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MGSC 485 Final
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Gravity
Terms in this set (53)
Capacity of a resource pool
=Units
Load batch
Net availability(in min,sec,etc)/unit load (in min,sec,etc)=Capacity
DMAIC
Define: defining the objective and scope of the project
Measure: drawing current state process map
Analyze: understand current state process performance
Improve: develop solutions
Control: error-proof the process
Tools of Define phase
Goal statement, in-scope vs. out of scope, SIPOC, Kano, CTQ tree, affinity diagram
Tools of Measure phase
Process map, swim-lane map, value stream map, operator-machine chart, spaghetti diagram
Tools of Analyze phase
basic statistics (mean, median, standard deviation), graphs (histograms, box plots), fishbone diagram, 5 whys, scatter plot and correlation
Tools of Improve phase
Negative brainstorming, prioritization matrix stop light, 5-s (sort, store, shine, standardize, sustain)
Tools of control phase
Control checks, standard work
Simple check-sheet example
telephone response times at a call center at different times of day
Frequency plot check-sheet example
identifying probabilities of different types of customer concerns about a product/service
Traveler check-sheet example
finding out process times and total process flow time for a patient in a hospital
Location check-sheet example
identifying response times at a call center at different times of a day
One Sample t-test (for a continuous variable)
To test whether the sample mean is equal to the population mean (when population variance is unknown)
One Sample z-test (for a continuous variable)
To test whether the sample mean is equal to the population mean (when population variance is known)
Two Sample f-test (for a continuous variable)
To test whether the variances of two populations are the same
Tests to compare two populations
Hypothesis tests can also be used to compare two populations, based on samples from two populations. In that case, they are called 2 sample t-test, 2 sample f-test, etc
Scatter plot
one, continuous independent (X) variable
one, continuous dependent (Y) variable
example: we want to test whether the income of an employee depends upon his/her years of experience
Multiple linear regression
many, continuous independent (X) variables
one, continuous dependent (Y) variable
example: we are Wal-mart. we want to test whether dollar sales revenue for a store is impacted by population in the country, disposable income per family, and total tax revenue collected by the county. We have collected this data for 100 of our stores throughout the country
Logistic regression
many, continuous independent (X) variables
one, attribute dependent (Y) variable
example: we want to test whether the chance of a person getting flu depends upon his/her age, weight, and income
One-way ANOVA
one, attribute independent (X) variable
one, continuous dependent (Y) variable
example: we want to test whether the starting income of a business school graduate depends upon his/her major
Multiple-way ANOVA
multiple, attribute independent (X) variables
one, continuous dependent (Y) variable
example: we want to know whether the starting income of a business school graduate depends upon his/her major; and whether or not s/he had an internship
One-way MANOVA
one, attribute independent (X) variable
multiple, continuous dependent (Y) variables
example: we want to test whether the race of a person determines his/her income and life-span (number of years s/he lives)
Multiple-way MANOVA
multiple, attribute independent (X) variables
multiple, continuous dependent (Y) variables
example: we want to test whether the race and gender of a person determines his/her income and life-span (number of years s/he lives)
Chi-square
one, attribute independent (X) variable
one, attribute dependent (Y) variable
example: we want to test whether decision to take a Spring-Break vacation (Myrtle Beach or Mountain) depends upon the "Status" (in relationship or single)
Adjusted R squared
is the true measure of how well the Regression Line estimated fits the data
Critical p-value depends upon
the number of parameters being estimated
For Regression, the number of parameters estimated equals
number of Xs plus the constant
If p-value is the output for a variable X is less than critical p-value
then the variable X significantly impacts the Y variable
ANOVA: be able to read outputs in Excel, significant impact
...
For ANOVA, first check if Interaction Term (sample x column) is statistically significant
p < .05
For ANOVA, if it is not significant, then look at whether each sample (row x variable) has statistical impact on Y
p < .05
Control Chart: U
When measuring "number of defects per unit" in variable sample sizes
Ex: number of dents per car door (don't need to sample the same number of doors to do this)
Control Chart: nP
When measuring "number of defectives" in each sample (fixed sample size each time)
Ex: number of rejected shoes from samples of 100 shoes drawn EVERY SHIFT
Control Chart: P
When measuring "percent of defectives" in each sample (variable sample sizes)
Ex: PERCENT of calls balking per day (every day call volume could be different)
Control Chart: X-Bar (r or s)
When measuring a continuous variable using samples of fixed size
Ex: Measuring the amount of coke filled in a 16 oz. coke bottle by sampling 8 bottles every hour
Normal distribution and other distribution properties
...
When we want to compare the means of three or more populations
we can't use t-test and need to use ANOVA
Why use z-test? why t-test?
z-test: large sample (> 30)
Z-test
Reject Ho if Z-statistic (abs value) > Critical Z
Critical Z for confidence of 96%
1.96
One-tailed test versus 2
...
How to write null versus alternative hypothesis
...
How to calculate t-test critical t
...
Ch-Square: Expected Frequency
= (row total * column total) / table total
VSM mapping symbols
...
Normal distribution properties
mean = median = mode (perfectly symmetric)
+/- 1 standard deviation = 68.3% probability
+/- 2 standard deviation = 95.4% probability
+/- 3 standard deviation = 99.7% probability
Exponential distribution
Continuous, memoryless distribution
Uniform distribution
Continuous, intervals of the same length, equally probable
Poisson distribution
Discrete, probability of a given number of events occurring in a fixed interval, with a known average rate, independent of the lat occurrence
Binomial distribution
Discrete, yes/no distribution
Null hypothesis, Ho
statement being tested to determine whether it is true or not
Alternative hypothesis, Ha
statement that represents reality if there is evidence to reject the null hypothesis
Imx
is used to track sequential individual values of a continuous variable (ex: daily blood pressure monitoring of an individual patient)
ImR
is used to track sequential of a continuous variable (ex: difference between sugar level in blood before and after lunch for a patient tracked on a daily basis)
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