1. Lecture 6 Dr. Haider Shah

2.  Understand what are the primary tools for forecasting  Understand regression analysis and when and how to apply it

3.  In previous lectures we have covered budget preparation  We will now look at how we could produce data to be placed in these budgets

4.  Qualitative Methods  Quantitative methods using historical data

5. The following checks need to be applied 1. Data must be examined for one off costs. We only want base data which is likely to re-occur 2. Has the data been affected adversely by accounting policies 3. Is the time period appropriate. e.g long enough to reflect seasonal changes 4. Be able to identify dependent and independent variables

6.  Y = a + bX (equation of straight line) a b X Y

7.  Y = a + bX Where: Y = the dependent variable – depends on the value of X X= the independent variable a = a constant a fixed amount b = a constant ( the number by which the value of X should be multiplied to derive the value of Y

8.  If there is a linear relationship between total costs and level of activity  Y will equal Total Costs  X will equal Level of activity  a will equal fixed cost  b will equal variable cost per unit

9. Variable cost - linear: Total Cost Output E.g. Direct Materials 10 30 Unit Cost Output £3

10. Stepped Fixed Costs: Total Cost Output E.g. Total Cost Output E.g. Semi-Variable Costs: Supervision Power Telephone Normal Operating Range (Relevant Range) Variable Costs Fixed Costs Total Costs Cost Classification

11.  Also called mixed costs. Comprise fixed and variable components.  Variable and fixed costs components need to be split for estimation purpose  We need some data which plot total cost against the cost driving activity.

12.  The data can be used to help us split the total cost into variable and fixed components

13.  Regression analysis:  Used for both Revenue & Costs estimates  Time series analysis  Used for Revenue estimates mostly  High-Low Method  Less sophisticated estimation method

14. MonthFactory O/HDir Lab Hours January159 February2019 March1411 April1614 May2523 June2012 July2012 August2322 September147 October2213 November1815 December1817

15. 4 16 12 20 8 24 12 28 84201624 Factory O/H (£) Direct Labour hours.......... Fixed Variable O/H rate

16. Total O/H (y) Activity (hours) (x) High£25 23 Low£14 7 Difference£1116 = 11 = £0.6875 16 Fixed O/H = Total O/H – variable rate * Dir Lab Hours Fixed O/H = 25 – 0.6875 * 23 = 9.1875 Y = a + bxY = 9.1875 + 0.6875x Variable Rate = Difference of y Difference of x

17.  Also known as ‘least squares technique’  Historical data is collected from previous periods and adjusted to a common price to remove inflation.  Provides information for activity levels (X) and associated costs (Y).

19. Identify fixed and variable cost elements MonthTruck maintenance(£) Truck usage (hrs) Jan13,600 2,100 Feb15,800 2,800 Mar14,500 2,200 Apr16,200 3,000 May14,900 2,600 June15,0002,500 Total90,00015,200

20. XYXYXsq 000 hrs£'000 12.113.628.564.41 22.815.844.247.84 32.214.531.904.84 43.016.248.609.00 52.614.938.746.76 62.51537.506.25 15.290.0229.5439.10 Solution:

21. n=No. of pairs = 6 b =6(229.54) - (15.2)(90) (6(39.1) - (15.2)sq) =(1377.24 - 1368) (234.6 - 231.04) =9.24 3.56 =£2.60 a =(sumY/n) - (bsumX / n) a = (90/6) - (2.6(15.2)/6) a =8.41 approx = £8,410

22. MonthOutput(units) 000s Costs(£k) Jan20 82 Feb16 70 Mar24 90 Apr22 85 May18 73 a)Calculate the fixed cost and the variable cost per unit b)What would be total costs if output was 22,000 units The following data is available for a factory

23. XYXYXsq 000 hrs£'000 120821640.00400.00 216701120.00256.00 324902160.00576.00 422851870.00484.00 518731314.00324.00 1004008104.002040.00

25.  Complex and difficult  Need to consider various factors

26. Economic conditions anticipated Past sales patterns New product introductions Results of market research Changes in product mix Competition Consumer tastes

27.  Sales of product A over the past 7 years were as follows: Yr Sales (‘000 units) 1 22 2 25 3 24 4 26 5 29 6 28 7 30 Noting that X becomes the years, identify the sales in Year 8 using regression analysis

28. YrXYXYX sq 1122 1 2225504 3324729 442610416 552914525 662816836 773021049 sum28184771140

29. Y = a + bX b=((7 x 771) -(28 x 184)) ((7 x 140) - (28 x 28) b= 245 / 196= 1.25 a =(184 ) -(1.25 x 28) = 21.3 77 Y=21.3 + 1.25X