executed with a three-step Boolean search (Oldroyd and Schroeder, 1982).

The first step was filtering out all active companies that were neither receivership nor dormant. This step ensured the inclusion of operating companies with financial data available for analysis. This initial selection yielded 219,117 companies (Kral et al., 2018).

The filter was further narrowed to enterprises included in the FTSE All-Share Index in the second step. The FTSE All-Share Index consists of all firms that make it through a size and liquidity screening and are listed on the LSE’s main market (Chbib, 2015). This step was taken to ensure the representativeness and comparability of the companies in the sample. After this step, 510 companies remained.

The third and final step involved selecting companies from specific BvD sectors (Yang et al., 2015). These sectors encompass various industries, from Agriculture, Mining & Extraction, Utilities, and Construction to Biotechnology and Life Sciences, Information Services, and Waste Management & Treatment. This step was taken to ensure a comprehensive and diverse sample representing various sectors of the economy.

After these three steps, the final sample comprised 303 companies.

This research defines an Ultimate Owner as a corporation with unknown shareholders’ identities or percentage ownership stakes (La Porta, Lopez de Silanes and Shleifer, 1998). In addition, at least 50.01% control must be the subject firm and the Ultimate Owner. This definition ensures that the companies in the sample have a clear ownership structure, making it easier to relate their financial performance to their size, sector, and other characteristics (Din et al., 2021).

Outline of the Variables Selected:

For this analysis, the variables selected include Firm Size, Sales Growth, and Return on Assets (ROA) as a barometer of financial success. The broader data set also considers other variables such as Liquidity, Financial Leverage, and Asset Tangibility. Here’s a breakdown of each variable and the rationale behind their selection:

1. 1. Firm Size: The natural logarithm of total assets is often used to represent this metric. A company’s profitability may depend critically on how big it is. Larger firms might benefit from economies of scale, leading to higher profitability (Mule, Mukras and Nzioka, 2015). Conversely, larger firms may become bureaucratic and harder to manage effectively, potentially hurting profitability.

• • Sales Growth: This variable is calculated as the percentage change in sales from the previous period. Sales growth is a critical indicator of the firm’s market performance and ability to expand its operations. High sales growth can increase profitability through improved efficiencies and market dominance (Tresnawati, 2021).

• • Return on Assets (ROA): A key profitability fraction indicates how well an enterprise utilises its resources to produce income. Net income/total assets are the formulae. A greater ROA indicates greater profitability and asset utilisation. (Petersen and Schoeman, 2008).

• • Liquidity: Liquidity measures a company’s capacity to pay its short-term debts and is calculated by dividing its current assets by its current liabilities. While a company’s ability to access and utilise its cash is essential, excessive reserves may indicate that its assets must be used to their full potential (Eljelly, 2004).

• • Financial Leverage: This is often gauged by the debt level (total debt / total assets). While obligation can provide a source of funds for expansion and thus lead to higher profitability, high leverage also increases financial risk and could reduce profitability. If the company’s ROI is lower than its debt service costs, it will likely default (Ozdgli, 2010).

• • Asset Tangibility: This is the proportion of tangible assets to total assets. Companies with more tangible assets may have different risk and profitability profiles than those with more intangible assets. For example, firms with many tangible assets may be able to use those assets as collateral for loans, potentially leading to a lower cost of capital and higher profitability (Lim, Macias and Moeller, 2019).

As financial theory and previous research suggested, these variables were selected for their potential influence on profitability. They provide a broad overview of a firm’s financial condition, market performance, and management efficiency.

Descriptive Statistics and Discussion:

In this analysis, we looked at five variables: Firm Size, Sales Growth, Liquidity (Current Ratio), Financial Leverage (Debt Ratio), and Asset Tangibility. The descriptive statistics for each variable are as follows:

1. 1. Firm Size: Firm sizes ranged from 14.66 on average to 14.57 in the middle, indicating that the size distribution is symmetric. The range of firm size was between 9.13 and 19.63, with a standard deviation of 1.72, indicating some variability in firm size. The skewness and kurtosis values close to 0 suggest a normal distribution. No outliers were identified.

• • Sales Growth: Sales Growth had a very wide range, from -100 to 20,143,099,900, with an average of 472,844,373.1. The median was 87,841,900, much lower than the mean, indicating a highly skewed distribution. The standard deviation was extremely large, suggesting significant variation in Sales Growth. The high skewness and kurtosis values confirm the uneven and leptokurtic distribution. No outliers were identified.

• • Liquidity (Current Ratio): The average current ratio was -2.00, with a median of -1.45. The range was from -20.91 to -0.06, with a standard deviation of 2.17, indicating a wide dispersion in the liquidity levels of the firms. The negative skewness and high kurtosis values suggest a negatively skewed and leptokurtic distribution. No outliers were identified.

• • Financial Leverage (Debt Ratio): The average debt ratio was -22.87, with a median of -22.46, showing a somewhat symmetric distribution. The range was between -126.30 and 0, and the standard deviation was 17.30, indicating a significant variation in the firms’ debt levels. The negative skewness and positive kurtosis values suggest a negatively skewed and leptokurtic distribution. No outliers were identified.

• • Asset Tangibility: The average asset tangibility was 22.51, with a median of 14.47, indicating a positively skewed distribution. The range was between 0 and 94.16, with a standard deviation of 22.65, showing considerable variation in asset tangibility among the firms. The positive skewness and kurtosis values suggest a positively skewed and leptokurtic distribution. No outliers were identified.

Based on the data, we can deduce that firm size has a weak negative correlation with ROA, sales growth has a weak positive correlation with ROA, liquidity has a weak negative correlation with ROA, financial leverage has a weak positive correlation with ROA, and the tangibility of the firm’s assets has a weak negative correlation with ROA (Dirmansyah, Syalsabila and Lestari, 2022).

Histograms can be used to inspect these distributions visually. Generally, normal distributions will have a bell-shaped histogram, while skewed distributions will have histograms pulled out to one side or the other. The presence of outliers can also be examined using boxplots. In this case, no outliers were identified.

Correlation Analysis:

Firm Size, Sales Growth, Liquidity, Financial Leverage, and Asset Tangibility were analysed for their correlations with return on assets (ROA). According to existing research, these factors were chosen for their potential impact on company profitability (Shahfira and Hasanuh, 2021).

The modest positive correlation between ROA and Financial Leverage was 0.232. Profitability may be affected by financial leverage. This positive association shows that enterprises’ return on assets marginally improves when they take on additional debt. Debt’s tax shield may lower a company’s tax obligation and boost profits (Kemsley and Nissim, 2002). While leverage may increase profits, it also raises financial risk (Hussan, 2016). Thus, organisations must handle influence wisely.

We next analysed the association between Firm Size and ROA, which was -0.155. This data shows that larger organisations may have a lower ROA than smaller ones. Coordination and communication issues may arise as organisations expand, lowering efficiency and profitability. However, the negative association is small. Thus, this interpretation should be cautious.

ROA’s association with Sales Growth was 0.003. This implies a more linear link between sales growth and ROA. This is surprising since enterprises with stronger sales growth should be more profitable. However, other variables may have outweighed the sales increase in our sample’s profitability.

Liquidity and Asset Tangibility had poor associations of -0.099 and -0.098, respectively. A firm’s short-term financial health is evaluated by its liquidity. The small negative association shows that more liquidity may be linked with lower ROA. The negative association between Asset Tangibility and ROA suggests that organisations with more tangible assets have a lower ROA.

The correlation study showed several intriguing patterns and probable links, although the correlations were typically weak (Cohen and Kohn, 2011). This suggests that although these variables may affect ROA, many other things do. Future research should examine additional factors and employ more sophisticated statistical methods to explain corporate profitability fully. This study used correlation analysis as an exploratory technique before regression analysis.

Regression Analysis:

Return on assets (ROA) is a key indicator of a company’s success. Hence a regression analysis was performed to isolate and quantify the variables most responsible for ROA (Pointer and Khoi, 2019).

Starting with Firm Size, the regression output shows an R-Squared value of 0.024, indicating that only 2.4% of the variability in ROA can be explained by Firm Size. Furthermore, the negative coefficient of -1.449 suggests that the ROA decreases as the Firm Size increases, which aligns with the earlier correlation analysis. However, the significance level (p-value) of 0.0067 indicates this relationship is statistically significant.

Insignificant effects of sales growth on return on investment. With such a low R-Squared and a high p-value of 0.954, Sales Growth likely contributes little to the variation in ROA.

Regarding Liquidity, the R-Squared value is also quite small (0.0097), indicating that only 0.97% of the variability in ROA can be explained by Liquidity. The coefficient for Liquidity is negative, suggesting that higher liquidity levels are associated with lower ROA. However, the p-value of 0.086 indicates that using the standard threshold for statistical significance, this connection does not seem to exist (e.g., 0.05).

Financial leverage and return on investment follow different paths. The R-Squared value of 0.054 suggests that Financial Leverage explains 5.4% of the variability in ROA. The positive coefficient of 0.215 indicates that higher financial leverage is associated with higher ROA. This relationship is also statistically significant, with a p-value much less than 0.05.

Finally, the regression analysis for Asset Tangibility revealed results identical to Financial Leverage. However, this is a mistake, as Asset Tangibility and Financial Leverage are different variables and should yield different results (Baloch, 2015).

Only Firm Size and Financial Leverage showed statistically significant relationships with ROA out of the selected variables. However, the small R-Squared values suggest that the model’s predictive power is limited. This implies that other variables not included in this model may significantly influence ROA. Further research could be directed towards identifying and including these variables to improve the model’s predictive power (Kaneko, 2021).

Limitations and Recommendations:

Though insightful, the research has certain drawbacks. First, the study had only 303 observations. This may restrict the results’ applicability to more companies. Comprehensive investigations with higher sample numbers may provide more accurate findings. Second, the research used cross-sectional data (Pedagógia, 2011). This design disregards future trends. Future longitudinal studies follow factors throughout time. This may reveal other time-dependent elements impacting ROA and how these interactions develop.

Thirdly, as seen by low R-Squared values, the model’s poor explanatory power shows that additional variables not examined in this research may substantially affect ROA (Veall and Zimmermann, 1996). These might include the firm’s industry, age, managerial quality, market circumstances, or macroeconomic issues. Future studies should include these characteristics and use more longitudinal data to overcome these constraints. Multi-level or structural equation modelling may reveal more intricate correlations and give a more comprehensive knowledge of ROA causes (Buncher, Succop and Dietrich, 1991).

Conclusion:

In conclusion, this research examined variables affecting return on assets (ROA), a key profitability indicator. Firm Size, Sales Growth, Liquidity, Financial Leverage, and Asset Tangibility were examined. Financial leverage demonstrated a small but statistically significant positive connection with ROA, indicating that enterprises with more power may have higher ROA (Shahfira and Hasanuh, 2021). Influence increases return on investment but also raises leverage risks.

Larger businesses may require assistance managing their assets since ROA has a weak and negative association with company size. This may show the risks of corporate growth and the need for asset management. Sales Growth, Liquidity, and Asset Tangibility did not affect ROA. This indicates that certain variables may not directly affect ROA linearly or that other, more powerful factors may overwhelm them.

These results help explain ROA factors. They also demonstrate the complexity of the business environment and the many elements that affect corporate profitability. The model’s limited explanatory power shows that ROA’s drivers are still being studied. Better corporate decisions and policy development need greater research.

References:

1. Baloch, Q.B. (2015) ‘Impact of Firm Size, Asset Tangibility and Retained Earnings on Financial Leverage: Evidence from Auto Sector, Pakistan’.
2. Beck, T., Demirgüç-Kunt, A. and Maksimovic, V. (2003) ‘Financial and Legal Institutions and Firm Size’, Economic Growth [Preprint]. Available at: https://doi.org/10.1596/1813-9450-2997.
3. Buncher, C.R., Succop, P.A. and Dietrich, K.N. (1991) ‘Structural equation modelling in environmental risk assessment.’ Environmental Health Perspectives, 90, pp. 209–213. Available at: https://doi.org/10.1289/EHP.90-1519490.
4. Carstina, S. et al. (2015) ‘Correlation Analysis of the Indicators of Asset Management and Profitability’, International Journal of Economics and Business Administration, III(Issue 2), pp. 3–21. Available at: https://doi.org/10.35808/IJEBA/67.
5. Chbib, I. (2015) ‘An investigation into the impact of board composition and ownership structure on corporate performance : the case of the FTSE All Share listed companies’.
6. Chen, L.-J. and Chen, S. (2017) ‘The influence of profitability on firm value with capital structure as the mediator and fit size and industry as moderators’, Investment management & financial innovations [Preprint].
7. Cohen, M.R. and Kohn, A. (2011) ‘Measuring and interpreting neuronal correlations’, Nature Neuroscience, 14(7), pp. 811–819. Available at: https://doi.org/10.1038/NN.2842.
8. David, F.D., Ezekiel, M. and Fox, K.A. (1960) ‘Methods of Correlation and Regression Analysis.’ Biometrika, 47(3/4), p. 487. Available at: https://doi.org/10.2307/2333331.
9. Din, S.U. et al. (2021) ‘Ownership structure and corporate financial performance in an emerging market: a dynamic panel data analysis’, International Journal of Emerging Markets, 17(8), pp. 1973–1997. Available at: https://doi.org/10.1108/IJOEM-03-2019-0220.
10. Dirmansyah, N.O., Syalsabila, L. and Lestari, H.S. (2022) ‘Pengaruh Likuiditas Terhadap Kinerja Perusahaan Pada Perusahaan Yang Terdaftar Di BEI’, Jurnal Ekonomi, 27(1), pp. 49–63. Available at: https://doi.org/10.24912/JE.V27I1.854.
11. Eklund, T. et al. (2002) ‘Assessing The Feasibility Of Self-organizing Maps For Data Mining Financial Information’.
12. Eljelly, A.M.A. (2004) ‘Liquidity ‐ profitability tradeoff: An empirical investigation in an emerging market’, International Journal of Commerce and Management, 14(2), pp. 48–61. Available at: https://doi.org/10.1108/10569210480000179.
13. Ghozali, I. (2018) ‘THE ROLE OF SALES GROWTH TO INCREASE FIRM PERFORMANCE IN INDONESIA’.
14. Goh, T.S. et al. (2022) ‘Sales Growth and Firm Size Impact on Firm Value with ROA as a Moderating Variable’, MIX: JURNAL ILMIAH MANAJEMEN, 12(1), p. 99. Available at: https://doi.org/10.22441/JURNAL_MIX.2022.V12I1.008.
15. Gupta, S. and Mehta, S.K. (2021) ‘Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies’, Global Business Review [Preprint]. Available at: https://doi.org/10.1177/0972150920984857.
16. Hussan, Md.J. (2016) ‘Impact of Leverage on Risk of the Companies’, Journal of Civil and legal sciences, 05(04). Available at: https://doi.org/10.4172/2169-0170.1000200.
17. Kaneko, H. (2021) ‘Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables’, Heliyon, 7(6). Available at: https://doi.org/10.1016/J.HELIYON.2021.E07356.
18. Kemsley, D. and Nissim, D. (2002) ‘Valuation of the Debt-Tax Shield’, Journal of Finance, 57(5), pp. 2045–2073. Available at: https://doi.org/10.1111/0022-1082.00488.
19. Kral, P. et al. (2018) ‘Comprehensive assessment of the selected indicators of financial analysis in the context of failing companies, Journal of International Studies, 11(4), pp. 282–294. Available at: https://doi.org/10.14254/2071-8330.2018/11-4/20.
20. Lim, S.C., Macias, A.J. and Moeller, T. (2019) ‘Intangible Assets and Capital Structure’, Merger and Acquisition [Preprint]. Available at: https://doi.org/10.2139/SSRN.2514551.
21. Mule, R., Mukras, M. and Nzioka, O.M. (2015) ‘CORPORATE SIZE, PROFITABILITY AND MARKET VALUE: AN ECONOMETRIC PANEL ANALYSIS OF LISTED FIRMS IN KENYA’, European Scientific Journal, ESJ [Preprint].
22. Al Mutairi, A.O. (2018) ‘The descriptive statistics for the generalised power function distribution’, Journal of Statistics and Management Systems, 21(5), pp. 775–785. Available at: https://doi.org/10.1080/09720510.2018.1453680.
23. Oldroyd, B.K. and Schroeder, J.J. (1982) ‘Study of strategies used in online searching: 2. Positional Logic — an example of the importance of selecting the right Boolean operator’, Online Information Review, 6(2), pp. 127–133. Available at: https://doi.org/10.1108/EB024094.
24. Ozdagli, A.K. (2010) ‘Financial Leverage, Corporate Investment and Stock Returns’, Capital Markets: Asset Pricing & Valuation eJournal [Preprint]. Available at: https://doi.org/10.2139/SSRN.1713434.
25. Pagano, P. and Schivardi, F. (2003) ‘Firm Size Distribution and Growth’, The Scandinavian Journal of Economics, 105(2), pp. 255–274. Available at: https://doi.org/10.1111/1467-9442.T01-1-00008.
26. Pedagógia (2011) ‘Cross-Sectional study’, Calcified Tissue International, 41(1 Supplement). Available at: https://doi.org/10.1007/BF02556813.
27. Petersen, M. and Schoeman, I. (2008) ‘Modeling of Banking Profit via Return-on-Assets and Return-on-Equity’.
28. Pointer, L. V. and Khoi, P.D. (2019) ‘Predictors of Return on Assets and Return on Equity for Banking and Insurance Companies on Vietnam Stock Exchange’, Entrepreneurial Business and Economics Review, 7(4), pp. 185–198. Available at: https://doi.org/10.15678/EBER.2019.070411.
29. La Porta, R., Lopez de Silanes, F. and Shleifer, A. (1998) ‘Corporate Ownership Around the World’, Corporate Finance and Organizations eJournal [Preprint]. Available at: https://doi.org/10.2139/SSRN.103130.
30. Puck, J. and Filatotchev, I. (2018) ‘Finance and the multinational company: Building bridges between finance and global strategy research’, Global Strategy Journal, 10(4), pp. 655–675. Available at: https://doi.org/10.1002/GSJ.1330.
31. Schober, P. and Schwarte, L.A. (2018) ‘Correlation Coefficients: Appropriate Use and Interpretation’, Anesthesia & Analgesia, 126(5), pp. 1763–1768. Available at: https://doi.org/10.1213/ANE.0000000000002864.
32. Searle, S.R., Draper, N.R. and Smith, H. (1967) ‘Applied Regression Analysis’, Biometrics, 23(2), p. 369. Available at: https://doi.org/10.2307/2528172.
33. Shahfira, D. and Hasanuh, N. (2021) ‘The Influence of Company Size and Debt to Asset Ratio on Return On Assets’, Moneter – Jurnal Akuntansi dan Keuangan, 8(1), pp. 9–13. Available at: https://doi.org/10.31294/MONETER.V8I1.8807.
34. Stierwald, A. (2009) ‘Determinants of firm profitability: the effect of productivity and its persistence’.
35. Tresnawati, R. (2021) ‘Influences Of Sales Growth And Leverage On Profitability (Empirical Study of Manufacturing Companies in the Consumer Goods Sector listed on the Indonesia Stock Exchange for the 2016-2019 Period)’. Available at: https://doi.org/10.17762/TURCOMAT.V12I8.2836.
36. Veall, M.R. and Zimmermann, K.F. (1996) ‘PSEUDO‐R2 MEASURES FOR SOME COMMON LIMITED DEPENDENT VARIABLE MODELS’, Journal of Economic Surveys, 10(3), pp. 241–259. Available at: https://doi.org/10.1111/J.1467-6419.1996.TB00013.X.

APPENDICES

DESCRIPTIVE STATISTICS

DESCRIPTIVE STATISTICS FIRM SIZE
MIN	MAX	Average	Median	Standard Deviation	Skewness	Kurtosis
9.130539302	19.62604957	14.66454481	14.57139777	1.715800242	0.31305066	0.234255296
Q1 AND Q3		IQR		Identify Outliers
Q1	Q3
13.41364765	15.72877972	2.315132064		Not Outlier
DESCRIPTIVE STATISTICS SALES GROWTH
MIN	MAX	Average	Median	Standard Deviation	Skewness	Kurtosis
-100	20143099900	472844373.1	87841900	1533713219	8.95473394	100.863349
Q1 AND Q3		IQR		Identify Outliers
Q1	Q3
24454900	305874900	281420000		Not Outlier
DESCRIPTIVE STATISTICS LIQUIDITY (CURRENT RATIO)
MIN	MAX	Average	Median	Standard Deviation	Skewness	Kurtosis
-20.91476806	-0.059542425	-2.002521442	-1.449130631	2.16916271	-4.4954791	27.86972193
Q1 AND Q3		IQR		Identify Outliers
Q1	Q3
-2.077166008	-0.967360413	1.109805594		Not Outlier
DESCRIPTIVE STATISTICS FINANCIAL LEVERAGE (DEBT RATIO)
MIN	MAX	Average	Median	Standard Deviation	Skewness	Kurtosis
-126.301921	0	-22.86953535	-22.45596249	17.30333267	-1.2714878	4.057415157
Q1 AND Q3		IQR		Identify Outliers
Q1	Q3
-31.19152215	-9.096099036	22.09542311		Not Outlier
DESCRIPTIVE STATISTICS Asset Tangibility
MIN	MAX	Average	Median	Standard Deviation	Skewness	Kurtosis
0	94.16348453	22.50571409	14.47109713	22.64738469	1.12796324	0.555277906
Q1 AND Q3		IQR		Identify Outliers
Q1	Q3
4.118607395	35.23141472	31.11280732		Not Outlier

CORRELATION ANALYSIS

Correlation B/w ROA and Firm Size	Correlation B/w ROA and Sales Growth	Correlation B/w ROA and Liquidity (Current Ratio)	Correlation B/w ROA and Financial Leverage (Debt Ratio)	Correlation B/w ROA Asset Tangibility
-0.155527277	0.003323203	-0.09878692	0.232500382	-0.098402492

FIRM SIZE SUMMARY OUTPUT

Regression Statistics
Multiple R	0.155527277
R Square	0.024188734
Adjusted R Square	0.020946836
Standard Error	15.81911488
Observations	303

ANOVA

	df	SS	MS	F	Significance F
Regression	1	1867.145505	1867.145505	7.461287995	0.006676115
Residual	301	75323.5631	250.2443957
Total	302	77190.70861

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	27.95842412	7.832915202	3.569351052	0.000416344	12.54421412	43.37263412	12.54421412	43.37263412
X Variable 1	-1.449168501	0.530532488	-2.731535831	0.006676115	-2.493190933	-0.405146068	-2.493190933	-0.405146068

SALES GROWTH SUMMARY OUTPUT

Regression Statistics
Multiple R	0.003323203
R Square	1.10437E-05
Adjusted R Square	-0.003311179
Standard Error	16.01389099
Observations	303

ANOVA

	df	SS	MS	F	Significance F
Regression	1	0.852469254	0.852469254	0.003324183	0.954061111
Residual	301	77189.85614	256.4447048
Total	302	77190.70861

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	6.690647846	0.962841404	6.948857641	2.28E-11	4.795894844	8.585401	4.795894844	8.585400847
X Variable 1	3.46411E-11	6.00827E-10	0.057655733	0.954061111	-1.14771E-09	1.22E-09	-1.14771E-09	1.21699E-09

LIQUIDITY SUMMARY OUTPUT

Regression Statistics
Multiple R	0.09878692
R Square	0.009758856
Adjusted R Square	0.006469018
Standard Error	15.93564879
Observations	303

ANOVA

	df	SS	MS	F	Significance F
Regression	1	753.2929831	753.2929831	2.966363868	0.086039953
Residual	301	76437.41563	253.9449024
Total	302	77190.70861

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	5.249007518	1.246893993	4.209666215	3.38071E-05	2.795274092	7.702740945	2.795274092	7.702740945
X Variable 1	-0.728092168	0.422740784	-1.722313522	0.086039953	-1.559993836	0.103809499	-1.559993836	0.103809499

FINANCIAL LEVERAGE SUMMARY REPORT

Regression Statistics
Multiple R	0.232500382
R Square	0.054056428
Adjusted R Square	0.050913758
Standard Error	15.57513721
Observations	303

ANOVA

	df	SS	MS	F	Significance F
Regression	1	4172.653948	4172.653948	17.20079841	4.37892E-05
Residual	301	73018.05466	242.5848992
Total	302	77190.70861

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	11.61984141	1.484515311	7.827363802	8.54538E-14	8.698498567	14.54118425	8.698498567	14.54118425
X Variable 1	0.214819131	0.05179629	4.147384526	4.37892E-05	0.112890428	0.316747834	0.112890428	0.316747834

ASSET TANGIBILITY SUMMARY REPORT

Regression Statistics
Multiple R	0.098402492
R Square	0.00968305
Adjusted R Square	0.006392961
Standard Error	15.93625874
Observations	303

ANOVA

	df	SS	MS	F	Significance F
Regression	1	747.4415231	747.4415231	2.943096325	0.087274313
Residual	301	76443.26709	253.9643425
Total	302	77190.70861

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	8.270390876	1.291750585	6.402467297	5.86E-10	5.728385216	10.81239654	5.728385216	10.81239654
X Variable 1	-0.069465167	0.040491591	-1.715545489	0.087274313	-0.149147619	0.010217285	-0.149147619	0.010217285