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Introduction to Statistics: Key Concepts and Definitions, Exams of Nursing

A comprehensive overview of fundamental statistical concepts, including variables, sampling, bias, association, causation, and statistical inference. It defines key terms, explains their significance, and illustrates them with examples. Particularly useful for students beginning their study of statistics, offering a solid foundation for understanding statistical methods and data analysis.

Typology: Exams

2024/2025

Available from 01/19/2025

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cases/units |- |correct |answer |subjects |or |objects |we |obtain |information |about
variable |- |correct |answer |any |characteristic |recorded |for |each |case
categorical |variable |- |correct |answer |divides |cases |into |groups
quantitative |variable |- |correct |answer |measures |or |records |a |numerical
|quantity |for |each |case
explanatory |variable |- |correct |answer |helps |explain |the |response |variable
response |variable |- |correct |answer |responds |to |the |explanatory |variable
population |- |correct |answer |all |individuals |or |objects |of |interest
sample |- |correct |answer |subset |of |the |population
sampling |bias |- |correct |answer |method |of |selecting |causes |the |sample |to
|differ |from |the |population |in |some |relevant |way; |cannot |trust |generalizations
|because |of |this
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cases/units |- |correct |answer |✔subjects |or |objects |we |obtain |information |about variable |- |correct |answer |✔any |characteristic |recorded |for |each |case categorical |variable |- |correct |answer |✔divides |cases |into |groups quantitative |variable |- |correct |answer |✔measures |or |records |a |numerical |quantity |for |each |case explanatory |variable |- |correct |answer |✔helps |explain |the |response |variable response |variable |- |correct |answer |✔responds |to |the |explanatory |variable population |- |correct |answer |✔all |individuals |or |objects |of |interest sample |- |correct |answer |✔subset |of |the |population sampling |bias |- |correct |answer |✔method |of |selecting |causes |the |sample |to |differ |from |the |population |in |some |relevant |way; |cannot |trust |generalizations |because |of |this

statistical |inference |- |correct |answer |✔how |to |use |the |information |in |a |sample |to |make |reliable |statements/gain |information |about |the |population representative |sample |- |correct |answer |✔resembles |the |population, |only |in |smaller |numbers simple |random |sample |- |correct |answer |✔all |groups |have |the |same |chance |of |becoming |the |sample; |each |unit |of |the |population |has |an |equal |chance |of |being |selected, |regardless |of |the |other |units |chosen |for |the |sample |-- |avoids |bias how |to |select |a |random |sample |- |correct |answer |✔not |haphazardly |(picking |on |our |own) |- |use |a |formal |method bias |- |correct |answer |✔exists |when |the |method |of |collecting |data |causes |the |sample |data |to |inaccurately |reflect |the |population how |bias |can |happen |- |correct |answer |✔1. |wording |of |questions

  1. |when |we |select |who |to |be |in |sample use |common |sense |to |identify |bias association |- |correct |answer |✔values |of |one |variable |tend |to |be |related |to |the |values |of |the |other |variable

proportion/relative |frequencies |- |correct |answer |✔number |in |that |category |divided |by |total |number proportion |for |a |sample |(symbol) |- |correct |answer |✔p-hat proportion |for |a |population |(symbol) |- |correct |answer |✔p two-way |table |- |correct |answer |✔used |to |show |the |relationship |between |two |categorical |variables outlier |- |correct |answer |✔observed |value |that |is |notably |distinct |from |the |other |values |in |a |dataset; |usually |much |larger |or |smaller |than |the |rest |of |the |data

|Q1 |- |1.5IQR < |Q3 |+ |1.5IQR symmetric |- |correct |answer |✔two |sides |approximately |match |when |folded |on |a |vertical |center |line skewed |to |the |right |- |correct |answer |✔if |the |data |are |piled |up |on |the |left |and |the |tail |extends |to |the |right median |< |mean skewed |to |the |left |- |correct |answer |✔if |the |data |are |piled |up |on |the |right |and |the |tail |extends |to |the |left

median |> |mean bell-shaped |- |correct |answer |✔if |the |data |are |symmetric |and |have |this |shape mean |- |correct |answer |✔numerical |average |of |the |data |values mean |of |a |sample |(symbol) |- |correct |answer |✔x-bar mean |of |a |population |(symbol) |- |correct |answer |✔ μ median |- |correct |answer |✔center |of |a |set |of |numbers; |splits |data |in |half resistant |- |correct |answer |✔statistic |is |relatively |unaffected |by |extreme |values; |median |is |this |but |mean |is |not variability |(spread) |- |correct |answer |✔data |is |generally |spread |out standard |deviation |- |correct |answer |✔statistic |that |measures |how |much |variability |there |is |in |data standard |deviation |of |a |sample |(symbol) |- |correct |answer |✔s measures |how |spread |out |the |data |in |x-bar |is

scatterplot |- |correct |answer |✔graph |of |the |relationship |between |two |quantitative |variables correlation |- |correct |answer |✔measure |of |the |strength |and |direction |of |linear |association |between |two |quantitative |variables always |between |-1 |<= |r |<= | 1 correlation |of |a |sample |(symbol) |- |correct |answer |✔r correlation |of |a |population |(symbol) |- |correct |answer |✔ ρ equation |of |a |regression |line |- |correct |answer |✔response |(hat) |= |a |+ |b |* |explanatory residual |- |correct |answer |✔difference |between |the |observed |and |predicted |values |of |the |response |variable observed |- |predicted |= |y |- |yhat least |squares |line |- |correct |answer |✔line |that |minimizes |the |sum |of |the |squared |residuals parameter |- |correct |answer |✔number |that |describes |some |aspect |of |a |population

statistic |- |correct |answer |✔number |that |is |computed |from |the |data |in |a |sample point |estimate |- |correct |answer |✔value |for |a |particular |sample |gives |this |of |the |population |parameter sampling |distribution |- |correct |answer |✔distribution |of |sample |statistics |computed |for |different |samples |of |the |same |size |from |the |same |population; |shows |us |how |sample |statistic |varies |from |sample |to |sample standard |error |- |correct |answer |✔SE; |standard |deviation |of |the |sample |statistic interval |estimate |- |correct |answer |✔range |of |plausible |values |for |a |population |parameter point |estimate |+/- |margin |of |error margin |of |error |- |correct |answer |✔number |that |reflects |the |precision |of |the |sample |statistic |as |a |point |estimate |for |this |parameter confidence |interval |- |correct |answer |✔interval |computed |from |sample |data |by |a |method |that |will |capture |the |parameter |for |a |specified |proportion |of |all |samples confidence |level |- |correct |answer |✔success |rate; |indicated |how |sure |we |are |that |our |interval |contains |the |population |parameter

summary |statistics |of |categorical |variables |- |correct |answer |✔frequency, |relative |frequency, |proportion correlation, |regression |line categorical |v |quantitative |(display |and |statistics) |- |correct |answer |✔side-by-side |boxplots statistics |for |the |quantitative |variable |within |each |category