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Pivot table







Goal Seek

� Goal Seek is used to get a particular result

when you're not too sure of the starting value.

�

For example, if the answer is 56, and the firstnumber is 8, what is the second number?

� Is it 8 multiplied by 7, or 8 multiplied by 6?

You can use Goal Seek to find out.









Example-Goal Seek

� The current Price Per Item is 25.00. We want

to change this with Goal Seek, because our

prices will be going up to hit our new profits of

35,000. So try this:



Goal seek



Analysis pack

� Load the Analysis ToolPak

� Click the Microsoft Office Button , and then click ExcelOptions.

� Click Add-Ins, and then in the Manage box, select Excel Add-

ins.� Click Go.

� In the Add-Ins available box, select the Analysis ToolPakcheck box, and then click OK. Tip If Analysis ToolPak is notlisted in the Add-Ins available box, click Browse to locate it.

� If you get prompted that the Analysis ToolPak is not currentlyinstalled on your computer, click Yes to install it.

� After you load the Analysis ToolPak, the Data Analysiscommand is available in the Analysis group on the Data tab.











Example





Example





Solver-Macro

� Step 1: Start the macro recorder (Tools > Macro > Record NewMacro ...).

� Step 2: Open the solver dialog box (Tools > Solver ...).

� Step 3: Clear any existing solver settings (Press the Reset Allbutton).

� Step 4: Choose the target cell, design variables, and constraints andpress the Solve button. Then select OK to accept the results.

� Step 5: Stop the macro recorder (Tools > Macro > Stop Recording...).

� Step 6: Add a button to the worksheet, using a button from the

Forms toolbar. (If the Forms toolbar is not displayed, right-click onany toolbar and click on "Forms".)

� Step 7: Assign the macro you created to the button. (Right-click onthe button and choose "Assign Macro ...")





Linear Programming & Excel

� Production planning problem

� A company manufactures four variants of the

same product and in the final part of themanufacturing process there are assembly,

polishing and packing operations. For each

variant the time required for these operations

is shown below (in minutes) as is the profitper unit sold.



LP Problem in Excel

� Assembly Polish Pack

� Variant 1 2

�2 4

� 3 3

� 4 7







Lp & Excel

� Constraints

� (a) operation time definition

� Tass = 2x1 + 4x2 + 3x3 + 7x4 (assembly)Tpol = 3x1 + 2x2 + 3x3 + 4x4 (polish)Tpac = 2x1 + 3x2 + 2x3 + 5x4 (pack

� (b) operation time limits� The operation time limits depend upon the situation being considered. In

the first situation, where the maximum time that can be spent on eachoperation is specified, we simply have:

� Tass <= 100000 (assembly)Tpol <= 50000 (polish)

Tpac <= 60000 (pack)� In the second situation, where the only limitation is on the total time

spent on all operations, we simply have:

� Tass + Tpol + Tpac <= 210000 (total time)



LP & Excel

� Objective

� Presumably to maximise profit - hence we

have� maximise 1.5x1 + 2.5x2 + 3.0x3 + 4.5x4

� which gives us the complete formulation of

the problem





Transportation

� A company can produce four different types of

devices identified as A, B, C and D. The

minimum amounts of devices are Amin, Bmin,

Cmin and Dmin. The maximum amounts are

defined as Amax, Bmax, Cmax and Dmax. The

prices of the devices are Ac, Bc, Cc and Dc



Transportation

Device Min amount Max amount Price, $

A 650 1000 4.5

B 1700 2200 5.5

C 1100 1400 6.5D 880 1300 7.0



Transportation

� There are three machines available to produce

the devices: M1, M2 and M3. Each machine

can operate from Tmin to Tmax minutes:

Machine \ Time Tmin, min Tmax, min

M1 55000 65000

M2 60000 69000

M3 62000 68000



Transportation

� The time required to produce each devices on

each machine (T) are given in the table (in

minutes.

Machine \

DeviceA B C D

M1 10 12 8 18

M2 14 11 10 17

M3 13 10 11 20

So, time to produce device A on machine M1 is denote as T_A_M1 and so on.

The amounts of devices are required to be found that maximize the profit.

The decision variables are amount of devices of each type: Ax, Bx, Cx and Dx.





Transporatation

Device Amount

A 650

B 2200

C 1400

D 1107

The Total Profit is $31877.5



Linear Regression

� A linear regression is just a statistical tool used

to determine whether or not two (or more)

variables are linearly related.

� Suppose you want to determine whether a

person's salary is a function of his or her

education level (measured in years).

The general form of the relationship is:

� Yi = a + bXi + errori



Linear Regression

� where:

� Yi = value of Y (salary) for observation i

� a = average value of Y (salary) when X (education) iszero

� b = average change in Y (salary) given a one unitincrease in X (education), i.e. the average increase insalary for each additional year of education.

� Xi = value of X (education) for observation i .

� errori = portion of Y (salary) that is unrelated to X(education), i.e. due to other factors (age, years on job,etc.)







Regression

� Then, the hypothesized relationship betweeneducation and earnings may be written

� I = a + bE + e

�

where� a = a constant amount (what one earns with zero

education);

� b = the effect in dollars of an additional year of

schooling on income, hypothesized to be positive;� e = the noise term reflecting other factors that

influence earn











Covariance

� The Covariance tool computes the value of theworksheet function COVAR for each pair of measurement variables.

� (Direct use of COVAR rather than the Covariance tool isa reasonable alternative when there are only twomeasurement variables, that is, N=2.)

� The entry on the diagonal of the Covariance tool'soutput table in row i, column i is the covariance of the

i-th measurement variable with itself.� This is just the population variance for that variable, as

calculated by the worksheet function VARP.

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