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Introduction
Profit is the difference between revenues (revenues from sales of goods and services) and the cost of production or acquisition and distribution of these goods and services. Profit = Revenue - Expenses (in monetary terms).
This is one of the most important indicators of the financial results of the economic activity of business entities (organizations and businesses), for which the ground and carries on business.
Profit as the main form of cash savings depends primarily on reducing the cost of production and circulation of goods , as well as from increased sales .
Profit margin as the final financial result of the enterprise depends on the second , no less important value - the gross income of the enterprise . Size of gross income of the enterprise and therefore profit depends not only on the quantity and quality of product produced and sold products (works , services) , but also on the level of prices used .
Type and level of prices used ultimately determine the amount of the gross income of the enterprise and, therefore, profit.
The problem of pricing is a key element in the system of market relations. With the high price of any company reimburse largest production cost that is not helped improve product quality and production efficiency .
Another factor that influences the amount of profit - is depreciation of fixed assets and intangible assets. Amount of depreciation is determined based on the carrying value of fixed assets and amortization of existing norms and amortization based on the useful life of intangible assets , but • not more than 10 years of continuous operation . This takes into account the accelerated amortization of the active part of fixed assets, which is reflected in the legislation of higher depreciation rates for the respective types of fixed assets .
Thus, the company's profit is influenced by the following main factors: the gross income of the enterprise, the enterprise's income from sales , gross expenses of the enterprise, the level of current prices for products sold and depreciation expense .
Foremost among these is the amount of gross expenditure . Quantified in the price structure costs occupy a significant proportion , so reducing costs very noticeable effect on earnings growth , ceteris paribus .
In the analysis of the factors influencing the amount of profit reserves are increasing the profits of the enterprise , the main ones are :
- Ensuring the growth of production on the basis of its technical upgrade and improve production efficiency .
- Improvement of sales, including by improving the payment and settlement of relations between enterprises.
- Restructuring of products produced and sold by increasing the proportion of more profitable.
- Reduction of gross expenditure on the production and circulation of products .
- Establishing real price levels depending on the quality of products, its competitiveness , demand and supply of similar products by other manufacturers.
- Increased revenue from other activities of the enterprise ( from the sale of fixed assets, other assets of the enterprise, currency values , securities , etc.) .
In this paper we will consider how the main profit factors affect the profit level. To do this we consider a fictional data of two firms: firm A will be a grocery store and firm B will be a luxury store.
Conceptual Model
The data of profit value as a dependent variable and the factors which have an impact on profit as independent variables for both firms is given below:
We draw this on a diagram to visualize the data.
For firm A:
For firm B:
As we may see, the profit of grocery store is not so big compared to the expenses, like in luxury store. This might be explained just by difference in goods categories – a business, based on retail sales of jewelery, is cost-effective and profitable investment.
Regression analysis
We perform the regression analysis for two firms, using linear additive models. We have profit variable as a dependent variable and monthly expenses and income as independent variables.
For firm A:
Regression Analysis: Profit per m versus Average inco; Average expe
The regression equation is
Profit per month, $ = - 260965 + 0,811 Average income, per month, $
+ 0,304 Average expenses, per month, $
Predictor Coef SE Coef T P
Constant -260965 40343 -6,47 0,000
Average income, per month, $ 0,8105 0,2268 3,57 0,001
Average expenses, per month, $ 0,3044 0,2097 1,45 0,158
S = 4253,98 R-Sq = 83,2% R-Sq(adj) = 81,9%
Analysis of Variance
Source DF SS MS F P
Regression 2 2413200926 1206600463 66,68 0,000
Residual Error 27 488601340 18096346
Total 29 2901802266
Source DF Seq SS
Average income, per month, $ 1 2375081097
Average expenses, per month, $ 1 38119829
Unusual Observations
Average
income,
per Profit per
Obs month, $ month, $ Fit SE Fit Residual St Resid
22 378705 120749 129722 1613 -8973 -2,28R
R denotes an observation with a large standardized residual.
The regression equation has the following form:
Profit=-206095+0.8105*Income+0.3044*Expenses
The obtained coefficient of determination R-squared is 83.2%, the adjusted R-squared is 81.9%. This is an evidence of that 81.9% of response variable variance is explained by this model.
The analysis of variance shows us that the model is significant with the level of significance even lower than 0.1%. This is a very good result.
For firm B:
Regression Analysis: Profit per m versus Average inco; Average expe
The regression equation is
Profit per month, $_1 = - 18268 + 0,484 Average income, per month, $_1
+ 0,443 Average expenses, per month, _1
Predictor Coef SE Coef T P
Constant -18268 86328 -0,21 0,834
Average income, per month, $_1 0,4842 0,1582 3,06 0,005
Average expenses, per month, _1 0,4431 0,1567 2,83 0,009
S = 6323,99 R-Sq = 88,0% R-Sq(adj) = 87,1%
Analysis of Variance
Source DF SS MS F P
Regression 2 7891309278 3945654639 98,66 0,000
Residual Error 27 1079806932 39992849
Total 29 8971116209
Source DF Seq SS
Average income, per month, $_1 1 7571550824
Average expenses, per month, _1 1 319758454
Unusual Observations
Average
income, per Profit per
Obs month, $_1 month, $_1 Fit SE Fit Residual St Resid
20 821375 536680 522991 1278 13689 2,21R
R denotes an observation with a large standardized residual.
The regression equation has the following form:
Profit=-18268+0.4842*Income+0.4431*Expenses
The obtained coefficient of determination R-squared is 88.0%, the adjusted R-squared is 87.1%. This is an evidence of that 87.1% of response variable variance is explained by this model.
The analysis of variance shows us that the model is significant with the level of significance even lower than 0.1%. This is a very good result.
However, the significance of constant is very low – p-value is 0.834. This might be evidence, that the model is not full and some significant factors are omitted.
Conclusion and recommendations
Linear regression is used in statistics as a regression model depends ( explain dependent ) variable y from another or several other variables ( factors , covariates , independent variables ) x with a linear function of dependence.
A linear regression model is frequently used and most studied in econometrics. Namely studied the properties of the parameter estimates obtained by different methods under the assumptions about the probability characteristics of the factors and random error model. Limit (asymptotic ) properties of the estimators of nonlinear models are also displayed on the basis of the last approximation linear models . It should be noted that the econometric point of view more important linearity parameters than linearity factors model.
In our research work we have constructed two regression models for two firms working in the area of sales. We conclude that the profitability of both firms might be expressed as a linear dependence of expenses and income, but there also might be some important factors omitted (see the model for firm B). However, both models are significant and may be used to make some forecasts and predictions about future monthly profits.
Referencing and Bibliography
Cohen, J., Cohen P., West, S.G., & Aiken, L.S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. (2nd ed.) Hillsdale, NJ: Lawrence Erlbaum Associates
Björck, Åke (1996). Numerical methods for least squares problems. Philadelphia: SIAM. ISBN 0-89871-360-9.
Draper, N.R.; Smith, H. (1998). Applied Regression Analysis (3rd ed.). John Wiley. ISBN 0-471-17082-8.
Francis Galton. "Regression Towards Mediocrity in Hereditary Stature," Journal of the Anthropological Institute.
Robert S. Pindyck and Daniel L. Rubinfeld (1998, 4h ed.). Econometric Models and Economic Forecasts.
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