stat unit 1 Flashcards

1
Q

Individual

A

smallest thing that will provide us with data/which data can be collected

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2
Q

Variable

A

any characteristic of interest of an individual

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3
Q

Categorical (qualitative) variable

A

a set of groups or categories

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4
Q

nominal level

categorical data

A

assosciated with words or names

ex. state of residency

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5
Q

ordinal level

categorical data

A

can be classified in a non-numeric order

ex. small, medium, large

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6
Q

Numerical (quantitative)

A

variable defined by a set of numbers

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7
Q

interval level

numerical

A

associated with a range of numbers, but 0 is not clearly defined

ex. temperature 0 degrees C = 32 degrees F

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8
Q

ratio level

numerical

A

can be compared between individuals, zero is clearly defined

Ex. $0 = absence of money

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9
Q

population

A

entire group of individuals

typically very large

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10
Q

Sample

A

portion of population that provides us with data

sample size (N) less than or equal to popoulation

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11
Q

Parameter

A

fixed unknown number describing some characteristic of the popoulation (fixed - stays the same bc population doesnt change, unknown - population is too big to find exact value)

characteristic examples: average, median, std. dev.

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12
Q

statistic

A

a varying and known number describing a characteristic of the sample
- varies with each sample taken from the same population
- used to estimate the perameter

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13
Q

observational study

A

only observe how individuals react within situations the individuals are already in
- researchers have no control over situations
- correlation

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14
Q

types of observational studies

A

surveys and ethical dilemmas
- describe a group or situation by passively collecting data

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15
Q

experiments

A

deliberately impose situations on individuals in order to see what happens
- describe a group or situation by actively collecting data
- cause and effect

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16
Q

Cause and Effect

A

Because of the situaton (imposed by researchers), something happened

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17
Q

Correlation

A

there may be a realtionship between the two, but we cant be certain

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18
Q

Bad Sampling Techniques (BST)

Voluntary Response Sample

A

Individuals participate only because they really care about the topic

Course evaluations, ppl who either love or hate the class participate

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19
Q

Bad Sampling Techniques (BST)

Convenience Sample

A

Individuals participate bc they are easy to contact

20
Q

Simple Random Sample (SRS)

A

Eliminates sampling bias, like a lottery
- N individuals chosen from the popoulation such that each individual of the populaiton has the same chance of being chosen
- each possible sample size N has the same chance of being chosen

Use table A for problems, 1-9

21
Q

Stratified Sample

good

A

Divide population into groups (strata) such that all individuals in a group have something in common
- take SRS from within each group
- Ex. pop. = clemson students, strata = freshmen, sophomore, etc, sample = randomly select 500 students from each group
- “some” OR “from each”

22
Q

Cluster Sample

good

A

Divide population into groups (clusters) such that all individuals in a group have something in common
- take SRS of groups, sample is all individuals within chosen groups
- Ex. pop. = clemson students, clusters = residence halls (bc there are many), sample = randomly select 5 residence halls

23
Q

Systematic Sample

good

A
  • order population in some way
  • randomly select a starting individual and a k integer, such that k is greater than 1 and less than the population size
  • sample = starting infividual and every Kth individual after that until desired sample size it met
24
Q

Sampling Errors

A

Use of bad sampling techniques
- fix = change sampling to SRS (stratified, cluster or systematic)

25
Q

Random Sampling Errors

A

Deviations between statistic and paramters
- statistic is consistently smaller/larger than the parameter
- fix = increase sample size

26
Q

Non-Sampling Errors

A

Errors not caused by sampling technique
- entire pop. isnt represented accurately = under coverage of a sub-population
- erroneous or multiple inclusion = subpop. counted too many times
- processing errors = mistake in mechanical tasks
- response errors = individual gives incorrect respons
- nonresponse = failure to obtain data

27
Q

observational studies

Slanted question

A

questions can be worded in a way to force a particular response
- biased

28
Q

observational studies

confusing words question

A

words may not be confusing to you but could be to the reader
- ex. football

29
Q

2 questions in 1

A

two questions might not be obvious, which would lead to greater misinterpretation

30
Q
A
31
Q

double negatives

A

confusing to reader

32
Q

properly ordered questions

A

if one question relies on or can be influenced by the answer from another, then they should be placed in approprate order

33
Q

matching

A

specific observational study when ethics are involved
-reasearchers dont control anything

34
Q

Experiments

Response Variable (RV)

A

dependent variable
- what we are hoping to see a change in
- measures outcome or result

35
Q

experiments

Explanatory Variavle (EV)

A

independent variable
- explains or causes change in the response variable

36
Q

experiments

Subject (S)

A

individuals studied in an experiment
- subject = single thing studied in experiment
- individual - single thing studied in an observational study

37
Q

experiments

treatment (T)

A

any specidic experimental condition applied, by the researchers, to the subject
- specific levels of the explanatory variable
- EV are general, these are specific

38
Q

experiment

Lurking Variable

A

Characteristic that affects the RV but is not included as an EV
-** inevitable**
-poorly designed experiment

39
Q

Confounded

A

two variables when their effects on an RV cannot be distinguished from one another

40
Q

Randomized Comparative Experiment (RCE)

A

designed in a way so that the treatments can be compared

41
Q

Double-Blind Experiment (DBE)

A

Neither the subjects nor the experimenters know which treatment each subject is recieving

42
Q

principles of experimental design

control

A

effects of lurking variables on the response variable by ensuring all subjects are affected by any lurking variables

43
Q

principles of experimental design

Randomize

A

Assigment of subjects to the treatments to ensure the groups are as similar as possible

43
Q

principles of experimental design

Use enough subjects

A

in each group to reduce chance variation in the results

44
Q

experimental designs

Completely Randomized Design (CRD)

A

All subjects are randomly distributed to the treatments
-hope to get the same number of subjects in each treatment group but not always possible

45
Q

experimental designs

Matched Pairs Design (MPD)

A

put subjects into pairs so that they are as similar as possible
- treatments are randomly assigned within each *pair *
- only two treatments
- sometimes each pair is a single subject who gets both treatments and the order of treatments is randomized

46
Q

experimental designs

Block Design (BD)

A

groups of similar subjects are put into “blocks”
- randomly assign treatments within each block
- Conclusion made for each embedded CRD, not one conclusion combining the infor
- based on most significant unavoidable sources of variability (gender, age, etc)