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QM Flashcards

1
Q

What is the QM323 Decision making framework?

A

define objectives/choices, analyze data/model, define risks & perform sensitivity analysis, and communicate results

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2
Q

What are the four decision making traps?

A

availability heuristic, confirmation trap, overconfidence, and overfitting

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3
Q

What are word clouds?

A

they allow you to quickly summarize data from large amounts of text. they break a piece of text into individual words & count the frequency with which they are observed

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4
Q

What does QM incorporate?

A

an understanding of risk & uncertainty

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5
Q

What are they key steps to creating a good work book?

A

plan out the structure, build the spreadsheet, review, and communicate your findings

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6
Q

What is an influence chart?

A

helps draw sketch of workbook. shows the outcome variable the model will generate and how outputs are calculated from inputs

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7
Q

When making Excel, should you isolate the parameters?

A

Yes, put parameters at the top, followed by decisions, outputs/objectives, and following calculations

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8
Q

What are the three ways to communicate results?

A

spreadsheets, visualizations, and dashboards

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9
Q

What are trace precedents in Excel?

A

They help understand the relationship between cells and which cells were used as inputs for a formula in another cell. Use formula auditing function

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10
Q

What are trace dependents?

A

they help understand which cells are affected by the value in a specific cell. They track how one cell impacts another

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11
Q

How to design structure of workbook?

A

define objective, sketch spreadsheet, organize spreadsheet into modules, start small, then isolate input parameters

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12
Q

What are the different variables for an influence chart?

A

decision variable, objective, intermediate quantities, and input parameter

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13
Q

What elements can be preceded by others in an influence chart?

A

intermediate calculations and outputs

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14
Q

What are major tasks in launching a product?

A

identifying a segment, targeting consumers, & positioning

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15
Q

How do you identify a segment?

A

You need to use cluster analysis

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16
Q

What is k-means clustering?

A

It helps assign customers to given segments using information on characteristics

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17
Q

Why do we use z score when doing k-means clustering?

A

Because the euclidean distance can be skewed cause of variables being measured on different scales

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18
Q

How to calculate z score?

A

Value - Mean/Stdev

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19
Q

What are the different types of analysis?

A

descriptive (describing observations), predictive (using a model from past data to make a prediction), and prescriptive (helps us identify the best course of action)

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20
Q

What are the steps for k means clustering?

A

Randomly put customers in clusters, find z-score, calculate the cluster centroids, and calculate distance from z score to given cluster centroid & reassign

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21
Q

How to find z-score with Excel?

A

=standardize(x,mean,stdev)

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22
Q

What are cluster centroids?

A

they are the average values of the z scores for each variable of each cluster(coordinates), need to rearrange data for this

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23
Q

How do you calculate distance from z-score to given cluster?

A

=sqrt((x1 - x2)^2 + (x3-x4)^2 + …))

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24
Q

How do you reassign?

A

Whichever cluster the observation is closer to is the one you reassign it to

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25
Q

How do you normalize data on XL miner?

A

you have to click standardization

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26
Q

What is inter cluster distance?

A

The euclidean distance between cluster centroids (how far you are from another cluster)

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27
Q

What is intra cluster distance?

A

average distance between points within the cluster. lets you see how homogenous each cluster is

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28
Q

What happens with clusters when you plot them on a scatter plot?

A

You usually only plot two variables, so it won’t show the full pictureH

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29
Q

How do you decide how many clusters to use?

A

Use trial and error to see which you make more conclusions from

30
Q

How do you decide to group clusters together?

A

Look at the inter cluster distance & group the ones with the smallest distance

31
Q

Does k-means clustering tell you which variable is most valuable?

A

No, you use the clusters you form to make segments which can then be used for conjoint analysis

32
Q

What is conjoint analysis beneficial for?

A

it helps you better tailor your product to the customer

33
Q

What are products and services considered?

A

They are a “bundle” of attributes. You need to decide which one is most valuable to you

34
Q

What helps identify attributes most important to consumers?

A

HOQ

35
Q

What are the steps in conjoint analysis?

A

identify a set of relevant attributes (needs to be something you can quantify), define reasonable levels for attributes (generally levels should be mutually exclusive and need to be greater than 2 per attribute), create product profile (hypothetical products/services described as bundles of attributes), obtain consumer preferences for profiles, and then analyze the data

36
Q

What is an example of an attribute?

A

Material, foam thickness, color

37
Q

What is an example of a level?

A

Plastic or wood for material attribute

38
Q

What is an example of a product profile?

A

An american car with a sedan body type and gasoline engine priced at 20,000 (4 dif attributes)

39
Q

What do you use to analyze conjoint analysis?

A

Use regression analysis, use one less variables

40
Q

What is the range of an attribute?

A

the max partworth (intercept) - min partworth. make sure you consider baseline options

41
Q

What is the formula for the importance of an attribute?

A

Range/(sum of range across all attributes)

42
Q

What is partworth?

A

the regression coefficient

43
Q

Do you use average when analyzing what target customers value?

A

No

44
Q

What are the 3 methods to analyze what the target consumer values?

A

standard deviation, ABC analysis, and compare relationships across attributes

45
Q

What does strong deviation mean?

A

It shows customer cares deeply about attribute because largely varied. If low all customer prefer the same thing

46
Q

What is ABC analysis?

A

It is used only for specific bundles & gives you the market share of each product. If you are hesitant on design, use ABC to see market share (pick the one with the highest value)

47
Q

What are the steps of ABC analysis?

A

Find value of each option, find market share, and analyze

48
Q

What are association rules?

A

AKA market basket & affinity analysis. Asks what goes with what. Uses an if/then statement

49
Q

What is an item set?

A

collection of items in store (basket of items)

50
Q

What is an antecedent?

A

item corresponding to the if portion of the rule

51
Q

What is a consequent?

A

item corresponding to the then portion of the rule

52
Q

What are two approaches to targeting customers?

A

one to one target marketing or using predictive regression modeling (Use multiple variables to predict probability of purchase and identify best customers to target)

53
Q

What is unsupervised Learning?

A

Can’t let the model do the work on own to discover information , can find all kinds of unknown patterns in data , can be unpredictable

54
Q

What is supervised learning?

A

Produce an output from previous experience , Helps you to optimize performance criteria using experience

55
Q

When making a dummy variable for purchase intention, what should you do?

A

Code definitely + probably will buy as 1 and the rest as 0

56
Q

What does sum of purchdum show in pivot table?

A

tells you for each row, how many were purchased (ex: if male is a variable, tells you how many men purchased)

57
Q

What does the grand total under sum of purchdum show in pivot table?

A

the total amount of people that purchased the items

58
Q

What does count of purchdum show?

A

just tells you the count of the variable (ex: how many males in data set if that’s the variable in the row)

59
Q

What does grand total in count of purchdum show?

A

the total number of transactions

60
Q

What are pitfalls of association rules?

A

They are descriptive, not causal, evidence, use randomized experiments to validate potential rules & need to sift through a lot of rules before finding useful
Beware of confirmation bias

61
Q

How to automatically calculate regression predictions?

A

When doing regression, check off residuals

62
Q

What is a residual?

A

Actual - Predicted

63
Q

Why would groups of people have the same predicted probabilities?

A

Because they might have the same values for the X variables

64
Q

What does predicted purchase probability help you find?

A

Helps you find the best group to target

65
Q

How do you find the cumulative purchdum when dealing with residuals?

A

Do actual purch dum + previous actual purchdum

66
Q

How to find % of cumulative purchdum?

A

(cumpurchdum)/total purchasers

67
Q

What is the point of doing the cumulative purchdum & %?

A

It helps you figure out how much a group of people will generate probability of potential sales (ex: looking at top 11 people and looking at % next to it, shows top 11 people will generate 15% probability of sales)

68
Q

What is breakeven response rate for?

A

The % given helps you see what predicted probability of purchase you should target (ex: if rate is 10%, should target ppl with purch prob of 10% or higher)

69
Q

What is support?

A

the amount of times you see antecedent or consequent in set

70
Q

With ABC analysis, what are you doing?

A

You are deciding you will offer either A, B, or C (bundle of attributes) to the market

71
Q

What are the downfalls of ABC?

A

Not establishing the best of ALL your options, just the best of the 3 options you are looking at

72
Q

How do you calculate value for each

A

QM (1 decks)

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