Use discriminant analysis to classify the accompanying new records using only Credit Score and Years of Credit History as input variables.Click here to view the credit approval data. Click here to view the new records.Complete the following table.(Round to four decimal places as needed.)Homeowner Credit Score Years of Credit History Revolving Balance Revolving Utilization Discriminant Score DecisionY57613$21,00015%N5217$4,00090%Y5922$8,50025%N59911$16,30070%N552101$2,50090%Y57515$16,70018%

Discriminant analysis is a statistical method used for classification and prediction purposes. In…

Answered: Use discriminant analysis to classify…

Managerial Accounting

15th Edition

ISBN:9781337912020

Author:Carl Warren, Ph.d. Cma William B. Tayler

Publisher:Carl Warren, Ph.d. Cma William B. Tayler

Chapter16: Financial Statement Analysis

Section: Chapter Questions

Problem 9E

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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?
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?
During its first year of operations, Pharoah Company had credit sales of $2,751,000, of which $366,200 remained uncollected at year-end. The credit manager estimates that $19,200 of these receivables will become uncollectible. Prepare the journal entry to record the estimated uncollectibles. (Assume an unadjusted balance of zero in Allowance for Doubtful Accounts.) (Credit account titles are automatically indented when amount is entered. Do not indent manually.) Account Titles and Explanation Debit Credit SHOW LIST OF ACCOUNTS LINK TO TEXT LINK TO TEXT INTERACTIVE TUTORIAL INTERACTIVE TUTORIAL Prepare the current assets section of the balance sheet for Pharoah Company, assuming that in addition to the receivables it has cash of $85,350, merchandise inventory of $189,550, and supplies of $12,900. (List current assets in order of liquidity) Pharoah Company Balance Sheet (partial) $
us Kimmel, tinancial Accounting, Be US eo 1 Srstom Announcements CALCATO Brief Exercise 8-10 Piet ts first vear of operations, Pronghorn Corp had credit sales of $2,714,100, of which $429,000 remained uncollected at year-end. The credit manager estimates that $16,370 of these recevahles w be Prepare the journal entry to record the estimated uncollectibles. (Assume an unadjusted balance of zero in Allowance for Doubtful Accounts.) (Credit account titles are automatically indented whee ment c indent manually.) Account Titles and Explanation Debit Credit SHOW LIST OF ACCOUNTS LINK TO TEXT LINK TO TEXT INTERACTIVE TUTORIAL INTERACTIVE TUTORIAL Prepare the current assets section of the balance sheet for Pronghorn Corp, assuming that in addition to the receivables it has cash of $85,320, merchandise inventory of $163,960, and supplies of $13,290. (List current a arder of liquidity) Pronghorn Corp Balance Sheet (partial) wT O secouNTS
Use the following financial statement information from Black Water Industries. BLACK WATER INDUSTRIES Ending Accounts Receivable Year Net Credit Sales 2017 $690,430 $335,250 2018 705,290 364,450 2019 770,500 406,650 A. Compute the accounts receivable turnover ratios for 2018 and 2019. Round your answers to two decimal places. 2018 times 2019 times B. Using the accounts receivable turnover, choose the statement that most closely describes the company's management of its receivables. a. The company's lending policies may be too strict. b. Collection efforts are not aggressive enough. There may be uncollectable receivables affecting the beginning and ending C. balances. d. All of the above statements may be correct. a b d
View Policies Current Attempt in Progress Assume the following information for Teal Mountain Corp. Accounts receivable (beginning balance) Allowance for doubtful accounts (beginning balance) Net credit sales Collections Write-offs of accounts receivable Collections of accounts previously written off (a) Account Titles and Explanation Uncollectible accounts are expected to be 6% of the ending balance in accounts receivable. (To record sales on account) (To record collection of accounts receivable) Prepare the entries to record sales and collections during the period. (List all debit entries before credit entries. Credit account titles are automatically indented when the amount is entered. Do not indent manually. If no entry is required, select "No Entry" for the account titles and enter O for the amounts) eTextbook and Media $143.000 List of Accounts 11,340 Save for Later Using multiple attempts will impact your score. 10% scare reduction after attempt 5 930,000 912.000 6,400 2,200…
Calculate the missing information on the revolving credit account. Interest is calculated on the unpaid or previous month's balance. PreviousBalance AnnualPercentageRate (APR) MonthlyPeriodicRate(as a %) FinanceCharge(in $) Purchasesand CashAdvances PaymentsandCredits NewBalance(in $) $1,026.61 1.75% $322.20 $300.00 Step 1 In the credit account statement below, the values of the annual percentage rate (APR), finance charge, and the new balance must be calculated. PreviousBalance AnnualPercentageRate (APR) MonthlyPeriodicRate(as a %) FinanceCharge(in $) Purchasesand CashAdvances PaymentsandCredits NewBalance(in $) $1,026.61 1.75% $322.20 $300.00 Recall that the annual percentage rate (APR) is tied to the monthly periodic rate by the following formula. monthly periodic rate = APR 12 By solving this equation for the APR, the known value for the monthly periodic rate can be substituted to calculate the APR. APR = monthly periodic rate ✕ 12 The…
Required: Age of Receivables. (Classify them to: 0-30 days, 31-60, 61-90, and 90+ days) Afterwards, compute for the Past Due Ratio. Amount Due Name of Client Amount of Credit (PHP) Date of Granting Due Date 03/01/2022 04/01/2022 02/14/2022 04/01/2022 11/01/2021 06/12/2021 Tengen Uzui Inc. 100,000.00 Kyojuro Rengoku Company Shinobu Kocho Ltd. 200,000.00 300,000.00 04/01/2022 Muichiro Tokito Inc. 400,000.00 04/01/2022
Use this table for calculations: Minimum Days Maximum days % uncollectible 1 30 1.5% 31 60 3.5% 61 90 12% 91 365 70% I need to calculate the number of days each receivable is outstanding look at image to fill in , show work when calculating or using formulas , included all work please. After that fill in the schedule accounts recievable shown in the other image for context. When filling in the schedule of accounts recievable show what the formulas w you use and all work.
Give Me More Credit uses the aging method to calculate its "Allowance for Expected Credit Losses". The aging report indicates that $17,627 of its $101,474 of accounts receivables are estimated to be uncollectible. If the Allowance for Expected Credit Losses has a $1,101 credit balance (Before any adjustments), we will DR. Credit Losses for:
The following is select financial statement information from Vortex Computing: Year Net Credit Sales Ending Accounts Receivable 2018 $1,557,200 $398,000 2019 $1,755,310 $444,400 2020 $1,865,170 $500,780 Compute the accounts receivable turnover ratios and the number of days’ sales in receivables ratios for 2019 and 2020 (round answers to two decimal places): 2019 Accounts Receivable Turnover = ["", "", "", ""] times. 2019 Days' Sales in Receivables = ["", "", "", ""] days. 2020 Accounts Receivable Turnover = ["", "", "", ""] times. 2020 Days' Sales in Receivables = ["", "", "", ""] days. What do the outcomes tell a potential investor about Vortex Computing, if 2020 industry average for accounts receivable turnover ratio is 4.00 times and days’ sales in receivables ratio is 91.25 days? It takes Vortex Computing ["", "", ""] its competitors in the industry to collect on accounts receivable. If a lender were deciding between companies,…
The following is select financial statement information from Vortex Computing: Year Net Credit Sales Ending Accounts Receivable 2018 $1,557,200 $398,000 2019 $1,755,310 $444,400 2020 $1,865,170 $500,780 Compute the accounts receivable turnover ratios and the number of days’ sales in receivables ratios for 2019 and 2020 (round answers to two decimal places): 2019 Accounts Receivable Turnover = ["", "", "", ""] times. 2019 Days' Sales in Receivables = ["", "", "", ""] days. 2020 Accounts Receivable Turnover = ["", "", "", ""] times. 2020 Days' Sales in Receivables = ["", "", "", ""] days.

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