Chapter%204%20Notes%20001

Chapter%204%20Notes%20001 - THE UNIVERSITY OF TEXAS AT...

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Unformatted text preview: THE UNIVERSITY OF TEXAS AT AUSTIN A! McCOMBS SCHOOL or BUSINESS ACC 312 - Spring 2011 Fundamentals of Managerial Accounting Instructor - Brian Lendecky, MPA, CPA (copyright © 2011 Brian Lendecky) Thursday, February 3, 2011 Chapter 4 — Techniques for Estimating Fixed and Variable Costs Income Statement Formats 1. Gross Margin Format (format used for GAAP reporting) Revenue 60,000 Cost of Goods Sold Beg FG Inv 0 Plus COGM 28,000 Less End FG Inv 74M COGS 21,000 Gross Margin 39,000 Sales, General, and Admin expenses (aka period costs) 22,200 Income 16,800 2. Contribution Margin Format (format commonly used for internal reporting) Revenue 60,000 Variable Costs Variable manufacturing costs 15,000 Variable SG&A costs (period costs) 10,000 Variable costs 25,000 Contribution Margin 35,000 Fixed Costs Fixed manufacturing costs 6,000 Fixed SG&A costs (period costs) 12,200 Fixed costs 18,200 Income 16,800 The contribution margin format helps show a company’s cost struc e. Cost structure — flu ogri film, M} Cx'xeal PN home 0 a (c (14?; “j; (05" The contribution margin format can also be presented by segments, products, divisions, locations, customer, etc. $305033 59.2: 20338550 335253 Batmcoo "mod moxmu mhcwmn “$0...”— omflwwfi ogdge 23493 8385 833.3“ Swami: £92.. 92623 203% Bmou Ext 3883 83mg 08.5.N 83:; 0833 £8 BE £83: $3Mme” 828.3 03.893 ommdwmg 592.. 553:28 23%; Sow/Ev , 2833 8:33 $2.. was gsgéém a 3.5% @325 83% 83mm; 833 8qu E Eggnog:th 2525, 8&3? 833.: 83%.me 8335 .28 355%.: 3 a8 Ragga: 80.8qu Sqfiflmma 08533 $2.23. _ ooodm oom‘wm ommfiv A32: :5 3.39, 81mm .33. $328.25 $.me .323 :axzstfi :.~.¥§< ~3§§é§5 ESFFEY§K yarfiec ERNC £922 cozsfibcoo EwEmmm How do you estimate costs? Cost Estimation Methods 1. Account-classification method (also called account analysis) Examining an account in the general ledger and using personal judgment to determine fiJture cost behavior (whether fixed or variable). Advantage 1. Cam prauML x/ofj.qccvffil-c cghMul—o; BGCQUSQ, i}- fquflu exam/31mm 69och cask xx 5mm, Disadvantages ' 1_ fixtmncd Jrth Eonwmin Cl .1 ' h" . , 2. «as L'Ch 60> “tide/t (7 I‘U,u./Q. (onemhmslo. mow/(11),, app! 6900*”, 3- sug’echu» hawk (9“ 4‘1 CD , , a. / a, qu pd ram/j tum 72;“ (Dr (4/30. agram‘zqh'ou oM'! 2. High-low method (\03' 4 AM Considers only two points of data, the highest and lowest for activity, within the relevant range. The points selected should be representative of normal behavior (aka not an outlier). From these two points we estimate u_n1;t variable cost and total fixed costs. This estimation of ugh variable cost and total fixed costs is performed using the cost equation: total variable costs + total fixed costs = total costs (unit variable cost)(volume of activity) + total fixed costs = total costs cost equation The high-low method first focuses on cost changes, allowing an analyst to determine the presence of any variable cost. This is done by computing the slope of the line. The slope is the variable cost per unit: “rise over run” = (Difference in costs) / (Difference in activity level) Next, fixed costs are determined by subtracting variable cost from the total cost at either of the two data points (it doesn’t matter which one). Let’s take a look at an example. . .. Advantages y 1. (,JSJCCX'I'VC’I 2. E” L Bo'w5 hOi’ Féqu're. 4(L° fif‘ Liasgtnkcnl'fo'\ 3. ml) (\ch c035 «Mi {Guppy gala/11¢ lo ccdcuhgm' Disadvantages , 1. ON; ‘0149 erA lwa poml’; 2. 2H; 61 ‘6’de u finance. moi] GS 4(W‘Lk~ L 3' Tad”; fill [Lasts 05 00H?” om’t'kud 669%; (/19Jr 51kt» [we’ll Pall/Li” (Mt .. 14.21.25 9“‘*‘“""“3 w ((a flict‘Calfi'orx 9C .‘AJ/V‘Icq"9( “(MS “KL Snip 3&3 CE. \ 332:? 8:2,: ufimmfivmw: ‘ r852 \ fl \ fl 060 S \ xix Qx Mm: \ €ng I 50:2 “E N gm Ema macaw vmwdmmw nmofimo. ; w Fmfiwmw momdmmw vmvd 5w Nnmfivmw vmw. :Vmw m Fmdmmw NVNdoww nomimwa mmmdmww momdmmw vandmww mum. 5mm www.mmwa wavmme Nvmdoww mvm. mew mow. Fame nowdmnw as .32 El ._o_>m:mm “moo mcszzmm 3.. 355 Ema wEEmw mnmdv mmndm Nwmdm owwdv mwodv ommdv mnmfiv onmdm o 5. EV mmndv ommdv www.mm nmm. Sq www.mv www.mv mwrfim onwdv mamdv Fowfv omoKm v.50 m._> 9:0 m._> N50 m._> T50 m._> #30 Y; «.50 Y; NbO F; 7:0 E> v30 n..> «:0 9.; NbO n..> T50 9; #50 N._> 9:0 N..> N30 N._> T50 N._> $50 _L> n30 _L> NhO _L> T30 _L> 9:; 00>O END #00 0.00 E5 E0 v50 000 N50 E00 35 0:0 N100 EC 36 0:0 NbO :6 $00 95 N50 EC 0._> 05> 0; 0.; E> E> E> v; 00> 00> 00> 0; N; N; N._> N; _.._> E> t> E> 0 000.00 000.02 000.00? 000.00N 000.00N 000.000 000.000 000.00v 000.00v 000.000 000.000 000.000 000.000 000.005 000.00N. 000.000 000.000 000.000 000.000 000.000. _. 000.000. F 000.00 V; 38 58+ 3:5 IOI 3E: .w> Hmoo 000.00 . 00.00 000.0v 000.00 000.0N 000.0? 0 ca 000.00% 000.00 5 000.00 5 000.00% 000.00% 000.00me 000.0000 000.00% 000.003 000.000» 000.030 000.0000 000.0009 000.00; 000.005 000.000» 000.000» 000.000» 000.000» 000.000. 5 000.000. 5 000.00 F . 5 E8 28% - 33-29: gamma 0.1... 33:2 - :9: a 23:2 u 26.. um “moo ume u :9: a 58 85“. u umoo m_am:m> w_w>_u:< 30..-..9: vuwdmww www.mv 35 m; 50.50. 5 mmfiom 050 m; w 5.5% Nnmdm N50 m; www.mmma owwdv 7:0 m._> vein 5w mwodv 35 VS Nnmdvmw mwmdv who v; vmm. 3mm onwfiv N50 v; m 5.26% www.mm E0 S> andmww 05.; 35 m; Nomfmm» nondv who 0; mmmdmww wand». NEG m._> moodmmw www.mm EU a; 35689 SN. 3. 35 N; mnmgbww www.mv «:0 N; @8ng www.mv N30 N; wwOJVwa 09.5 EU N; nvmdoma onwdv v.50 t> mvmémww £343 95 _.._> momémmw Fowfv who E> 59¢de omoKm EC E> $00 .38. 375 Sun 2956 3. Regression Analysis (more specifically Least-Squares Regression method) A statistical approach that is both objective and considers fl data points. The regression line is in the form Y = a +bX X is the independent variable (activity level) and Y is the dependent variable ($). The coefficient of determination can be used to judge the line’s goodness of fit, or how well the line ' fits—the data on which it is based (it will always lie between 0 and l 5. m" "7' If the goodness of fit is relatively high (close to 1), a large proportion of the variation in the dependent variable is explained by changes in the independent variable. The p-value indicates the confidence that the coefficient estimates reliably differ from zero. We hope to see p—values lower than 0.05 and p-values less than 0.01 indicate a sufficiently high level of confidence in the estimates. (Page 123 and 124 shows how Excel can be used to calculate various parameters related to regression analysis.) ' Let’s take a look at an example... Advantages 1, 33 0:33 Mam,W/a( amour la omit; at m be? Farina (esl— pom, (2c. the r‘deQap the) VHR Monaco [5 “0" “(Vka hem 1R hOh’IOuJ. 2' Vsrj o‘ficLHa/c/ Disadvantages 1. Not as we to A.» M 0.x?! «'r\ 2- no bbjcwua 7 Which cost estimation method is best? No one method is always the best. - Account classification is best when historical patterns may not continue - High-low may be preferred for quick and simple estimates - Regression might be called for when cost patterns are complex and we expect historical relations to continue. Data Collection Problems The process of collecting appropriate data to use in cost estimation is important. Any method will fail if it integrates poor data (Garbage In = Garbage Out). Common problems: missing data, outliers, mismatched time periods, inflation, etc. The three estimation methods discussed above assume there is a historical pool of data from which to draw. These methods may not be relevant if there is a change in the relevant range or if the industry has fast-changing conditions (ex. tech industry). co_wm9mwm .moxm vmodmvw + xmvd; u 380 :32. Esmom commemmm mu : mo. E m. L. $8 23.; m. EONNMQd ommumomood ommohoood vowv P w wvoé m¢NthwNmN 2K Fmomvdr F m_nmtm> X avomdmowh .u momm.mmowm_. mvmmm v Food mNNNwwomwd mwmmdowm: mONoémommv 39:95 *8 a. 3 $3 633 mega Em ~ gem 985m £56580 flllllrIJNIIIIIIIK 3553mm. macaw. “moo ume \ mwnanwm EN 9 EC... wommmmovww vmwwwnvwvfi; or fiszom mmmouoood 03508.9. a Sr Emmmom a re Eamon w :o_mmm._mmm m 8.60% .5 m 92 mm % <>02< om . 2252080 8393.? .otm 23.35 ‘1 mmmnmmmgd 9635 m 0933.1 2036555930 *0 NEEDED—000v um— moammfivd 223 m 30858.0 m 29:35. nuancfim :3me mm 1 .5th0 >m<s§5w 000.00 000.0? 00? F0? 000 00v mac: 83E 000.00 + 060.: 000.0N u mumoo 5.0... 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Chapter%204%20Notes%20001 - THE UNIVERSITY OF TEXAS AT...

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