Revenue Recognition and a Matching Concept - 1159 Words

Revenue Recognition Matching Concept Part I Revenue recognition is a significant issue in accounting because of integrity and fairness in the financial statements that are depended upon by investors, creditors, and other financial statement users. When revenue is not properly recognized in financial statements, material misstatements can occur, which misleads users. Even though the matching principle is not the same as revenue recognition, it is related in the sense of matching expenses spent to revenues earned from the expenses, or revenues to expenses that generated the revenues. Revenue recognition is a primary cause of financial statement restatements, are related to fraudulent behavior, cause market value and capitalization drops, and cause higher enforcement efforts to ensure fairness and integrity in financial statements (Kieso, Weygandt, Warfield, 2008). Most fraud cases that involve company executives involve revenue recognition is some manner, whether to steal company money and hide the actions or in efforts to meet financial analysts expectations to maintain stock prices and still maintain job positions and bonuses. Each case has led to material misstatements where financial statements had to be restated with SEC, caused market and capitalization drops for the companies, and caused higher enforcement efforts by SEC. And, each case has effected financial statement users and has caused uncertainties in minds of investors, which compromise the companies. 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In the words of Nikolai, Bazley andRead MoreAssets, Liability, Owners Equity and the Accounting Concepts1211 Words   |  5 Pageskeeping the books of business, a company must decide the amount to for assets purchased and liabilities incurred. The amount that a company should record assets and liabilities are at the historical cost principle. Historical cost principle is a simple concept which means that the data you see on the balance sheet is recorded at the historical cost. The historical cost is therefore the cost at the time the company or entity com pleted the transaction. Historical cost accounting is therefore the oppositeRead MoreThe International Accounting Standards Board Essay1506 Words   |  7 PagesUS-GAAP  include  (1) entity  concept, (2) going concern, (3) matching principle, (4) conservatism,  (5)  cost principle, (6) objective evidence, (7) materiality, (8)  consistency and (9)  full disclosure, which are explained in detail below.   1) Entity is defined as the â€Å"person or organization that is the focus of attention†Ã‚  (Finkler et al., 2013, p. 103). While a HCO may be the entity, there are sub-entities  comprised of individual units or service lines.   The entity concept indicates that  there shouldRead MoreAccrual Accounting : An Accounting Method1285 Words   |  6 Pagesregardless of when cash transactions occur. They are documented by matching revenues to expenses at the time in which the transaction occurs rather than when a payment is processed. 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Ever thought youd see car parts transformed into...

Ever thought youd see car parts transformed into jewellery? Or clothing? Shoes? Eye wear? Well that’s exactly what the Lexus Fashion Workshop did. The Lexus CT Hybrid is a gorgeous, sleek car. Which is also 90% recyclable, so Lexus decided to dismantle a Lexus CT Hybrid and bring in four fashion designers, and see how they would use pieces of the car, and up cycle them into amazing bits of work. Fashion designer John Patrick, used floor mats made from plant-based plastic, sustainable sound-dampening material, wire harness, leather seat covers and cargo covers, to create a stunning grey trench coat, a fabulous pair of shorts, and a simple clutch bag, and named it Nomadic Sanctuary. John Patrick is a interesting fashion designer who†¦show more content†¦The Environmental Crown of Virtue was created by Moss Lipow using transmission started and exhaust manifold gasket, to create a fascinating, unique eye piece, which has instead of 2 lenses has four and does not only rest upon the ears, but has a head piece connected. Moss Lipow realized his destiny to be an eye wear designer in his teens when he discovered he needed prescription glasses, although he actually studied film before finally settling on his career as an eye wear designer. One of his designs, framed in ostrich and alligator leather was priced at $3,800 and has become one of the most expensive pairs of sunglasses in the world, he also claims to have the largest collection of vintage 20th century eye wear in the world, with a collection of over 3,000 different frames. Finally, Alejandro Ingelmo, shoe designer created The Lunar shoe. Using the armrest leather trim, tire tread and the clear plastic tubing used for wiper fluid, to create an out of this world pair of shoes. Black and silver and absolutely stunning, these shoes where more than a pair of well designed shoes, they were a piece of art. Alejandro Ingelmo followed in his great GrandfathersShow MoreRelatedMaybelline1247 Words   |  5 PagesIndia. Now HUL (Hindustan Unilever Limited) has about 110 salons all over India providing beauty services. Lakme has been ranked as 47th most trusted brand in India by  The Brand Trust Report[2]  The company is the title sponsor for  Lakme Fashion Week  (LFW) a bi-annual fashion event which takes place in Mumbai.[3] Maybelline The Maybelline Company was created by lanaya New York chemist T.L. Williams in 1915. Williams, in his early 30s noticed his younger sister applying a mixture of  Vaseline  andRead MoreEmotion Detection Using Sobel Filtering And Retrieving With Sparse Code Words2097 Words   |  9 Pagesetc.To improve content based image retrieval we use attribute enhanced sparse codewords to retrieve similar images from the database.To evaluate the performance of these methods, we conduct extensive experiments on separate public datasets named LFW. These datasets contain faces taken in unconstrained environment. To sum up the contribution of this work, First, it takes an image, then by skin color segmentation, it detects human skin color, then it detect human face. Then it separates the eyesRead MoreThe Family And Medical Leave Act2286 Words   |  10 PagesOn the other hand, there have been some negative feedback for those employees who do not request FMLA. In the medical standpoint of FMLA, issues have arrived due to the lack of communication between employee and employer. Examples such as Burnett v. LFW, Inc., can shows these issues of communication that could interfere when it comes for leave when taking care of a child. Burnett v. LWF, Inc. to explain how an employer’s responsibility to undergo any eligibility for FMLA leave. A lawsuit was created

Intermediate Quantitative Methods for Accounting†MyAssignmenthelp

Question: Discuss about the Intermediate Quantitative Methods for Accounting. Answer: Introduction This paper discusses the use of statistical analysis techniques in business reporting. The International Union of Accountants belongs to a body of accounting firms all over the globe that seeks to improving corporate governance. This paper seeks to report on the environmental accounting compliance of different accounting firms across the globe. The accounting firms are under the control of the Accounting Director of International Union of Accountants and therefore the data is readily available. The variables under investigation in this paper include environmental accounting compliance scores for this year, last year, and a year before last year, percentage of accountants in the selected firms with at least an accounting degree and type of country for each accounting firm. The environmental accounting scores are represented as an average, this is because average is the best measure in descriptive analysis to represent quantitative data, percentage of accountants in different accounting firms is represented as categorical variable where 1 = less than 50% of accountants have an accounting degree and 2 = at least 50% of the accountants have an accounting degree, and the type of country is presented as ordinal variable where 1 = first word countries, 2 = second world countries, and 3 = third world countries(Bhat 2010). The statistical analysis involved in this paper are inferential statistics. Statistical inference is a process which information is acquired about populations from the samples(Cameron 2013). Hypothesis testing and estimation are the two major procedures in making inferences. In estimation, the main objective is determining the value of population parameter based on sample statistic(Carlberg 2014). The process involved in estimation include identification of parameter being estimated, specification of the parameters estimator and the sampling distribution, and construction of interval estimator. In hypothesis testing the main purpose is determining whether there is significant evidence favoring a certain belief about parameters(Chatterjee 2013). Under hypothesis testing we have null and alternative hypothesis and the main objective is reject or not to reject the null hypothesis. Rejecting the null hypothesis refers to that there is substantial or significant evidence to conclude the alternative hypothesis is true. Failing to reject the null hypothesis we conclude there is no significant evidence to support the alternative hypothesis(Cramer 2007). The steps involved in testing hypothesis are as follows: Setting up the null and alternative hypothesis denoted as H0 and H1 Determination of the test statistic and the sampling distribution. Specification of the significance level, usually set as 1%, 5%, or 10% Definition of the decision rule Calculating value of the test statistic Conclusion from the results. Finally from the results that will be obtained, a conclusion will be reached on specific research questions that the Director wished to address, summarizing and discussion of the limitations will also be addressed. Is the percentage of accountants in each firm who has at least one university degree in accounting related to country type? The test statistic to be used in this research question is the Chi-Square test of independence and the Cramers V value(Elliott 2007). The Chi-Square test of independence is a statistical test that is used in determining with there is a significant relationship between two nominal or categorical variables(Heckard 2012). In our case the variables under investigation are country type and percentage of accountants with at least one accounting degree and both are categorical variables. The population of interest in this research question is the percentage of accountants with at least one university degree in accounting and the type of country an accountant is from(Connor n.d.). Following the steps in testing hypothesis, Setting up the null and alternative hypothesis denoted as H0 and H1 respectively. H0: The percentage of accountants with at least one accounting degree and the type of country an accountant is from are independent or there is no significant relationship between percentage of accountants with at least one accounting degree and the type of country an accountant is from. H1: The percentage of accountants with at least one accounting degree and the type of country an accountant is from are dependent or there is a significant relationship between percentage of accountants with at least one accounting degree and the type of country an accountant is from. Determination of the test statistic and the sampling distribution. The test statistic to be used is the Chi-Square test of independence. This the most appropriate test statistic to use because the variables under investigation are both nominal(Epsetein n.d.). Specification of the significance level, usually set as 1%, 5%, or 10% The test will be tested at both 1% and 5% level of significance. Definition of the decision rule The decision rule used in this test is should the p-value ( defined as the smallest value of that leads to rejection of the null hypothesis) be less than the level of significance the null hypothesis will be rejected(Cramer, Advanced Quantitative Data Analysis 2007). Calculating value of the test statistic Table 1 Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Acc-Dgr% at time t * Country classification 60 100.0% 0 .0% 60 100.0% Table 1 above represents the total number of firms under study. Therefore, from the results above, we had 60 firms and none of the firms had a missing data. Acc-Dgr% at time t * Country classification Crosstabulation Count Country classification Total First World Countries Second World Countries Third World Countries Acc-Dgr% at time t Less than 50% 2 7 21 30 At least 50% 13 12 5 30 Total 15 19 26 60 Table 2 above summarizes both variables under study. Firms with less than 50% of accountants who had an accounting degree and those with at least 50% were at par with 30 each. Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 19.229a 2 .000 Likelihood Ratio 20.933 2 .000 Linear-by-Linear Association 18.389 1 .000 N of Valid Cases 60 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 7.50. Table 3 above represents the Chi-Square tests results of the variables under study. The p-value 0.0000 is less than the level of significance at both 1% and 5%. Therefore, we fail to accept the null hypothesis and conclude that the percentage of accountants with at least one accounting degree and the type of country an accountant is from are dependent or there is a significant relationship between percentage of accountants with at least one accounting degree and the type of country an accountant is from. Symmetric Measures Value Asymp. Std. Errora Approx. Tb Approx. Sig. Nominal by Nominal Phi .566 .000 Cramer's V .566 .000 Interval by Interval Pearson's R -.558 .097 -5.125 .000c Ordinal by Ordinal Spearman Correlation -.564 .098 -5.200 .000c N of Valid Cases 60 a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. c. Based on normal approximation. Table 4 indicates the measure of association between the percentage of accountants with at least one accounting degree and the type of country an accountant is from, this is represented as Cramers V(Lee n.d.). Conclusion from the results. Results obtained from the Chi-Square test of independence reveal that there is an association between the percentage of accountants with at least one accounting degree and the type of country an accountant is from(Stott 2010). This can be interpreted as at least 50% of accountants from first world countries have an accounting degree while less than 50% of accountants in third world countries have an accounting degree. The relationship between the percentage of accountants with at least one accounting degree and the type of country an accountant is from is strong as this is depicted by the Cramers V of 0.566(Levin 2010). Therefore, we can conclude that the percentage of accountants in each firm who has at least one university degree in accounting is related to country type(Triola, Elementary Statistics using Excel 2010). This is well illustrated in the column chart below Is there evidence of different environmental compliance scores (EAC) over the three-year period The test statistic to be used in this research question is the one-way analysis of variance which is an extension of the two-sample t test(Liu 2012). This test is used when there are several means to be compared. If the ANOVA indicates that the means are not equal, this means that the means are an unequal and a post hoc analysis on the means can be carried out to rank the difference in means(Moy n.d.). The population of interest in this research question is the average of environmental accounting compliance scores of the accounting firms under investigation(Donnelly 2013). Following the steps in testing hypothesis Setting up the null and alternative hypothesis denoted as H0 and H1 respectively. The hypothesis being tested in this research question is as follows: H0: There is no significant difference in means of the environmental accounting compliance scores over the three year period of study. H1: There is a significant difference in means of the environmental accounting compliance scores over the three year period of study. Before deciding on which test statistic to use, it is wise to test for normality of the data in order to know whether the data follows normality or not(Ryan 2012). Data that follows normal distribution allows parametric distribution techniques to be used in data analysis while data that fails to follow a normal distribution assumes non-parametric techniques of analysis. Since the sample size is greater than 20, Shapiro-Wilk test will be used to test for normality, if the sample size was less than 20, Kolmogorov-Smirnov would have been used(Spicer 2005). Tests of Normality Country classification Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. EAC-Scrs this year (t) First World Countries .129 15 .200* .923 15 .214 Second World Countries .101 19 .200* .966 19 .694 Third World Countries .110 26 .200* .960 26 .400 EAC-Scrs last year (t-1) First World Countries .129 15 .200* .928 15 .256 Second World Countries .164 19 .191 .922 19 .125 Third World Countries .150 26 .138 .973 26 .689 EAC-Scrs the year before last year (t-2) First World Countries .142 15 .200* .956 15 .628 Second World Countries .131 19 .200* .929 19 .166 Third World Countries .120 26 .200* .978 26 .840 a. Lilliefors Significance Correction *. This is a lower bound of the true significance. Table 1 above represents the results for testing normality, all the p-values for environment accounting compliance scores in all the three years is greater than the level of significance at 1% and 5%. Therefore, we fail to reject the null hypothesis and conclude that the data follows normality(Triola 2010). The test statistic to be used will therefore be a parametric distribution. This is also explained in the histograms below: Determination of the test statistic and the sampling distribution. The test statistic to be used is the Analysis of Variance. This the most appropriate test statistic to use because we have more than two means to compare. Specification of the significance level, usually set as 1%, 5%, or 10% The test will be tested at both 1% and 5% level of significance. Definition of the decision rule The decision rule used in this test is should the p-value ( defined as the smallest value of that leads to rejection of the null hypothesis) be less than the level of significance the null hypothesis will be rejected. Rejecting the null hypothesis is an indication that there is mean from one group that is not equal to the rest of the means. Calculating value of the test statistic Table 2 above indicates the ANOVA results of the environmental accounting compliance scores for the past three years. The p-value in all three years is less than the significance level at 1% and 5%. Therefore, we fail to accept the null hypothesis and conclude that there is a significant difference in means of the environmental accounting compliance scores across the three years. Conclusion from the results. From the results obtained in the ANOVA we can comfortably conclude that there is sufficient evidence of different environmental compliance scores (EAC) over the three-year period. This means that across the three year period different accounting firms had different environmental accounting compliance scores. Is there evidence to suggest that the environmental compliance scores (EAC) for this year (time t) is greater than two years ago (time t-2) among the 60 selected accounting firms? The test statistic to be used in this research question will be one sample t-test. This is because we are comparing two means of environmental compliance scores for this year (time t) and that of two years ago (time t-2). Since we are not comparing whether the two means are equal but rather whether the environmental compliance scores for this year (time t) is greater than that of two years ago, the test will be one tailed and not two tailed test(Terrell 2012). The population of interest in this research question is the average of environmental accounting compliance scores of the accounting firms under investigation for this year and those of two years ago. Following the steps in testing hypothesis Setting up the null and alternative hypothesis denoted as H0 and H1 respectively. The hypothesis being tested in this research question is as follows: H0: There is no significant difference in means of the environmental accounting compliance scores for this year and those of two years ago. H1: The environmental compliance scores for this year are greater than those of two years ago. This hypothesis can be re-written as H0: 0 = 1 = 0 H1: 0 1 Determination of the test statistic and the sampling distribution. The test statistic to be used is the one sample t-test. This the most appropriate test statistic to use because we are comparing two means. Specification of the significance level, usually set as 1%, 5%, or 10% The test will be tested at both 1% and 5% level of significance. Calculating value of the test statistic One-Sample Test Test Value = 0 t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper EAC-Scrs this year (t) 55.279 59 .000 62.8717 60.596 65.147 EAC-Scrs the year before last year (t-2) 50.048 59 .000 59.7050 57.318 62.092 Table 1 above represents the results from one sample t-test. The p-value is less than the significance level at both 1% and 5% and indicates a positive t-statistic. Therefore, we fail to accept the null hypothesis and conclude that this years environmental compliance scores is greater than that of two years ago(Topics in Applied Statistics 2016). Conclusion from the results Results from the t-test indicates that this years is environmental compliance scores are greater than those of two years ago. This can be explained in the positive t-test statistic obtained from the results. If the t-test statistic would be negative, we would have concluded that the environmental compliance score for this year is less than that of two years ago. Is there evidence to suggest that this year environmental compliance scores (EAC) of accounting firms which has less than 50% accountants with at least one university degree in accounting is lower than this year environmental compliance scores (EAC) of accounting firms which has at least 50% accountants with at least one university degree in accounting? The test statistic to be used in this research question will be the independent t-test. This is because we are comparing the mean of environmental compliance scores of this year (time t) of accounting firms that have less than 50% accountants with at least one university degree in accounting whether is lower than this years environmental compliance scores of accounting firms which has at least 50% accountants with at least one university degree in accounting(Newbold 2013). Since we are not comparing whether the two means are equal but rather whether the environmental compliance scores for this year (time t) is greater than that of two years ago, the test will be one tailed and not two tailed test. The grouping factor in this test will be accounting degree at time t. The population of interest in this research question is the average of environmental accounting compliance scores of the accounting firms under investigation for this year and the percentage of accountants who have at least one university degree in accounting. Following the steps in testing hypothesis Setting up the null and alternative hypothesis denoted as H0 and H1 respectively. The hypothesis being tested in this research question is as follows: H0: There is no significant difference in the mean of environmental compliance scores of this year (time t) of accounting firms that have less than 50% accountants with at least one university degree in accounting and this years environmental compliance scores of accounting firms which has at least 50% accountants with at least one university degree in accounting. H1: The mean of environmental compliance scores of this year (time t) of accounting firms that have less than 50% accountants with at least one university degree in accounting whether is lower than this years environmental compliance scores of accounting firms which has at least 50% accountants with at least one university degree in accounting. This hypothesis can be re-written as H0: 0 = 1 = 0 H1: 0 1 Determination of the test statistic and the sampling distribution. The test statistic to be used is the independent t-test. This the most appropriate test statistic. Specification of the significance level, usually set as 1%, 5%, or 10%. The test will be tested at both 1% and 5% level of significance. Calculating value of the test statistic Group Statistics Acc-Dgr% at time t N Mean Std. Deviation Std. Error Mean EAC-Scrs this year (t) dimension1 Less than 50% 30 58.750 7.9148 1.4450 At least 50% 30 66.993 7.7532 1.4155 Table 1 represents the descriptive statistics of this years environmental compliance scores grouped by the percentage of accountants who have at least one university degree in accounting. Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference EAC-Scrs this year (t) Equal variances assumed 0.01 0.9 -4.08 58 0 -8.2433 2.0228 Equal variances not assumed -4.08 57.98 0 -8.2433 2.0228 Table 2 above indicates the t-test statistic between mean difference of environmental compliance scores of this year (time t) of accounting firms that have less than 50% accountants with at least one university degree in accounting and this years environmental compliance scores of accounting firms which has at least 50% accountants with at least one university degree in accounting. Since we have a negative t-test statistic, we can conclude that there is evidence to suggest that this year environmental compliance scores (EAC) of accounting firms which has less than 50% accountants with at least one university degree in accounting is lower than this year environmental compliance scores (EAC) of accounting firms which has at least 50% accountants with at least one university degree in accounting. Bibliography Bhat, K. S. 2010. Quantitative analysis for business decisions. Mumbai: Himalaya Pub. Cameron, A. C. And Trivedi, P.K. 2013. Regression analysis of count data. Cambridge: Cambridge University Press. Carlberg, C. 2014. Statistical Analysis. Carlberg: Upper Saddle River. Chatterjee, S. And Simonoff, J. S. 2013. Handbook of regression anaysis. Hoboken: Wiley Sons. Connor, G., Goldberg, L. R. And korajczyk, R. A. n.d. Portfolio risk analysis. Cramer, D. 2007. Advanced Quantitative Data Analyis. Maidenhead: McGraw-Hill International (UK) Ltd. Advanced Quantitative Data Analysis. Maidenhead: McGraw-Hill International (UK) Ltd. Donnelly, R. A. 2013. Business Statistics. Boston: Pearson. Elliott, A. C. And Woodward, W. A. 2007. Statistical analysis quick reference guidebook. Thousand Oaks: Sage Publications. Epsetein, M. J. And Lee, J. Y. n.d. Advances in management accounting. Epstein. Heckard, R. F., Utts, J. M. And Utts, J. M. 2012. Statistics. Australia: Cengage Learning. Lee, C. F. And Lee, J. C. n.d. Handbook of financial econometrics and statistics. Levin, J., Fox, J. A. And Forde, D. R. 2010. Elementary statistics in social research. Boston: Allyn Bacon. Liu, M. 2012. Power and sample size for some chi-square goodness of fit tests. Liu, Mozhu. Moy, R. L., Chen, L. And Kao, L. J. n.d. Study guide for statistics for business and finacial economics. Newbold, P., Carlson, W. L. And Thorne, B. 2013. Statistics for business and economics. Essex: Pearson Education. Ryan, T. P. 2012. Modern regression methods. New York: Wiley. Spicer, J. 2005. Making sense of multivariate data analysis. Thousand Oaks: Sage Publications. Stott, J. R., Truman, M., Lymer, A. And Azmat, N. 2010. Basic accounting. Abingdon: Bookpoint Ltd. Terrell, S. R. 2012. Statistics Translated. New York: Guilford Press. Topics in Applied Statistics. Springer Verlag. Triola, M. F. 2010. Elementary Statistics. Upper Saddle River: Perason Education. 2010. Elementary Statistics using Excel. Boston: Addision-Wesley.