Which of the following would not be considered an analytical procedure?a. Projecting an error rate by comparing the results of a statistical sample with the actual populationcharacteristics.b. Developing the expected current year sales based on the sales trend of the prior five years.O c. Computing accounts receivable turnover by dividing credit sales by the average net receivables.d. Estimating payroll expense by multiplying the number of employees by the average hourly wage rate and thetotal hours worked.

The correct answer is: (A) Projecting an error rate by comparing the results of a statistical sample…

Answered: Which of the following would not be…

Business

Accounting

Which of the following would not be considered an analytical procedure?

Auditing: A Risk Based-Approach to Conducting a Quality Audit

10th Edition

ISBN:9781305080577

Author:Karla M Johnstone, Audrey A. Gramling, Larry E. Rittenberg

Publisher:Karla M Johnstone, Audrey A. Gramling, Larry E. Rittenberg

Chapter10: Auditing Cash And Marketable Securities

Section: Chapter Questions

Problem 28MCQ

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In constructing a pro forma balance sheet a manager can estimate the accounts receivable because: Select one: a. managers typically construct a pro forma income statement prior to the balance sheet. Thus, an estimate of sales has already been made and this is critical to estimating accounts receivable. O b. if the firm has already made an estimate of expected sales, then it can also estimate average daily sales. O c. if the firm maintains similar credit standards it can use the historical average age of accounts receivable to help estimate the anticipated average age of accounts receivable O d. if the firm has each piece of information as stated in the three choices above THEN they can estimate accounts receivable for the pro forma balance sheet..
Other than sales account, identify two (2) other accounts in the client’s Statement of Financial Position where you would use Dollar Unit Sampling (DUS). Explain your answer.
Consider again the example introduced in Section 4.5 of a credit card company that has a database of information provided by its customers when the customers apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3). Figure 4.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? d. Recall the multiple regression in Figure 4.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts a, b, and c? What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? e. Add the coefficients of determination for the simple linear regression in parts a, b, and c, and compare the result to the coefficient of determination for the multiple regression model in Figure 4.23. What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? f. Add age, a dummy variable for gender, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 4.23. Code the dummy variable for gender as 1 if the customers gender is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?
Which among the following is an example of qualitative data? a. Opinion poll b. Payroll data c. Production data d. Sales figures
An auditor is required to confirm accounts receivable if the accounts receivable balances area. Older than the prior year.b. Material to the financial statements.c. Smaller than expected.d. Subject to valuation estimates.
Consider again the example introduced in Section 7.5 of a credit card company that has a database of information provided by its customers when they apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3). Figure 7.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? d. Recall the multiple regression in Figure 7.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts (a), (b), and (c)? What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? e. Add the coefficients of determination for the simple linear regression in parts (a), (b), and (c), and compare the result to the coefficient of determination for the multiple regression model in Figure 7.23. What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? f. Add age, a dummy variable for sex, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 7.23. Code the dummy variable for sex as 1 if the customer is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?
An investor wants to know if a company is manipulating its earnings with the help of allowances for doubtful accounts? What is the best way to do this? A.Look at the level of doubtful accounts as a percentage of sales and percentage of receivables for past years. b.Compare the level of doubtful accounts as a percentage of sales and percentage of receivables with peers in the same industry. c.Analyze the earnings trends without allowances for doubtful allowances and identify earnings pressures if any. d.All of these.
Which of the following is not a management report?A.Sales by product actual versus budget B. Department spending actual versus budget C. Income statement D. Accounts receivable aging
Which one of the following best defines the term credit scoring? A. Categorizing customers into groups depending on the length of time it takes each customer to pay for purchases B. Compiling a list of accounts receivable segregated by the length of time each receivable has been outstanding C. Evaluating the opportunity costs of a credit policy D. Process of quantifying the probability of default when granting credit to customers E. Tracking of both the number and the size of customer orders over a period of time
Suppose the following data represent the number of persons unemployed for a given number of months in Killeen, Texas. The values in the first column show the number of months unemployed and the values in the second column show the corresponding number of unemployed persons. Let x be a random variable indicating the number of months a randomly selected person is unemployed. Use the data to develop an empirical discrete probability distribution for x. Show that your probability distribution satisfies the conditions for a valid discrete probability distribution. What is the probability that a person is unemployed for two months or less? Unemployed for more than two months? What is the probability that a person is unemployed for more than six months?
Given the information, give the following ratios; a. acid test ratio b. working capital c. Receivable turnover d. average Collection period.
Which of the following statements is correct? Select one: a. Account receivable turnover is equal to net credit sales divided by average net accounts receivable. b. Buying aged receivables from businesses and then collects the payments directly from the customers is called Allowance for uncollectible accounts. c. Auto generated document numbers is a fraud preventive method which is more efficient than physical controls. d. If the same people are responsible for a series of related accounting activities is called Segregation of Duties.