Concept explainers
Cognitive Bias. A team of accounting students is working on a case where they are required to assess a set of information to determine a company’s allowance for
The company’s allowance for bad debts has been 5% of its receivables for the last several years
This year, the company has strengthened its credit extension policy.
The average time that an
The economy has weakened over the year, with a pending recession.
Following is part of the discussion at their first team meeting. Analyze the discussion and determine the type of cognitive bias most consistent with the statements made by each student, providing an explanation for your answer.
Discussion
Tom initiated the discussion saying, “I have seen this kind of situation before when a company has to report a higher allowance than last year. Allowances are always increasing.”
Jennifer offered. “The first piece of information in the case is always the most important.
The bad debts have historically been 5%. Therefore, the allowance has to be 5%.”
Jake added, “As I look at the case, I keep coming back to the fact that the average time that an account receivable has been outstanding has increased by 10 days. In my view, this is the most important piece of information—the other facts don’t matter.”
Manna’s view was, “Even though the economy has deteriorated, the historical data is always more important. The general trends in the economy are not relevant.”
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Intermediate Accounting
- 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?arrow_forwardYou are an assistant in the accounting department of Hasher Electronics, a small electronicsretailer. Hasher has a loan that requires the company to maintain a minimum cash balance of$125,000, as reported on its year-end balance sheet. Although Hasher has struggled in recent years,as of yesterday it looked as though Hasher would be able to meet this requirement. The cash balance in Hasher’s general ledger was $130,000 and the company’s credit manager was expecting toreceive a $30,000 electronic funds transfer that day on account from your biggest customer. Yourdepartment supervisor had been worried about meeting the loan requirement, so she had delayedmaking payments to Hasher’s suppliers for several days. But in anticipation of receiving the EFT,she decided yesterday to issue checks to suppliers totaling $15,000.It is now the last day of the fiscal year and your supervisor approaches you with a problem.Your big customer had backed out at the last minute, indicating it had “some…arrow_forwardFlorence Company had a debit balance of $1,500 in the Allowance for Doubtful Accounts account and a debit balance of $500,000 in the Accounts Receivable account with Credit Sales of $1,500,000 for the year. Management estimates 1.5% of credit sales will become uncollectible. What is the amount of estimated bad debts expense?arrow_forward
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