Using MS Excel for Six Sigma

8/7/2019 Using MS Excel for Six Sigma

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This is not a training on Six Sigma!!The training presentation assumes that you are alreadyaware of Six Sigma concepts, and are looking for ways to

implement the same using MS Excel.The training presentation also assumes that you know thebasics of MS Excel, and hence it focuses on some advancedanalytical concepts.The excel tips and tools mentioned in this presentation canbe used in multiple phases of the DMAIC order. So, thepresentation does not follow a DMAIC flow of thought.The training is based on MS Excel 2007. Improvise a littlewhen you are using MS Excel 2003.

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In mathematics, the central tendency of a data set is a measure of the"middle" or "expected" value of the data set. There are many differentdescriptive statistics that can be chosen as a measurement of thecentral tendency of the data items. These include mean, the medianand the mode.

Other statistical measures such as the standard deviation and the rangeare called measures of spread and describe how spread out the data is.

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Mean Median Mode

Measures of Central Tendency

Standard Deviation Variance Range

Measures of Spread


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The arithmetic mean (average) of a list of numbers is the sum of all of the list divided by the number of items in the list.To obtain the arithmetic mean from a dataset, use the excel functionAverage. Click below for the syntax for using the function.

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Syntax=AVERAGE(number1,number2,...)

Click for the syntax


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A median is described as the number separating the higher half of asample, a population, or a probability distribution, from the lower half.If there is an even number of observations, the median is not unique, soone often takes the mean of the two middle values.

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Syntax=MEDIAN(number1,number2,...)



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The mode is the value that occurs the most frequently in a data set or aprobability distribution. The mode is not necessarily unique, since thesame maximum frequency may be attained at different values.

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Syntax=mode(number1,number2,...)



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In Statistics, variance is the expected square deviation of a variable ordistribution from its expected value or mean. To obtain variance from adistribution, excel uses the function =var. Click below for the syntax.

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Syntax=VAR(number1,number2,...)


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Standard deviation is a measure of the variability or dispersion of astatistical population, a data set, or a probability distribution. Tocalculate Standard Deviation in an excel worksheet, we use thefunction, =stdev.

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Syntax=STDEV(number1,number2,...)


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In descriptive statistics, the range is the length of the smallest intervalwhich contains all the data. It is calculated on excel by subtracting theMin from the max value of the sample. Click below for the syntax.

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Syntax=max(A2:A16)-Min(A2:A16)


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In probability theory and statistics, skewness is a measure of theasymmetry of the probability distribution of a real-valued randomvariable. It is measured in Six Sigma because, in reality, data points arealways not perfectly symmetric.

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Syntax=skew(A2:A16)


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In probability theory and statistics, kurtosis is a measure of the"peakedness" of the probability distribution of a real-valued randomvariable.

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Syntax=kurt(A2:A16)


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60 70 80 100 110 120D ays

Z-Scale

-3 -2 -1

0 2 31

-7 -6 -5

-4 4 5 6 7

Area under curve toright of USL would

be considered %defective

Yield

USL

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)1Pr()90Pr(

15/ )8590(==

==

zxY

Z

If the mean is 85 days and the standard deviation is 5 days,what is the yield if the USL is 90 days?

P(z-1) = 1-.15865 =.8413 Yield 84.1%


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=normdist(x,mean,standarddeviation,cumulative)

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Hoursa s

Yield

USL

For a pizza delivery center, the mean of the delivery time is20 minutes and the standard deviation is 3.5. What is theirtarget, if the probability of achieving the target is 99.78%?


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=norminv(probability,mean,standarddeviation)

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=norminv(probability,mean,standarddeviation)

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Data in raw form are usually not easy to usefor decision making

Some type of organization is needed Table Graph

Techniques reviewed here:

Ordered ArrayHistogramsBar charts and pie chartsContingency tables

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A sorted list of data:Shows range (min to max)

Provides some signals about variabilitywithin the range

May help identify outliers (unusual observations)

If the data set is large, the ordered array isless useful

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Data in raw form (ascollected):

24, 26, 24, 21, 27, 27, 30, 41,32, 38

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Data in ordered array from

smallest to largest:

21, 24, 24, 26, 27, 27, 30, 32, 38,41


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A graph of the data in a frequency distribution iscalled a histogramThe class boundaries (or class midpoints ) areshown on the horizontal axisthe vertical axis is either frequency, relativefrequency, or percentage

Bars of the appropriate heights are used torepresent the number of observations withineach class

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Histogram: Daily High Tem perature

0

3

6

5

4

2

00

1

23

4

5

6

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5 15 25 35 45 55 More

Frequency

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(No gapsbetween

bars)

Class

10 but less than 20 15 320 but less than 30 25 630 but less than 40 35 5

40 but less than 50 45 450 but less than 60 55 2

FrequencyClass

Midpoint


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Choose Histogram

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Input data range and bin range (bin

range is a cell range containingthe upper class boundaries foreach class grouping)

Select Chart Outputand click OK

(


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Scatter Diagrams are used for bivariatenumerical data

Bivariate data consists of paired observationstaken from two numerical variables

The Scatter Diagram:

one variable is measured on the vertical axis andthe other variable is measured on the horizontalaxis

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Select the Insert Menutab

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Select Scatter plotdropdown andclick on any ofthe options. If indoubt, select thefirst option(scatter with onlymarkers)


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Cost per Day vs. Production Volume

0

50

100

150

200

250

0 10 20 30 40 50 60 70

Volume per Day

Cost per Day

Volumeper day

Cost perday

23 125

26 14029 146

33 160

38 167

42 170

50 18855 195

60 200


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Microsoft Exceldescriptive statistics output,

using the house price data:

House Prices:

$2,000,000500,000300,000100,000100,000


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SelectData Analysis

Choose Correlation from

the selection menuClick OK . . .


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Input data range and selectappropriate optionsClick OK to get output


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Select theinput range sfrom the data

Select theresidualspattern. If youare not sure,just selectline fit plots.


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Regression Statistics

Multiple R 0.76211

R Square 0.58082

Adjusted R Square 0.52842

Standard Error 41.33032

Observations 10

ANOVAdf SS MS F Significance F

Regression 1 18934.9348 18934.9348 11.0848 0.01039

Residual 8 13665.5652 1708.1957

Total 9 32600.5000

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386

Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580

The regression equation is:

feet) (square 0.10977 98.24833 price house +=


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Date post:	08-Apr-2018
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Using MS Excel for Six Sigma

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