reviewer Flashcards

1
Q

the science of gathering, analyzing, and interpreting data.

A

STATISTICS

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2
Q

the process of gathering information from various sources to make informed decisions.

A

DATA COLLECTION

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3
Q

Types of Data Collection

A

P - primary Data
S - secondary Data
Q - qualitative Data
Q - quantitative Data

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4
Q

Directly gathered from the source through methods like surveys, interviews, or experiments.

A

Primary Data

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5
Q

Collected from existing sources like reports, articles, or databases.

A

Secondary Data

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6
Q

Non-numerical information such as opinions and experiences, collected through interviews, focus groups, or observations.

A

Qualitative Data

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7
Q

Numerical data, gathered through structured methods like surveys or experiments, often used to measure and analyze trends.

A

Quantitative Data

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8
Q

Common Data Collection Methods

A

S - survey
I - interview
F - focus group
O - observation
C - case study
E - experiments
S - secondary data analysis

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9
Q

Asking questions to individuals or groups, often conducted online, by phone, or in person.

A

Survey

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10
Q

One-on-one conversations to gain in-depth insights.

A

Interview

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11
Q

Group discussions led by a facilitator to explore specific topics.

A

Focus Group

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12
Q

Watching and recording behaviors or events in their natural setting.

A

Observation

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13
Q

Testing hypotheses by manipulating variables to observe effects.

A

Experiments

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14
Q

Detailed analysis of a single subject or event.

A

Case Study

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15
Q

Using existing data collected for other purposes.

A

Secondary Data Analysis

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16
Q

Steps to Collect Data

A

D - define your problem
I - identify data source
S - select your method
P - plan the process
T - test the process
C - collect the data
A - analyze the data
R - report the findings

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17
Q

Review and interpret the data to draw conclusions.

A

analyze the data

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18
Q

Things to consider in collecting data

A

V - validate the accuracy and consistency
P - protect data from unauthorized access
R - respect privacy and ask for consent if necessary
M - match your analysis to your data type
K - keep data well-structured

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19
Q

The Engineering Method

A

D - define the objective
I - identify key factors
P - propose a model
C - conduct experiment
R - refine the model
D - develop a solution
V - validate the solution
D - draw a conclusion

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20
Q

Clearly describe the issue you’re addressing.

A

Define the problem

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21
Q

Determine the important factors that affect the problem or its solution.

A

Identify key factors

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22
Q

Use your scientific or engineering knowledge to create a model of the problem, noting any limitations or assumptions.

A

Propose a model

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23
Q

Test or validate your model with appropriate experiments and data collection.

A

Conduct experiment

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24
Q

Adjust the model based on the data you’ve gathered.

A

Refine the model

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25
Use the refined model to create a solution to the problem.
Develop a solution
26
Perform an experiment to ensure the solution is both effective and efficient.
Validate the solution
27
Summarize the results and suggest further actions based on your findings.
Draw conclusion and make recommendations
28
Collecting Engineering Data
R - retrospective Study O - observational Study D - designed Experiment
29
Analyzing historical data that’s already been collected.
Retrospective Study
30
Watching and recording data from a process in real-time with minimal interference.
Observational Study
31
Conducting controlled tests where we purposely change certain variables to see how they impact outcomes.
Designed Experiment
32
a method of asking a large group of people a few focused questions to gather information.
Survey
33
The researcher asks questions directly.
Face-to-face survey
34
Participants complete the survey on their own, either on paper, by mail, or online.
Self-administered survey
35
Steps for Planning, Conducting, and Analyzing an Experiment
I - identify the problem C - choose factors, levels and ranges S - select the response D - design the experiment C - conduct the experiment A - Analyze the data D - draw conclusion and make recommendations
36
(denoted by Ω) a set that contains all possible outcomes of a random experiment.
Sample space
37
a sample space with a limited number of outcomes
Finite sample space
38
A sample space with infinitely many possible outcomes.
Infinite sample space
39
subset of the sample space. It includes one or more outcomes that satisfy a certain condition.
Event
40
A single outcome of the experiment.
Simple event
41
An event that contains more than one outcome
Compound event
42
This event occurs if either event 𝐴 or event 𝐵 (or both) occur. It represents the combination of outcomes from both events.
Union
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This event occurs if both 𝐴 and 𝐵 happen at the same time. It represents outcomes that are common to both events.
Intersection
44
This event occurs if event 𝐴 does not happen. It includes all the outcomes in the sample space that are not in 𝐴.
Complement
45
Two events are _ _ (or disjoint) if they cannot occur at the same time. In set notation, this means 𝐴 ∩ 𝐵=∅.
Mutually exclusive event
46
These laws describe the relationships between the complements of unions and intersections of events
De Morgan's Law
47
all about sequences where the order matters.
Permutations
48
The order doesn't matter.
Combination
49
When items can repeat,
Permutations with repetition
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When items can't repeat
Permutations without repetition
51
A factorial is the product of all numbers from 1 up to a given number 𝑛
Factorials for permutations
52
involve selecting items from a set where each item can be chosen more than once, and the order of selection does not matter.
Combination with repetition / multiset combination
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involve selecting items from a set where each item can only be used once, and the order of selection does not matter.
Combination without repetition
54
involve selecting items from a set where each item can only be used once, and the order of selection does not matter.
Combination without repetition
55
Rules of probability
R1 - probability of an event R2 - probability of the impossible event R3 - probability of the certain event R4 - additivity rule
56
For any event 𝐸, the probability 𝑃(𝐸) lies between 0 and 1
Probability of an event
57
The probability of the empty set ∅, representing the "impossible event," is always zero
Probability of the impossible event
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The probability of the sample space 𝑆, which represents the "certain event," is always 1
Probability of the certain event
59
If an event 𝐸 can be broken into disjoint (non-overlapping) events 𝐸1,𝐸2,𝐸3…, then the probability of 𝐸 is the sum of the probabilities of its disjoint parts
Additivity rule
60
the probability of an event occurring given that another event has occurred. The conditional probability of event 𝐴 given that event 𝐵 has occurred
Conditional probability
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Two events A and B are independent if the occurrence of one does not affect the probability of the other.
independence of two events
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provides a way to update the probability of an event based on new information.
Baye's theorem
63
a function that assigns a numerical value to each outcome in a sample space.
Random variable
64
Take on a countable number of distinct values.
Discrete random variable
65
Take on an infinite number of values within a range or interval.
Continuous random variables
66
describes the probabilities associated with the possible values of a random variable.
Probability distribution
67
lists the possible values of the random variable along with their respective probabilities
Probability mass function
68
denoted by 𝐹(𝑥), gives the probability that a random variable 𝑋 takes a value less than or equal to 𝑥.
Cumulative distribution function
69
The expected value of a random variable is a measure of the central tendency
mean of discrete random variable
70
measures how far the values of 𝑋 are from the expected value 𝐸(𝑋) on average.
Variance of random variable
71
the square root of the variance, providing a measure of the spread of values in the same units as the original data.
Standard deviation
72
a probability distribution where each outcome in a finite set of 𝑛 equally likely outcomes has the same probability.
discrete unifom distribution
73
a discrete probability distribution that models the number of successes in a fixed number of independent trials, where each trial has only two possible outcomes: success or failure.
Binomial distribution
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a trial with only two possible outcomes is used so frequently as a building block of a random experiment.
Bernoulli trial