1) A nutritionist claims that the proportion of females who consume too much saturated fat is lower than the proportion of males who consume too much saturated fat. In interviews with 513 randomly selected females, she determines that 300 consume too much saturated fat. In interviews with 564 randomly selected males, she determines that 391 consume too much saturated fat.
Do the data support the claim that the proportion of females who consume too much saturated fat is less than the proportion of males who consume too much saturated fat? Use α = 0.05 and the 4-step process.
2)
The developer of a new filter for filter-tipped cigarettes claims that it leaves less nicotine in the smoke than does the current filter. Because cigarette brands differ in a number of ways, he tests each filter on one cigarette of each of nine randomly selected brands and records the difference in nicotine content. His results are given in the table below.
|
Brand |
A |
B |
C |
D |
E |
F |
G |
H |
J |
|
Old Filter nicotine, mg |
0.7 |
0.8 |
0.8 |
0.9 |
0.9 |
1.0 |
1.2 |
1.2 |
1.8 |
|
New Filter nicotine, mg |
0.6 |
0.6 |
0.7 |
0.8 |
0.7 |
1.0 |
0.8 |
0.9 |
1.5 |
Does the data give convincing evidence that the filter tips leave less nicotine in the smoke? Follow the 4-step process.
old=c(.7, .8, .8, .9, .9, 1, 1.2, 1.2, 1.8)
new=c(.6, .6, .7, .8, .7, 1, .8, .9, 1.5)
3)
An investor with a stock portfolio worth several hundred thousand dollars sued his broker and brokerage firm because lack of diversification in his portfolio led to poor performance. The following data lists the rates of return, in percent, for a random sample of 39 months that the account was managed by the broker. The arbitration panel compared these returns with the average S&P 500 for the same period.
stock=c(-8.36, 1.63, -2.27, -2.93, -2.70, -2.93, -9.14, -2.64, 6.82, -2.35, -3.58, 6.13, 7.00, -15.25, -8.66, -1.03, -9.16, -1.25, -1.22, -10.27, -5.11, -0.80, -1.44, 1.28, -0.65, 4.34, 12.22, -7.21, -0.09, 7.34, 5.04, -7.24, -2.14, -1.01, -1.41, 12.03, -2.56, 4.33, 2.35)
Does the data show that the mean return is different from 0.95%, the average return for the S&P 500. Use α = 0.01 and the 4-step process.
4) A random sample of 328 medical doctors showed that 171 had a solo practice. Find and interpret a 95% confidence interval for the proportion of all doctors who have a solo practice. Follow the 4-step process.
5)
You are conduction a t-test for the mean using a sample of 9 observations. Do the following graphs indicate that it is safe to conclude the sample data is normal? Explain.
answer all please. thank you.
In: Statistics and Probability
|
Comprehensive Ratio Analysis The Jimenez Corporation's forecasted 2019 financial statements follow, along with some industry average ratios. Jimenez Corporation: Forecasted Balance Sheet as of December 31, 2019
Jimenez Corporation: Forecasted Income Statement for 2019
Calculate Jimenez's 2019 forecasted ratios, compare them with the industry average data, and comment briefly on Jimenez's projected strengths and weaknesses. Assume that there are no changes from the prior period to any of the operating balance sheet accounts. Do not round intermediate calculation. Round DSO to the nearest whole number. Round the other ratios to one decimal place.
So, the firm appears to be -Select- managed. The "Comment" section column is blank. |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In: Accounting
Verify Login Page that either gives error when not meeting the requirement or directing it to "blogs.php" when it's a successful log in..
Hi, so this is actually my html code for the log in and sign up page and I just needed help creating a verify log in/ sign up page with exception handling. Username has to be at least 6 character long. Password has to be 6 characters long and end with a number.
Blogs.com
"
"index.html"
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0
Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html;
charset=utf-8" />
<title>Blogs.com</title>
</head>
<body>
<h2>Sign in </h2>
<p> Enter your username and password to sign
in!</p>
<form method="POST" action ="blogs.php">
<p> User Name <input type ="text"
name="username"/></p>
<p> Password <input type="text" name ="passwordname"
/></p>
<input type="submit" value= "Sign in"/></p>
</form>
<p> Sign up if you're a first time user!</p>
<form method="POST" action ="UserRegistration.php">
<input type="submit" value= "Sign up!"/></p>
<br /><br />
<script>
var date =new Date();
document.write("Today " ,date);
</br>
</script>
</body>
</html>
"UserRegistration"
<?php
session_start();
$_SESSION = array();
session_destroy();
?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html
xmlns="http://www.w3.org/1999/xhtml">
<head>
<title>User Registration</title>
<meta
http-equiv="content-type" content="text/html; charset=iso-8859-1"
/>
</head>
<body>
<h1>User
Registration</h1>
<h2>Register / Log In</h2>
<p>New
user's, please complete the top form to register as a new user.
Returning user's, please complete
the second form to log in.</p>
<hr
/>
<h3>User
Registration</h3>
<form
method="post" action="Register.php?<?php echo SID;
?>">
<p>Enter your Name:
First <input type="text"
name="first" />
Last <input type="text"
name="last" />
</p>
<p>Enter your e-mail address:
<input type="text"
name="email" />
</p>
<p>Enter your password:
<input type="password"
name="password" />
</p>
<p>Confirm your password:
<input type="password"
name="password2" />
</p>
<p>
<em>(Passwords are
case-sensitive and must be at least 6 characters
long)</em>
</p>
<input type="reset" name="reset" value="Reset
Registration Form" />
<input type="submit" name="register"
value="Register" />
</form>
<hr
/>
<?php
$nag_counter = 0;
if(isset($_COOKIE['userVisit']))
$UserVisit = "<p>Your
visit number is $nag_counter was on " .
$_COOKIE['userVisit'];
else
$UserVisit = "<p>This
is your first visit!</p>\n";
++$nag_counter;
setcookie("userVisit", date("F j, Y, g:i a"),
time()+60*60*24*365);
?>
<?php
echo $UserVisit;
?>
</body>
</html>
"
In: Computer Science
In this program, you will generate a random “sentence” constructed of random “words”. Quotes are used in the preceding sentence because the “words” will mostly be nonsense words that do not exist in the English language.
Step 1. The average number of words in an English sentence is about 17 words. First generate a pseudorandom number NW between 10 and 20 for the number of words in your random “sentence”. Use srand( ) to set the initial value in the iterative algorithm within the rand( ) function. Given NW, initialize a for-loop for(i=0; i
Step 2. To generate a pseudorandom “word” within the for-loop
above, you first need to generate a pseudorandom number NL for the
number of letters in the “word”.
The number of letters in a word follows the following probability
table. two-letter words 0.20 three-letter words 0.27 four-letter
words 0.22 five-letter words 0.14 six-letter words 0.09
seven-letter words 0.08 -------------------------------- Total =
1.00
Think of generating a pseudorandom value NL between 0.0 and 1.0 and
choosing the number of letters in the “word” using the pseudorandom
value according to the following: If NL >= (0.00) and NL <=
(0.20), then the number of letters in the word will be two. If
NL> (0.20) and NL <= (0.20+0.27), then the number of letters
in the word will be three. If NL> (0.20+0.27) and NL <=
(0.20+0.27+0.22), then the number of letters in the word will be
four. If NL> (0.20+0.27+0.22) and NL <=
(0.20+0.27+0.22+0.14), then the number of letters in the word will
be five. If NL> (0.20+0.27+0.22+0.14) and NL <=
(0.20+0.27+0.22+0.14+0.09), then the number of letters in the word
will be six.
If NL> (0.20+0.27+0.22+0.14+0.09) and NL <=
(0.20+0.27+0.22+0.14+0.09+0.08), then the number of letters in the
word will be seven.
Notice that there are 6 intervals, representing NL=2 to NL=7. To
assign a number of letters NL in the “word”, declare a 6-cell 1-D
float array prob_letters[ ] and load the 1-D array with the
probability sums from above. float prob_interval[6]={ 0.2, 0.47,
0.69, 0.83, 0.92, 1.0};
Now generate a pseudorandom value x between 0.0 and 1.0 x=rand(
)/(float)RAND_MAX; Initialize the number of letters NL to 2: NL=2;
Now check if x lies in one of the other five intervals for NL=3 to
NL=7. If so, assign NL to the number of letters for that interval.
for(i=1; i<6; i++){ if(x>= prob_interval[i-1] &&
x<= prob_interval[i])NL=i+2; }
Two important Notes : Important Note 1 : A better way to generate
the 1-D array of interval values prob_interval[ ] is to start from
the probabilities for the occurrencess of NL=2 through NL=7. float
prob_interval[6]={ 0.2, 0.27, 0.22, 0.14, 0.09, 0.08}; Then form
the array elements as the sums for(i=1; i<6; i++){
prob_interval[i]= prob_interval[i]+ prob_interval[i-1]; } Use this
approach in the
Step 3 below. Important Note 2. : The array prob_interval[ ]
should be generated only one time, before you begin any looping. Do
not generate this array over-and-over-again, by incorrectly placing
it inside the loop in Step 1. Step 3. Print out a “word” of length
NL letters to the display. This “word” will be constructed from
pseudorandom letters a-z. Start with a for-loop for(j=0; j a 0.085
b 0.021 c 0.045 d 0.034 e 0.112 f 0.018 g 0.025 h 0.030 i 0.075 j
0.002 k 0.011 l 0.055 m 0.030 n 0.067 o 0.07 p 0.032 q 0.002 r
0.076 s 0.057 t 0.070 u 0.036 v 0.011 w 0.013 x 0.002 y 0.018 z
0.002 ---------------- total 1.000
Use the method from Step 2. You can make use of the sequential
ascii character codes for letters ‘a’ through ‘z’. float
prob_let[26]= {0.085, 0.021, 0.045, 0.034, 0.112, 0.018, 0.025,
0.030, 0.075, 0.002, 0.011, 0.055, 0.030, 0.067, 0.071, 0.032,
0.002, 0.076, 0.057, 0.070, 0.036, 0.011, 0.013, 0.002, 0.018,
0.002}; Important Note: The first letter of the first word in the
sentence should be printed as capitalized; i.e. uppercase. Step 4.
Place a blank space after the “word” printed in Step 3, or place a
period after the “word” printed in Step 3 if it is the last “word”
in the sentence. Your results should look like: Elfh gfk llae
mjlodp noc tjvjs mlknko si. Note, we haven’t applied any rules such
as: 1. a “minimum of one vowel per word”; 2. ‘t’ is often followed
by ‘h to form ‘th’; 3. ‘s’ often occurs at the end of a word to
form a plural; 4. etc.. so actual words will only appear
infrequently
In: Computer Science
CarryAll Company produces briefcases from leather, fabric, and synthetic material in a single production department. The basic product is a standard briefcase that is made from leather, lined with fabric. CarryAll has a good reputation in the market because the standard briefcase is a high quality item and has been well-produced for many years. Last year, the company decided to expand its product line and produce specialty briefcases for special orders. These briefcases differ from the standard in that they vary in size, they contain both leather and synthetic materials, and they are imprinted with the buyer’s logo, whereas the standard briefcase is simply imprinted with the CarryAll name in small letters. The use of some synthetic materials in the briefcase was made to hold down the materials costs. To reduce the labor costs per unit, most of the cutting and stitching on the specialty briefcases is done by automated machines which are used to a much lesser degree in the production of the standard briefcases. Because of these changes in the design and production of the specialty briefcases, CarryAll believed that they would cost less to produce than the standard briefcases. However, because they are specialty items, they were priced slightly higher—standards are priced at $30, specialty briefcases at $32. After reviewing last month’s results of operations, CarryAll’s President became concerned about the profitability of the two product lines because the standard briefcase showed a loss while the specialty briefcase showed a greater profit margin than expected. The President is wondering whether the company should drop the standard briefcase and focus entirely on specialty items. The cost data for last month’s operations as reported to the President are as follows:
Standard Specialty
Units Produced: 10,000 2,500
Direct Materials:
Leather 1.0 sq. yd. $15.00 0.5 sq. yd. $ 7.50
Fabric 1.0 sq. yd. 5.00 1.0 sq. yd. 5.00
Synthetic ____ 5.00
Total Materials: $20.00 $17.50
Direct Labor: 0.5 hr @ $12.00 6.00 0.25 hr. @ $12.00 3.00
Factory Overhead: 0.5 hr. @ $ 8.98 4.49 0.25 hr. @ $ 8.98 2.24
Cost per Unit $30.49 $22.74
Factory overhead is applied on the basis of direct-labor hours. The rate of $8.98 per direct-labor hour was calculated by dividing the total overhead $50,500 for the month by the direct-labor hours of 5,625. As shown above, the cost of a standard briefcase is $0.49 higher than its $30 sales price whereas the specialty briefcase has a cost of only $22.74 for a gross profit per unit of $9.26. The problem with these costs is that they do not accurately reflect the activities involved in manufacturing each product. Determining the costs using ABC should provide better product costing data to help gauge the actual profitability of each product line.
Analyzing the Factory Overhead Costs The factory overhead costs must be analyzed to determine the activities causing the costs. Assume that the following costs and cost drivers have been identified.
Purchasing Department cost is $6,000. The major activity driving the purchasing department costs is the number of purchase orders processed. During the month, purchasing prepared the following number of purchase orders: For leather 20 For fabric 30 For synthetic material 50
Receiving and Inspecting Materials cost is $7,500. Receiving and inspecting costs are driven by the number of deliveries. During the month, the following number of deliveries were made: Leather 30 Fabric 40 Synthetic material 80
Setting Production Line cost is $10,000. Set-up activities involve changing the machines to produce the different types of briefcases. A set-up for production of the standard briefcases requires one hour while set-up for the specialty briefcases requires two hours. Standard briefcases are produced in batches of 200; specialty briefcases are produced in batches of 25. During last month, there were 50 set-ups for the standard items and 100 set-ups for the specialty items.
Inspecting Finished Goods cost is $8,000. All briefcases are inspected to ensure that quality standards are met. However, the final inspection of standard briefcases takes very little time because the employees identify and correct quality problems as they do the hand-cutting and stitching. A survey of the personnel responsible for inspecting the final products showed that they spent 150 hours on the standard briefcase and 250 hours on the specialty ones during the month.
Equipment Related costs are $6,000. Equipment related costs include repairs, depreciation and utilities. Management has determined that a logical basis for assigning these costs to products is machine hours. A standard briefcase requires ½ hour of machine time, and a specialty briefcase requires 2 hours. Thus, during the last month, 5,000 hours of machine time relate to the standard line and 5,000 hours relate to the specialty line. Plant Related costs are $13,000. Plant related costs include property taxes, insurance, administration and others. These costs are to be assigned to products using machine hours.
Required: a. Using activity-based costing concepts, what overhead costs are assigned to the two products?
b. What is the unit cost of the two products using activity-based costing concepts?
c. Reevaluate the President’s concern about the profitability of the two
In: Accounting
Waterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the nearest fire station. This information will be used in setting rates for insurance coverage. For a sample of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire damage, in thousands of dollars (y). The MegaStat output is reported below.
| ANOVA table | |||||
| Source | SS | df | MS | F | |
| Regression | 1,835.5782 | 1 | 1,835.5782 | 40.45 | |
| Residual | 1,270.4934 | 28 | 45.3748 | ||
| Total | 3,106.0716 | 29 | |||
| Regression output | |||
| Variables | Coefficients | Std. Error | t(df=28) |
| Intercept | 13.6904 | 3.0467 | 2.427 |
| Distance–X | 3.2931 | 0.5178 | 6.36 |
Write out the regression equation. (Round your answers to 3 decimal places.)
How much damage would you estimate for a fire 4 miles from the nearest fire station? (Round your answer to the nearest dollar amount.)
Determine and interpret the coefficient of determination. (Round your answer to 3 decimal places.)
Fill in the blank below. (Round your answer to one decimal place.)
_______________ % of the variation in damage is explained by variation in distance.
Determine the correlation coefficient. (Round your answer to 3 decimal places.)
State the decision rule for 0.01 significance level: H0 : ρ = 0; H1 : ρ ≠ 0. (Negative value should be indicated by a minus sign. Round your answers to 3 decimal places.)
Compute the value of the test statistic. (Round your answer to 2 decimal places.)
In: Statistics and Probability
A consumer testing agency tested 130 makes and models of cars. In the model below, price (in 1000s) was the dependent variable, and the independent variables included miles per gallon (MPG), Handling score (on a scale from 0 to 5) and Reliability score (on a scale from 0 to 20), as well as a dummy, d_Leather, indicating that the car has a leather interior. The residual plot for MPG is also included.
|
SUMMARY OUTPUT |
|||||||||||||||||||||||||||||
|
Regression Sta tistics |
|||||||||||||||||||||||||||||
|
Multiple R |
0.719064 |
||||||||||||||||||||||||||||
|
R Square |
0.517054 |
||||||||||||||||||||||||||||
|
Adjusted R Square |
0.501599 |
||||||||||||||||||||||||||||
|
Standard Error |
14.24754 |
||||||||||||||||||||||||||||
|
Observations |
130 |
||||||||||||||||||||||||||||
|
ANOVA |
|||||||||||||||||||||||||||||
|
df |
SS |
MS |
F |
Significance F |
|||||||||||||||||||||||||
|
Regression |
4 |
27166.07 |
6791.518 |
33.45699 |
|||||||||||||||||||||||||
|
Residual |
125 |
25374.06 |
202.9925 |
||||||||||||||||||||||||||
|
Total |
129 |
52540.13 |
|||||||||||||||||||||||||||
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
||||||||||||||||||||||||
|
Intercept |
22.27657 |
4.912525 |
4.534647 |
1.33E-05 |
12.55407 |
||||||||||||||||||||||||
|
MPG |
1.135804 |
0.098529 |
11.52767 |
2.17E-21 |
0.940804 |
31.99907 |
|||||||||||||||||||||||
|
Handling |
0.133298 |
0.765562 |
0.174118 |
0.862054 |
-1.38184 |
1.330804 |
|||||||||||||||||||||||
|
Reliability |
0.146641 |
0.212107 |
0.691351 |
0.490627 |
-0.27315 |
1.648441 |
|||||||||||||||||||||||
|
d_Leather |
3.784139 |
2.539686 |
1.490003 |
0.138742 |
-1.24221 |
0.566427 |
|||||||||||||||||||||||
a. Write the model estimated in the above equation.
b. Is the regression significant overall?
c. What is the interpretation of the coefficient for d_Leather?
d. What is the interpretation of the coefficient for Reliability?
e. Looking at the residual plot, does it look like our assumptions on the error term are sound? What would you recommend to improve the model?
f. Write the new model based on your recommendation from part c.
In: Statistics and Probability
A consumer testing agency tested 130 makes and models of cars. In the model below, price (in 1000s) was the dependent variable, and the independent variables included miles per gallon (MPG), Handling score (on a scale from 0 to 5) and Reliability score (on a scale from 0 to 20), as well as a dummy, d_Leather, indicating that the car has a leather interior. The residual plot for MPG is also included.
|
SUMMARY OUTPUT |
|||||||||||||||||||||||||||||
|
Regression Sta tistics |
|||||||||||||||||||||||||||||
|
Multiple R |
0.719064 |
||||||||||||||||||||||||||||
|
R Square |
0.517054 |
||||||||||||||||||||||||||||
|
Adjusted R Square |
0.501599 |
||||||||||||||||||||||||||||
|
Standard Error |
14.24754 |
||||||||||||||||||||||||||||
|
Observations |
130 |
||||||||||||||||||||||||||||
|
ANOVA |
|||||||||||||||||||||||||||||
|
df |
SS |
MS |
F |
Significance F |
|||||||||||||||||||||||||
|
Regression |
4 |
27166.07 |
6791.518 |
33.45699 |
|||||||||||||||||||||||||
|
Residual |
125 |
25374.06 |
202.9925 |
||||||||||||||||||||||||||
|
Total |
129 |
52540.13 |
|||||||||||||||||||||||||||
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
||||||||||||||||||||||||
|
Intercept |
22.27657 |
4.912525 |
4.534647 |
1.33E-05 |
12.55407 |
||||||||||||||||||||||||
|
MPG |
1.135804 |
0.098529 |
11.52767 |
2.17E-21 |
0.940804 |
31.99907 |
|||||||||||||||||||||||
|
Handling |
0.133298 |
0.765562 |
0.174118 |
0.862054 |
-1.38184 |
1.330804 |
|||||||||||||||||||||||
|
Reliability |
0.146641 |
0.212107 |
0.691351 |
0.490627 |
-0.27315 |
1.648441 |
|||||||||||||||||||||||
|
d_Leather |
3.784139 |
2.539686 |
1.490003 |
0.138742 |
-1.24221 |
0.566427 |
|||||||||||||||||||||||
a. Write the model estimated in the above equation.
b. Is the regression significant overall?
c. What is the interpretation of the coefficient for d_Leather?
d. What is the interpretation of the coefficient for Reliability?
e. Looking at the residual plot, does it look like our assumptions on the error term are sound? What would you recommend to improve the model?
f. Write the new model based on your recommendation from part c.
In: Statistics and Probability
The accompanying table shows a portion of a data set that refers to the property taxes owed by a homeowner (in $) and the size of the home (in square feet) in an affluent suburb 30 miles outside New York City.
| Taxes | Size |
| 21972 | 2330 |
| 17347 | 2427 |
| 18263 | 1873 |
| 15636 | 1098 |
| 43971 | 5639 |
| 33623 | 2429 |
| 15188 | 2332 |
| 16750 | 1898 |
| 18236 | 2108 |
| 16089 | 1245 |
| 15126 | 1227 |
| 36053 | 3027 |
| 31050 | 2814 |
| 42032 | 3329 |
| 14362 | 1635 |
| 38961 | 4074 |
| 25312 | 4016 |
| 22960 | 2470 |
| 16162 | 3584 |
| 29264 | 2879 |
| Taxes | Size |
| 21972 | 2330 |
| 17347 | 2427 |
| 18263 | 1873 |
| 15636 | 1098 |
| 43971 | 5639 |
| 33623 | 2429 |
| 15188 | 2332 |
| 16750 | 1898 |
| 18236 | 2108 |
| 16089 | 1245 |
| 15126 | 1227 |
| 36053 | 3027 |
| 31050 | 2814 |
| 42032 | 3329 |
| 14362 | 1635 |
| 38961 | 4074 |
| 25312 | 4016 |
| 22960 | 2470 |
| 16162 | 3584 |
| 29264 | 2879 |
| Taxes | Size |
| 21,972 | 2,330 |
| 17,347 | 2,427 |
| ⋮ | ⋮ |
| 29,264 | 2,879 |
a. Estimate the sample regression equation that
enables us to predict property taxes on the basis of the size of
the home. (Round your answers to 2 decimal
places.)
TaxesˆTaxes^ = + Size.
b. Interpret the slope coefficient.
As Size increases by 1 square foot, the property taxes are predicted to increase by $6.67.
As Property Taxes increase by 1 dollar, the size of the house increases by 6.67 ft.
c. Predict the property taxes for a
1,600-square-foot home. (Round coefficient estimates to at
least 4 decimal places and final answer to 2 decimal
places.)
TaxesˆTaxes^
In: Statistics and Probability
Wiemers Products Company operates three divisions, each with its own manufacturing plant and marketing/sales force. The corporate headquarters and central accounting office are in Wiemers, and the plants are in Freeport, Rockport, and Bayport, all within 50 miles of Wiemers. Corporate management treats each division as an independent profit center and encourages competition among them. They each have similar but different product lines. As a competitive incentive, bonuses are awarded each year to the employees of the fastest-growing and most-profitable division.
Indy Grover is the manager of Wiemers's centralized computerized accounting operation that enters the sales transactions and maintains the accounts receivable for all three divisions. Indy came up in the accounting ranks from the Bayport division where his wife, several relatives, and many friends still work.
As sales documents are entered into the computer, the originating division is identified by code. Most sales documents (95%) are coded, but some (5%) are not coded or are coded incorrectly. As the manager, Indy has instructed the data-entry personnel to assign the Bayport code to all uncoded and incorrectly coded sales documents. This is done, he says, “in order to expedite processing and to keep the computer files current since they are updated daily.” All receivables and cash collections for all three divisions are handled by Wiemers as one subsidiary accounts receivable ledger.
(a)
Who are the stakeholders in this situation?
(b)
What are the ethical issues in this case?
(c)
How might the system be improved to prevent this situation?
In: Accounting