Linear Regression "Least Squares Method"
sammyd22
Registered Posts: 207 Dedicated contributor ๐ฆ
Anyone studying Cima with any knowledge they can share on the above.
Im competent with costing but this is a bit tricky.
Any tips?
Im competent with costing but this is a bit tricky.
Any tips?
0
Comments

thought that was covered in P2??? ....i have some details on it from my college days...0

thought that was covered in P2??? ....i have some details on it from my college days...
Under the new syllabus they have dropped it from P2 to P1. Any help is appreciated.
They are also assessing investment appraisal in detail on P1 and the lecturer has said that this is also new to P1.0 
must have been asleep in my lecture, ill check ym BPP books when i get home.
yeah NPV and IRR are in P1 now0 
From what I can remember:
P1  Linear Regression (Forecasting ?)
P2  Linear Programming (Limiting Factors)
When I did P2 there was a longer question asking to use Graphical Linear Programming with a limiting factor style scenario (so maybe uselful to get to grips with this), but I never came across any question on P1 using Linear regression, only in college.
Good luck, I don't think it is as bad as it looks  the text books just look awful from first glance.0 
Linear Regression "Least Squares Method"
Linear Regression "Least Squares Method"
This is an AAT topic in PEV and MAC and builds on the Reports and Returns unit
The formula y = a + bx represents a straight line. a is the fixed part, b is the variable part per unit and x is the number of units.
So the same method that applies to semivariable costs also applies to forecasting salesSandy
sandy@sandyhood.com
www.sandyhood.com0 
You need to be able to derive a and b on the CIMA unit (but not on AAT)
Each exam has a set of formulae to help
Would you like to work through an example to try to help?
email me if you think I can help (my PM is virtually always full!!)Sandy
sandy@sandyhood.com
www.sandyhood.com0 
You need to be able to derive a and b on the CIMA unit (but not on AAT)
Each exam has a set of formulae to help
Would you like to work through an example to try to help?
email me if you think I can help (my PM is virtually always full!!)
Im familiar with the linear regression formula of Y= a + bx, where Y = total costs, A = Fixed Costs & b = Variable element per unit and X = Qty. This is not a problem for me as i covered this in AAT.
The problem is with the Least Square method. Ill get a practice question from home and post it on.
Thanks0 
hope the below helps more than confuses
http://www.itl.nist.gov/div898/handbook/pmd/section1/pmd141.htm
http://www.itl.nist.gov/div898/handbook/pmd/section4/pmd431.htm0 
hope the below helps more than confuses
http://www.itl.nist.gov/div898/handbook/pmd/section1/pmd141.htm
http://www.itl.nist.gov/div898/handbook/pmd/section4/pmd431.htm
thanks.
I doesnt seem to be as bad as first thought. I missed the lecture last week and had to look it up myself. The scientific formula had me confused but i should be ok.
Thanks All0 
Steven,
Regression is a topic covered in a lot of detail in any A level Statistics or Maths textbook discussing statistics. Work through a few examples and it should be easy. From memory, you would need to draw the graph Y=A + bx and I think there is another formula to follow to work out the regression.0 
JayneHogan wrote: ยปSteven,
Regression is a topic covered in a lot of detail in any A level Statistics or Maths textbook discussing statistics. Work through a few examples and it should be easy. From memory, you would need to draw the graph Y=A + bx and I think there is another formula to follow to work out the regression.
Thanks Jayne0 
Sample from my BPP text:
"Regression Analysis (also know as "method of least squares" ) find the Straight line which minimises the squares of the vertical distances from the original data to the regression line, i.e the line of best fit"
used with the formula (given in exam) you should be able to work out a and b for the straight line expression.0 
Sample from my BPP text:
"Regression Analysis (also know as "method of least squares" ) find the Straight line which minimises the squares of the vertical distances from the original data to the regression line, i.e the line of best fit"
used with the formula (given in exam) you should be able to work out a and b for the straight line expression.
Thanks for looking Slackda!0
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