1. Problem 1
For the examination of the financial statements of Scotia Inc., Rosa Schellenberg, a public accountant, has decided to apply non-statistical audit sampling in the tests of sales transactions. Based on her knowledge of Scotia’s operations in the area of sales, she decides that the estimated population deviation rate is likely to be 3 percent and that she is willing to accept a 5 percent risk the true population rate is not greater than 6 percent. Given this information, Rosa selects a random sample of 150 sales invoices from the 5,000 prepared during the year and examines them for exceptions. She notes the following exceptions in her working papers. There is no other documentation.
REQUIRED
Which of the invoices in the table should be defined as an exception?
Explain why it is inappropriate to set a single acceptable TER and EPER for the combined exceptions.
State the appropriate analysis of exceptions for each of the exceptions in the sample.
Invoice No:
Comment
5028
Sales invoice had incorrect price, but a subsequent credit was sent out as a correction.
6791
Voided sales invoice examined by auditor.
6810
Shipping document for a sale of merchandise could not be located.
7364
Sales invoice for $2,875 has not been collected and is six months past due.
7625
Client unable to locate the printed duplicate copy of the sales invoice.
8431
Invoice was dated three days later than the date of the shipping document.
8528
Customer purchase order is not attached to the duplicate sales invoice.
8566
Billing is for $100 less than it should be due to a pricing error.
8780
9169
Credit is not authorized, but the sale was only for $7.65.

2. Solution a.
Invoice Number
Exception?
Type of Exception
5028 No
Error was detected and corrected by client.
6791
Sales invoice was voided.
6810
Yes
Proof of shipment not presented.
7364
Credit collection problem; should be noted for review of allowance for doubtful accounts.
7625
Duplicate sales invoice not properly filed.
8431
Invoices not recorded by proper date; represents potential cutoff problem.
8528
Customer orders not included in invoice package to verify compliance with the order.
8566
Error in pricing. No internal verification.
8780
9169
Credit not authorized.
b. It is inappropriate to set a single acceptable tolerable exception rate and estimated population exception rate for the combined errors because each attribute has a different significance to the auditor and should be considered separately in analyzing the results of the test.

3. c. For each exception, the auditor should check with the controller to determine his explanation for the cause. In addition, the appropriate analysis for each type of exception is as follows:
Invoice No.
Deviation Analysis
6810
Confirm the account balances to the customer; examine the reduction in the perpetual inventory records.
7625
Trace the amount to the sales journal and subsidiary ledger; examine the shipping document and recompute the sale amount. Ask the client to reprint the invoice if the full document is available in the automated system.
8431
Determine who recorded the invoice and check several others prepared by him or her to determine if the error consistently occurs. Increase cut-off testing around year-end work.
8528
Examine subsidiary ledger for subsequent cash receipts; examine sales invoices for other sales invoices to the same customer to determine if customer orders were attached.
8566
Check the price on other invoices to the same customer. Check the price on other invoices that have the same product.
8780
See 7625
9169
Check credit history of customer and evaluate collectability of the customer’s account.

4. Problem 2 note: good segregation of duties has the receptionist prepare a prelisting of cash receipts when they are received in the mail
You have been asked to do planning for statistical testing of the audit of cash receipts. Following is a partial audit program for the audit of cash receipts.
Review the cash receipts journal for large and unusual transactions.
Trace entries from the prelisting of cash receipts to the cash receipts journal to determine whether each is recorded.
Compare customer name, date, and amount on the prelisting with the cash receipts journal.
Examine the related remittance advice for entries selected from the prelisting to determine whether cash discounts were approved.
Trace entries from the prelisting to the deposit slip to determine whether each has been deposited.
REQUIRED
Identify which audit procedures can be tested using attribute sampling. Justify your response.
State the appropriate sampling unit for each of the tests in part (a).
Define the attributes that you would test for each of the tests in part (a). State the audit object associated with each of the attributes.
Define exception conditions for each of the attributes that you have described in part (c).
Which of the exceptions would be indicative of potential fraud? Justify your response.

5. Solution
It would be appropriate to use attributes sampling for all audit procedures except audit procedure 1. Procedure 1 is an analytical procedure for which the auditor is doing a 100% review of the entire cash receipts journal.
The appropriate sampling unit for audit procedures 2-5 is a line item, or the date the prelisting of cash receipts is prepared. The primary emphasis in the test is the completeness objective and audit procedure 2 indicates there is a prelisting of cash receipts. All other procedures can be performed efficiently and effectively by using the prelisting.
c. The attributes for testing are as follows
Audit Procedure
Attribute 2
Cash receipts in the prelisting are recorded in the cash receipts journal 3
Customer name, date, and amount are equal on the prelisting and cash receipts journal. 4
Cash discounts were approved on the related remittance advice. 5
Cash included in the prelisting has been included on the deposit slip.

6. d. & e. Exception conditions apply to items 2 through 5
(d) Exception condition
(e) Potential indicator of fraud? ALL ...
(2) Items on the prelisting are not recorded in the cash receipts journal OR
Items on the prelisting are recorded in the cash receipts journal in a different amount.
They could indicate theft of cash or lapping.
(3) Customer name, date, or amount do not agree to the amounts listed in the cash receipts journal.
Differences in customer name or date could indicate lapping.
Differences in amount could indicate theft.
(4) Cash discounts are not approved, but are taken by customers.
Unauthorized discounts could be given to related parties.
(5) Items on the prelisting are not recorded on the deposit slip OR
Items on the prelisting are recorded on deposit slip in a different amount.

7. Problem 3 – Attribute Sampling
Lenter Supply Corp. is a medium sized distributor of wholesale hardware supplies in southern Manitoba. It has been a client of yours for several years and has instituted excellent internal control for the control of sales, at your recommendation.
In providing control over shipments, the client has prenumbered “warehouse removal slips” that are used for every sale. It is company policy never to remove goods from the warehouse without an authorized warehouse removal slip. After shipment, two copies of the warehouse removal slip are sent to billing for the computerized preparation of a sales invoice. One copy is stapled to the duplicate copy of the prenumbered sales invoice, and the other copy is filed numerically. In some cases more than one warehouse removal slip is used for billing one sales invoice. The smallest warehouse removal slip number for the year is and the largest is The smallest invoice number is and the largest is
In the audit of sales, one of the major concerns is the effectiveness of the control in making sure all shipments are billed. The auditor has decided to use attribute sampling in testing internal control.

8. State an effective audit procedure for testing whether shipments have been billed. What is the sampling unit for the audit procedure?
A random selection of warehouse removal slips should be made
Examined to see if they have the proper sales invoice attached
The sampling unit will be the warehouse removal slip
Assuming the auditor expects no deviations in the sample but is willing to accept a TDR of 3%, at a 10% RIA, what is the appropriate sample size?
TDR = 3%
RIA = 10% (Beta Risk)
EPDR (Expected Population Deviation Rate) = 0
As auditor expects no deviations

9. Sample Size – CAS 530
R = nP
R is the PPS Factor determiner from a PPS table.
n is the sample size
P is the precision or Tolerable Deviation Rate
The auditor also needs to know the Expected Population deviation Rate (EPDR)
Usually obtained from last years audit sampling results
Use and EPDR of 0. i.e. the auditor expects no deviations

10. R Value Table Number of Errors R @ 90% R @ 95% R @ 97.5% 2.31 3.00
2.31
3.00
3.69 1
3.89
4.75
5.58 2
5.33
6.30
7.23 3
6.69
7.76
8.77 4
8.00
9.16
10.25 5
9.28
10.52
11.67 6
10.54
11.85
13.06 7
11.78
13.15
14.43 8
13.00
14.44
15.77 9
14.21
15.71
17.09 10
15.41
16.97
18.40

11. Given a confidence level of 90% and TDR of 3%
With and EPDR of 0, R = 2.31
Thus n = R/P = 2.31/0.03 = 77.

12. The following table is from the text, Appendix 10A.
TABLE FOR DETERMINING SAMPLE SIZE FOR TEST OF CONTROL USING THE MUS APPROACH TOLERABLE RATE OF DEVIATIONS FOR ERRORS
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.15
0.20
Beta Risk = 0.05
300
150
100 75 60 50 43 38 34 30 20 15
Beta Risk = 0.10
231
116 77 58 47 39 33 29 26 24 16 12
Beta risk or risk of incorrect acceptance. The risk of accepting an account as materially accurate when it is in fact materially misstated. More audit work would actually be needed and it is not performed.
Alpha risk or risk of incorrect rejection. The risk of rejecting an account as materially accurate when it is in fact not materially misstated. This would require more audit work when it is really not needed.

13. TOLERABLE DEVIATION RATE (IN PERCENTAGE) 2 3 4 5 6 7 8 9 10 15 20
EXPECTED POPULATION DEVIATION RATE (IN PERCENTAGE)
TOLERABLE DEVIATION RATE
(IN PERCENTAGE) 2 3 4 5 6 7 8 9 10 15 20
5 % Risk of Incorrect Acceptance (RIA or Beta Risk)
0.00
149 99 74 59 49 42 36 32 29 19 14
0.25
236
157
117 93 78 66 58 51 46 30 22
0.50 .
0.75
208
1.00
156
1.25
124
1.50
192
103
1.75
227
153 88 77
2.00
181
127 68
2.25 61
2.50
150
109
2.75
173 95
3.00
195
129 84
3.25
148
112
3.50
167 76 40
3.75
185
100
4.00
146 89
5.00
158
116
6.00
179 50
7.00 37

14. 10 % Risk of Incorrect Acceptance (RIA or Beta Risk)
TDR 2 3 4 5 6 7 8 9 10 15 20
10 % Risk of Incorrect Acceptance (RIA or Beta Risk)
0.00
114 77 57 45 38 32 28 25 22 11
0.25
194
129 96 64 55 48 42 18
0.50
0.75
265
1.00 .
176
1.25
221
132
1.50
105
1.75
166 88
2.00
198 75
2.25 65
2.50
158
110 58
2.75
209 94 52
3.00
3.25
153
113 82
3.50 73
3.75
131 98
4.00
149
4.50
218
130 87 34
5.00
160
115 78
5.50
142
103
6.00
182
116
7.00
199
7.50
8.00 60
8.50 68
EPDR
Sample size

15. Effect of population size -Initial sample size only
-Possible to make adjustment to initial sample size based on overall population size
-Finite correction factor
n = revised sample size
n’ = initial sample size
N = population size
n = n’
1 + n’/N

16. From the problem
Population is – = 22840
n’ = 77/(1+(77/22840)) = 76.74
Thus revised sample size is still 77
the population has very little effect

17. Use of a random number table
A one-to-one correspondence between warehouse removal slip
How is this correspondence achieved?
Random stab in the random number table

18. Population of Warehouse Removal Slips
37039
97547
64673
31546
99314
66854
97855
25145
84834
23009
51584
66754
77785
52357
98433
54725
18864
65866
76918
78825
58210
97965
68548
81545
82933
93545
85959
63282
78049
67830
14624
17563
25697
07734
48243
50203
25658
91478
08509
23308
48130
65047
40059
67825
18934
64998
49807
71126
77818
84350
67241
54031
34535
04093
35062
58163
30954
51637
91500
48722
60988
60029
60873
86723
36464
98305
08009
00666
29255
18514
50188
22554
86160
92250
14021
65859
16237
50014
00463
13906
35936
71761
95755
87002
66023
21428
14742
94874
58533
26507
04458
61862
63119
09541
01715
87901
91260
57510
36314
30452
09712
37714
95482
30507
43373
58939
95848
28288
60341
52174
11879
61500
12763
64433
02268
57905
72347
49498
78938
71312
99705
71546
42274
23915
38405
64257
93218
35793
43671
64055
88729
11168
56864
21554
70445
24841
04779
56774
96129
35314
29631
06937
54545
04470
75463
77112
40704
48823
65963
39359
12717
56201
22811
07318
44623
02843
33299
59872
86774
06926
94550
23299
45557
07923
75126
00808
01312
34348
81191
21027
77087
10909
03676
97723
92277
57115
50789
68111
75305
53289
39751
56093
58302
52236
65756
50273
61566
61962
16623
17849
96701
94971
94758
08845
32260
50848
93982
66451
32143
05441
10399
17775
48006
58200
58367
66577
68583
21108
41361
56640
27890
28825
96509
21363
53657
60119
Population of Warehouse Removal Slips
14,682 – 37,521
Random Stab
Random Number Table

19. Upper Exception Limit (UEL) or CUER or Computed Upper Exception Rate
Sample size = 100
TER = 3%
RIA = 10%
Number of deviations = 1
Using the following tables:
UEL = 3.8%
Are the controls working? No, UEL > TER

20. ACTUAL NUMBER OF DEVIATIONS FOUND 1 2 3 4 5 6 7 8 9 10
SAMPLE SIZE
ACTUAL NUMBER OF DEVIATIONS FOUND 1 2 3 4 5 6 7 8 9 10
5 % Risk of Incorrect Acceptance ( RIA or Beta Risk) 25
11.3
17.6 . 30
9.5
14.9
19.5 35
8.2
12.9
16.9 40
7.2
18.3 45
6.4
10.1
13.3
16.3
19.2 50
5.8
9.1
12.1
14.8
17.4
19.9 55
5.3
8.3
11.0
13.5
15.9
18.1 60
4.9
7.7
12.4
14.6
16.7
18.8 65
4.5
7.1
9.4
11.5
15.5
19.3 70
4.2
6.6
8.7
10.7
12.6
14.4
16.2
18.0
19.7 75
3.9
6.2
10.0
11.8
15.2
18.4
20.0 80
3.7
11.1
12.7
14.3
15.8
17.3 90
3.3
5.2
6.8
8.4
9.9
14.1
16.8
100
3.0
4.7
7.6
8.9
10.2
14.0
16.4
125
2.4
6.1
9.3
10.3
12.2
13.2
150
2.0
3.1
4.1
5.1
6.0
6.9
8.6
200
1.5
2.3
3.8
6.5

21. 1 2 3 4 5 6 7 8 9 10 Sample size ACTUAL NUMBER OF DEVIATIONS FOUND .1 2 3 4 5 6 7 8 9 10
10 % Risk of Incorrect Acceptance (RIA or Beta Risk) 20
10.9
18.1 . 25
8.8
14.7
19.9 30
7.4
12.4
16.8 35
6.4
10.7
14.5 40
5.6
9.4
12.8
15.9
19.0 45
5.0
8.4
11.4
14.2
17.0
19.6 50
4.5
7.6
10.3
12.9
15.4
17.8 55
4.1
6.9
11.7
14.0
16.2
18.4 60
3.8
6.3
8.6
10.8
14.9
16.9
18.8 70
3.2
5.4
9.3
11.1
14.6
17.9
19.5 80
2.8
4.8
6.5
8.3
9.7
11.3
14.3
15.7
17.2
18.6 90
2.5
4.3
5.8
7.3
8.7
10.1
12.7
15.3
16.6
100
2.3
5.2
6.6
7.8
9.1
11.5
13.8
15.0
120
1.9
4.4
5.5
9.6
10.6
11.6
12.5
160
1.4
2.4
3.3
4.9
5.7
7.2
8.0
9.5
200
1.1
2.6
4.0
4.6
7.0