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This case includes this document and an Excel document with the On the Go exhibits, which is included in this week's folder REQUIRED Answer the questions below in a separate Word file. Number all answers so that they correspond with the questions below. While you are required to use Excel to do calculations and analyses throughout the case, most of these will be easy to copy and paste into the final Word document. Except as noted below, I will expect all conclusions to be fully documented within the Word documentI will be checking your Excel file, however, to ensure that the calculations were done within Excel and done using proper formulas. You should label your Excel calculation clearly so that I can determin worksheets with the appropriate question numbers. Submit the completed case study using Blackboard by the due date e to which question they are related; you might want to label the NOTE Some of the requirements exhibits provided, while others will require you to do your own additional calculations. Consider using the point values provided for each question as a guide to how much work you should be putting into each question. For example, a and thought than a question worth 1 point below should be quite simple given the data provided in the Excel question worth 5 points should take you more time BACKGROUND INFORMATION On the Go Stores has twenty-three convenience stores located in the Southeast. twenty-three stores are five new stores (no. 1, no. 4, no. 10, no. 13, and no during the year. Operations vary by demographic location and the mix of products sold . 22) that opened selection of store location is based on several factors, such as competition and the c environment of the location. While On the Go attempts to optimize all location The original selections, some stores are in more favorable locations than others. Typically, a store's operations do not change much unless a new product line is introduced, such as selling offering check-cashing services, or selling lottery tickets. The mix of products and services can vary, as they affect the volume of customers as well as the number of full-time employee needed gas Assume you are the Senior auditor for the audit of On the Go Stores. You have been asked to perform various analytical procedures to test for the existence of sales revenue. AWk6RegressionTips.pdf Wk6OnTheGoExhib...s Question 1 Auditors will consider many factors when they develop an estimate of sales revenue for each individual location of On the Go. They will consider both general environmental or industry factors and factors specific to this client. For example, they will probably consider prior year sales as one factor in developing their estimate for current sales. List at least 3 other factors that you would consider when determining your estimate of individual store sales revenue (3 pts) TREND ANALYSIS- AT THE PLANNING STAGE Trend analysis can be used in any analytical procedure, but it is typically more appropriate for the planning phase of an audit. This is because trends are usually considered at a high level and do not take into consideration detailed changes in specific factors Question 2 Consistent with a typical high level analytical procedure that might be used during planning. compare total sales in the current year to total sales in the prior year provided ir the Excel file. Based on the comparison, conclude on the likelihood of a material misstatement in sales revenue (low, moderate, or high) being sure to include appropriate numbers in your discussion. (1 pt) Exhibit 1 of Question 3 The test above does not take into consideration that the number of stores change year. Modify the above test to account for this factor using data provided in Exhibit 1 of the Excel file. Do the calculation/comp numbers here along w total sales revenue (low, moderate, or high) (1 pt) s each arison within Excel, but then copy and paste the relevant ith your conclusion of the likelihood of a material misstatement in Question 4 Now consider individual stores and identify the ones that you believe may be mo risk of having materially misstated sales revenue based on changes in sales revenue from last year to this year (using Exhibit 1). Assume for this test that we have set tolerable misstatement for sales revenue to be $150,000 per store, or an 8% change from the prior year. All calculations can remain in Excel; include only the store numbers here. (2 pt) re or les Question 5 (this is a multi-part question -be sure to answer all parts for each explanation) . Develop four potential explanations for the material differences between your estimate and actual revenues for the stores you identified in Question 4 above as being most likely to have misstatements. Categorize those potential explanations as either error or non-error For each explanation, identify evidence/additional procedures that you would perform to determine if the explanation is the most likely the cause of the difference.1 (6 pts) RATIO ANALYSIS Ratio analysis involves the comparison of relationships between financial statement accounts, a comparison of an account with nonfinancial data, or a comparison of relationships across an industry, such as gross profit comparisons Question 6 Using the data on "All Stores" in Exhibit 2, compare the current year gross profit percentage to prior year gross profit percentage to determine how likely there are to be material misstatements in total sales revenue and the related accounts. Conclude on the likelihood of a material misstatement in sales revenue (low, moderate, or high) being sure to include appropriate numbers in your discussion. Note subtracting two percentages gives you a "change in percentages", it does not give you the percent change. In order to calculate the percent change of anything, you must subtract the two years' numbers and then divide by the prior year. (1 pt) Question 7 Because gasoline is sold at a different mark-up than other types of products, we might expect stores that sell gasoline to have a different gross profit percentage than stores that do not sell gasoline. Therefore, using the additional data in Exhibit 2, compare current year and prior year gross profit percentages for the separate groups of stores. Conclude on which stores might be more or less at risk of having materially misstated sales revenue being sure to include appropriate numbers in your discussion misstatement for this test. (1 pt) . Use 10% as a reasonable tolerable USING NON-FINANCIAL DATA The m store and sales per stores (see Exhibit 3). The region's average sales per square foot was anagement of On the Go Stores has provided you with the amount of square footage per An example of what I am looking for here not related to this case: say that based on a comparison of prior to tify accounts receivable as being potentially overstated by a material amount because the percentage increase was exceptionally high large sale at the end of the current year to a new client. This would be consid true, accounts receivable is stated correctly in the current year and no adjustment would need to b . One potential explanation is that the company had a very ered "non-error" since, assuming it is e made. In order to is explanation is true and AR docs not need an adjustment, one procedure we might do would be to confirm the year-end sale directly with the new client. obtained from information provided by the National Association of Convenience Stores (NACS), which publishes information on the convenience store industry Question 8 Using the industry average sales per square foot and data provided in Exhibit 3, develop an estimate of what you would expect total sales to be for On the Go (not by store). Give consideration as to whether all stores should be included in this "total estimate" Compare your estimate to the actual total sales and conclude on the likelihood there are material misstatements in sales revenue (low, moderate, or high). Do the calculation/comparison within Excel, but then copy and paste the relevant numbers here along with clear documentation of your consideration of which stores should be included and your conclusion. (2 pts) Question 9 Using data provided in Exhibit 3, analyze sales per square foot by store and identify those stores that you believe may be more or less at risk of having materially misstated sales revenue. Use 15% for tolerable misstatement for this test. All calculations can remain in Excel; include only the store numbers here. (1 pt) REGRESSION ANALYSIS Regression analysis has the same objective as trend, ratio analysis, and reasonableness testing to identify the potential for misstatement. The advantage of regression over the other methods is that regression: (a) provides an explicit, mathematically objective, and precise method for forming an expectation; (b) allows the inclusion of a larger number of relevant independent variables; and (c) provides direct and quantitative measures of the precision of the expectation The auditor's specific objective in using regression for On the Go Stores is to determine which stores should be targeted for investigation for potential misstatement in sales. The regression determines which stores have total sales that are most out of line in comparison with the others This type of analysis is called cross-sectional regression. In predicting sales, the o usually includes relevant predictors, such as the size of the store (as used in the reasonableness testing above), and other features that might affect store sales volume Question 10 For each store, we have data on the level of inventory, number of employees, whether it is new, whether it sells gas, and its size. List three additional factors you believe might be potential predictors of sales for On the Go stores. (2 pts) Question 11 Using the data provided in Exhibit 4, perform a regression analysis on the data, and include residuals in your output. Based on these results, which variables are most important in determining sales? You may keep all data within Excel. (If you don't know how to do regression analyses in Excel, see the tips sheet in this week's folder.) (6 pts) 4 Question 12 What do the residuals mean? What does a negative residual mean in terms of potential errors and what does a positive residual mean in term of potential errors? Which is more of a concern to auditors? (2 pts) Question 13 Combine the regression analyses results with those from all of the previous questions and discuss the stores you believe should be investigated further by the auditor because they have the highest likelihood of material misstatement in sales revenue. For example, you might list each of the store numbers and have columns for each of the tests you did. Then mark which ones were identified as at higher risk (use a 5% tolerable misstatement for the regression analysis). Include the table in your Word document, and provide a two sentence discussion of your results. Think critically about this, understanding that any store you highlight will require additional audit hours for investigation. (2 pts) Paste (- Merge & Center Format Painter Clipboard Alignment Number Average Number Prior Year Current Year DollarPercent Current Year Sales Change ChangeInventory Square of Full-Time Feet Employees Sales 10 Store Audited (S) (S) 781,793 781,793 48,725 2,500 1,165,221 1,146,438 (18,783) (1.16) 1,147,430 1,195,004 2,500 37,218 4,000 45,826 4,000 53,862 4,000 ,000 47,016 4,000 59,726 4,000 2,500 37,664 2,500 34,662 2,500 44,782 4,000 38,774 4,000 55,423 4,000 52,884 4,000 46,834 4,000 53,772 4,000 43,982 4,000 44,893 4,000 37,665 4,000 33,826 2,500 44,857 2,500 47,574 951,784 951,784 2,037,4631,981,409 (56,054) (2.75) 2,257,920 2,300,671 42,751 1,850,354 1,916,884 1,799,713 (117,1716.11) 1,833,209 1,820,641 (12,568) (0.69) 1,956,481 106,127 49,883 20 9 774,954 774,954 980,484 1,159,004 178,520 35,882 1,069,652 1,139,475 948,522 948,522 1,795,123 1,984,777 189,654 2,119,015 2,293,847 174,832 1,947,303 1,984,722 37,419 1,705,789 1,798,33692,547 2,396,971 2,484,50387,532 ,901,631 1,837,400 (64,231) (3.38) 1,514,798 1,609,385 94,587 6.24 32 21 33 22 1,874,229 (12,358) (065) 698,333 698,333 1,092,908 1,198,229 105,321 N/A 36 Total 30,618,742 35,719,650 5,100,908 1,038,041 80,000 250.8 Store opened during current year Exhibit1 Exhibit2 Exhibit3 | Exhibit4 cO Wrap Text General Merge & Center-s-% , Paste B 1 u. 0,-. _ Format Painter Clipboard Font Alignment Number M14 AB All Stores Total Sales COGS Current Year Prior Year $31,564,264 $30,618,742 21,463,700 21,987,932 ss Margin $10,100,564 $8,630,810 10 12 13 14 15 16 17 18 19 20 21 Gross Margin % 31 99% 28 19% Current Year Prior Year Sells Gasoline $23,905,477 $23,329,838 Total Sales COGS Gross Margin Gross Margin % 16,112,291 16,307,557 $7,793,186 $7,022,281 30 10% 23 24 25 26 27 28 29 30 31 32 32 60% Current Year Prior Year $7,658,787 $7,288,904 5,351,4095,680,375 Doesn't Sell Gasoline Total Sales COGS Gross Margin $2,307,378 $1,608,529 Gross Margin % 30 10% 22 10% 34 35 36 | Exhibit1 Exhibit2 Exhibit3 Exhibit4 K37 Current Year Sales Sales Avg. Sales per Dollar Percent Square per Sq. Ft. Sq. Ft. per Difference Difference 6 7 Store Feet NACS 781,793 2,500 1,146,438 2,500 1,195,004 2,500 951,784 4,000 1,981,409 4,000 2,300,671 4,000 1,956,481 4,000 1,799,713 4,000 4,000 774,9542,500 1,159,0042,500 1,139,4752,500 4,000 1,984,777 4,000 2,293,847 4,000 4,000 1,798,336 4,000 2,484,5034,000 1,837,400 4,000 490 36 12% 6.33% 2 45% 478 238 495 490 490 490 12 252 (5) (85) 51.43%. (1.02%) (17.35%) 0.20% 8.16% 489 450 455 490 1,820,641 490 490 490 490 464 456 237 496 573 496 36 73% 5.31% 6.94% 51 63% (1.22%) (16.94%) (1.22%)| 948,5224, 253 (6) (83) (6): 490 1,984,722 490 490 490 490 490 490 490 (131) 459 402 469 279 479 (26.73%) 6.33% 17.96% 4.29% 28 20 1,609,3854,0 1,874,229 4,000 698,3332,500 1,198,229 2.500 30 22 43 06% 2.24% 33 Total 35,719,650 80,000 11.270 Store opened during current year Exhibit1 Exhibit2 Exhibit3 Exhibit4 1Exhibit 4 2 On the Go Stores 3 Regression Data (1-4,000 sq. ft. Merchandise Full-Time (1-yes, 0-no) (1-yes, 0-no) 0-2,500 sq.ft.) 7 Store Inventory Employees New Store Sells Gas Size Sales 48,725 44,171 45,714 37,218 45,826 53,862 49,883 47,016 59,726 35,882 37,664 34,662 44,782 38,774 55,423 52,884 46,834 53,772 43,982 44,893 37,665 33,826 44,857 0 781,793 0 1,146,438 0 1,195,004 1 951,784 1 1,981,409 1 2,300,671 1 1,956,481 1 1,799,713 1 1,820,641 0 774,954 0 1,159,004 0 1,139,475 1 948,522 1 1,984,777 1 2,293,847 1 1,984,722 1 1,798,336 1 2,484,503 1 1,837,400 1 1,609,385 1 1,874,229 0 698,333 0 1,198,229 11.31 0 0 0 0 11.86 10.06 12 13 14 10.71 7.5 0 11.2 11.6 12.7 11.86 12.2 11.1 10.4 8.84 12.1 12 13 21 0 0 23 17 18 19 25 26 27 28 7.2 21 10.5 10.9 23 32 Exhibit1 Exhibit2 Exhibit3 Exthibit4 This case includes this document and an Excel document with the On the Go exhibits, which is included in this week's folder REQUIRED Answer the questions below in a separate Word file. Number all answers so that they correspond with the questions below. While you are required to use Excel to do calculations and analyses throughout the case, most of these will be easy to copy and paste into the final Word document. Except as noted below, I will expect all conclusions to be fully documented within the Word documentI will be checking your Excel file, however, to ensure that the calculations were done within Excel and done using proper formulas. You should label your Excel calculation clearly so that I can determin worksheets with the appropriate question numbers. Submit the completed case study using Blackboard by the due date e to which question they are related; you might want to label the NOTE Some of the requirements exhibits provided, while others will require you to do your own additional calculations. Consider using the point values provided for each question as a guide to how much work you should be putting into each question. For example, a and thought than a question worth 1 point below should be quite simple given the data provided in the Excel question worth 5 points should take you more time BACKGROUND INFORMATION On the Go Stores has twenty-three convenience stores located in the Southeast. twenty-three stores are five new stores (no. 1, no. 4, no. 10, no. 13, and no during the year. Operations vary by demographic location and the mix of products sold . 22) that opened selection of store location is based on several factors, such as competition and the c environment of the location. While On the Go attempts to optimize all location The original selections, some stores are in more favorable locations than others. Typically, a store's operations do not change much unless a new product line is introduced, such as selling offering check-cashing services, or selling lottery tickets. The mix of products and services can vary, as they affect the volume of customers as well as the number of full-time employee needed gas Assume you are the Senior auditor for the audit of On the Go Stores. You have been asked to perform various analytical procedures to test for the existence of sales revenue. AWk6RegressionTips.pdf Wk6OnTheGoExhib...s Question 1 Auditors will consider many factors when they develop an estimate of sales revenue for each individual location of On the Go. They will consider both general environmental or industry factors and factors specific to this client. For example, they will probably consider prior year sales as one factor in developing their estimate for current sales. List at least 3 other factors that you would consider when determining your estimate of individual store sales revenue (3 pts) TREND ANALYSIS- AT THE PLANNING STAGE Trend analysis can be used in any analytical procedure, but it is typically more appropriate for the planning phase of an audit. This is because trends are usually considered at a high level and do not take into consideration detailed changes in specific factors Question 2 Consistent with a typical high level analytical procedure that might be used during planning. compare total sales in the current year to total sales in the prior year provided ir the Excel file. Based on the comparison, conclude on the likelihood of a material misstatement in sales revenue (low, moderate, or high) being sure to include appropriate numbers in your discussion. (1 pt) Exhibit 1 of Question 3 The test above does not take into consideration that the number of stores change year. Modify the above test to account for this factor using data provided in Exhibit 1 of the Excel file. Do the calculation/comp numbers here along w total sales revenue (low, moderate, or high) (1 pt) s each arison within Excel, but then copy and paste the relevant ith your conclusion of the likelihood of a material misstatement in Question 4 Now consider individual stores and identify the ones that you believe may be mo risk of having materially misstated sales revenue based on changes in sales revenue from last year to this year (using Exhibit 1). Assume for this test that we have set tolerable misstatement for sales revenue to be $150,000 per store, or an 8% change from the prior year. All calculations can remain in Excel; include only the store numbers here. (2 pt) re or les Question 5 (this is a multi-part question -be sure to answer all parts for each explanation) . Develop four potential explanations for the material differences between your estimate and actual revenues for the stores you identified in Question 4 above as being most likely to have misstatements. Categorize those potential explanations as either error or non-error For each explanation, identify evidence/additional procedures that you would perform to determine if the explanation is the most likely the cause of the difference.1 (6 pts) RATIO ANALYSIS Ratio analysis involves the comparison of relationships between financial statement accounts, a comparison of an account with nonfinancial data, or a comparison of relationships across an industry, such as gross profit comparisons Question 6 Using the data on "All Stores" in Exhibit 2, compare the current year gross profit percentage to prior year gross profit percentage to determine how likely there are to be material misstatements in total sales revenue and the related accounts. Conclude on the likelihood of a material misstatement in sales revenue (low, moderate, or high) being sure to include appropriate numbers in your discussion. Note subtracting two percentages gives you a "change in percentages", it does not give you the percent change. In order to calculate the percent change of anything, you must subtract the two years' numbers and then divide by the prior year. (1 pt) Question 7 Because gasoline is sold at a different mark-up than other types of products, we might expect stores that sell gasoline to have a different gross profit percentage than stores that do not sell gasoline. Therefore, using the additional data in Exhibit 2, compare current year and prior year gross profit percentages for the separate groups of stores. Conclude on which stores might be more or less at risk of having materially misstated sales revenue being sure to include appropriate numbers in your discussion misstatement for this test. (1 pt) . Use 10% as a reasonable tolerable USING NON-FINANCIAL DATA The m store and sales per stores (see Exhibit 3). The region's average sales per square foot was anagement of On the Go Stores has provided you with the amount of square footage per An example of what I am looking for here not related to this case: say that based on a comparison of prior to tify accounts receivable as being potentially overstated by a material amount because the percentage increase was exceptionally high large sale at the end of the current year to a new client. This would be consid true, accounts receivable is stated correctly in the current year and no adjustment would need to b . One potential explanation is that the company had a very ered "non-error" since, assuming it is e made. In order to is explanation is true and AR docs not need an adjustment, one procedure we might do would be to confirm the year-end sale directly with the new client. obtained from information provided by the National Association of Convenience Stores (NACS), which publishes information on the convenience store industry Question 8 Using the industry average sales per square foot and data provided in Exhibit 3, develop an estimate of what you would expect total sales to be for On the Go (not by store). Give consideration as to whether all stores should be included in this "total estimate" Compare your estimate to the actual total sales and conclude on the likelihood there are material misstatements in sales revenue (low, moderate, or high). Do the calculation/comparison within Excel, but then copy and paste the relevant numbers here along with clear documentation of your consideration of which stores should be included and your conclusion. (2 pts) Question 9 Using data provided in Exhibit 3, analyze sales per square foot by store and identify those stores that you believe may be more or less at risk of having materially misstated sales revenue. Use 15% for tolerable misstatement for this test. All calculations can remain in Excel; include only the store numbers here. (1 pt) REGRESSION ANALYSIS Regression analysis has the same objective as trend, ratio analysis, and reasonableness testing to identify the potential for misstatement. The advantage of regression over the other methods is that regression: (a) provides an explicit, mathematically objective, and precise method for forming an expectation; (b) allows the inclusion of a larger number of relevant independent variables; and (c) provides direct and quantitative measures of the precision of the expectation The auditor's specific objective in using regression for On the Go Stores is to determine which stores should be targeted for investigation for potential misstatement in sales. The regression determines which stores have total sales that are most out of line in comparison with the others This type of analysis is called cross-sectional regression. In predicting sales, the o usually includes relevant predictors, such as the size of the store (as used in the reasonableness testing above), and other features that might affect store sales volume Question 10 For each store, we have data on the level of inventory, number of employees, whether it is new, whether it sells gas, and its size. List three additional factors you believe might be potential predictors of sales for On the Go stores. (2 pts) Question 11 Using the data provided in Exhibit 4, perform a regression analysis on the data, and include residuals in your output. Based on these results, which variables are most important in determining sales? You may keep all data within Excel. (If you don't know how to do regression analyses in Excel, see the tips sheet in this week's folder.) (6 pts) 4 Question 12 What do the residuals mean? What does a negative residual mean in terms of potential errors and what does a positive residual mean in term of potential errors? Which is more of a concern to auditors? (2 pts) Question 13 Combine the regression analyses results with those from all of the previous questions and discuss the stores you believe should be investigated further by the auditor because they have the highest likelihood of material misstatement in sales revenue. For example, you might list each of the store numbers and have columns for each of the tests you did. Then mark which ones were identified as at higher risk (use a 5% tolerable misstatement for the regression analysis). Include the table in your Word document, and provide a two sentence discussion of your results. Think critically about this, understanding that any store you highlight will require additional audit hours for investigation. (2 pts) Paste (- Merge & Center Format Painter Clipboard Alignment Number Average Number Prior Year Current Year DollarPercent Current Year Sales Change ChangeInventory Square of Full-Time Feet Employees Sales 10 Store Audited (S) (S) 781,793 781,793 48,725 2,500 1,165,221 1,146,438 (18,783) (1.16) 1,147,430 1,195,004 2,500 37,218 4,000 45,826 4,000 53,862 4,000 ,000 47,016 4,000 59,726 4,000 2,500 37,664 2,500 34,662 2,500 44,782 4,000 38,774 4,000 55,423 4,000 52,884 4,000 46,834 4,000 53,772 4,000 43,982 4,000 44,893 4,000 37,665 4,000 33,826 2,500 44,857 2,500 47,574 951,784 951,784 2,037,4631,981,409 (56,054) (2.75) 2,257,920 2,300,671 42,751 1,850,354 1,916,884 1,799,713 (117,1716.11) 1,833,209 1,820,641 (12,568) (0.69) 1,956,481 106,127 49,883 20 9 774,954 774,954 980,484 1,159,004 178,520 35,882 1,069,652 1,139,475 948,522 948,522 1,795,123 1,984,777 189,654 2,119,015 2,293,847 174,832 1,947,303 1,984,722 37,419 1,705,789 1,798,33692,547 2,396,971 2,484,50387,532 ,901,631 1,837,400 (64,231) (3.38) 1,514,798 1,609,385 94,587 6.24 32 21 33 22 1,874,229 (12,358) (065) 698,333 698,333 1,092,908 1,198,229 105,321 N/A 36 Total 30,618,742 35,719,650 5,100,908 1,038,041 80,000 250.8 Store opened during current year Exhibit1 Exhibit2 Exhibit3 | Exhibit4 cO Wrap Text General Merge & Center-s-% , Paste B 1 u. 0,-. _ Format Painter Clipboard Font Alignment Number M14 AB All Stores Total Sales COGS Current Year Prior Year $31,564,264 $30,618,742 21,463,700 21,987,932 ss Margin $10,100,564 $8,630,810 10 12 13 14 15 16 17 18 19 20 21 Gross Margin % 31 99% 28 19% Current Year Prior Year Sells Gasoline $23,905,477 $23,329,838 Total Sales COGS Gross Margin Gross Margin % 16,112,291 16,307,557 $7,793,186 $7,022,281 30 10% 23 24 25 26 27 28 29 30 31 32 32 60% Current Year Prior Year $7,658,787 $7,288,904 5,351,4095,680,375 Doesn't Sell Gasoline Total Sales COGS Gross Margin $2,307,378 $1,608,529 Gross Margin % 30 10% 22 10% 34 35 36 | Exhibit1 Exhibit2 Exhibit3 Exhibit4 K37 Current Year Sales Sales Avg. Sales per Dollar Percent Square per Sq. Ft. Sq. Ft. per Difference Difference 6 7 Store Feet NACS 781,793 2,500 1,146,438 2,500 1,195,004 2,500 951,784 4,000 1,981,409 4,000 2,300,671 4,000 1,956,481 4,000 1,799,713 4,000 4,000 774,9542,500 1,159,0042,500 1,139,4752,500 4,000 1,984,777 4,000 2,293,847 4,000 4,000 1,798,336 4,000 2,484,5034,000 1,837,400 4,000 490 36 12% 6.33% 2 45% 478 238 495 490 490 490 12 252 (5) (85) 51.43%. (1.02%) (17.35%) 0.20% 8.16% 489 450 455 490 1,820,641 490 490 490 490 464 456 237 496 573 496 36 73% 5.31% 6.94% 51 63% (1.22%) (16.94%) (1.22%)| 948,5224, 253 (6) (83) (6): 490 1,984,722 490 490 490 490 490 490 490 (131) 459 402 469 279 479 (26.73%) 6.33% 17.96% 4.29% 28 20 1,609,3854,0 1,874,229 4,000 698,3332,500 1,198,229 2.500 30 22 43 06% 2.24% 33 Total 35,719,650 80,000 11.270 Store opened during current year Exhibit1 Exhibit2 Exhibit3 Exhibit4 1Exhibit 4 2 On the Go Stores 3 Regression Data (1-4,000 sq. ft. Merchandise Full-Time (1-yes, 0-no) (1-yes, 0-no) 0-2,500 sq.ft.) 7 Store Inventory Employees New Store Sells Gas Size Sales 48,725 44,171 45,714 37,218 45,826 53,862 49,883 47,016 59,726 35,882 37,664 34,662 44,782 38,774 55,423 52,884 46,834 53,772 43,982 44,893 37,665 33,826 44,857 0 781,793 0 1,146,438 0 1,195,004 1 951,784 1 1,981,409 1 2,300,671 1 1,956,481 1 1,799,713 1 1,820,641 0 774,954 0 1,159,004 0 1,139,475 1 948,522 1 1,984,777 1 2,293,847 1 1,984,722 1 1,798,336 1 2,484,503 1 1,837,400 1 1,609,385 1 1,874,229 0 698,333 0 1,198,229 11.31 0 0 0 0 11.86 10.06 12 13 14 10.71 7.5 0 11.2 11.6 12.7 11.86 12.2 11.1 10.4 8.84 12.1 12 13 21 0 0 23 17 18 19 25 26 27 28 7.2 21 10.5 10.9 23 32 Exhibit1 Exhibit2 Exhibit3 Exthibit4