68 terms

Statistics Exam 1

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population
complete set of people or things being studied
population parameters
specific numbers describing characteristics of the population
sample
subset of the population from which data are actually obtained
raw data
the actual measurements or observations collected from the sample constitute
sample statistics
numbers describing characteristics of the sample found my consolidating or summarizing the raw data
margin of error
used to describe the range of values, or confidence interval, likely to contain the population parameter
from (sample statistic-margin or error)
to (sample statistic + margin of error)
census
the collection of data from every member of a population
representative sample
sample in which the relevant characteristics of the sample members are generally the same as the characteristics of the population
bias
if a study is designed to favor certain results
random sample
every member of the population has an equal chance of being selected to be part of the sample
simple random sampling
every possible sample of a particular size has an equal chance of being selected
systematic sampling
using a simple system to choose the sample, such as selecting every 10th or 50th member of the population
convinence sampling
using a sample that happens to be convenient to select
cluster sampling
we first divide the population into groups and select some of these groups at random. we than obtain the sample by choosing all the members within each of the selected clusters
stratified sampling
we use this method when we are concerned about differences among subgroups within a population. we first identify the strata and then draw a random sample within each stratum. The total sample consists of all the samples from the individual strata
observational study
we observe or measure characteristics of the subjects, but do not attempt to influence or modify these treatments
experiment
researchers apply some treatment and observe its effects on the subjects of the experiment
variables of interest
items or quantities that the study seeks to measure
explanatory variable
variable that may explain of cause the effect
response variable
variable that responds to changes in the explanatory variable
retrospective (case- control study)
observational study that uses data from the past to learn about issues of concern
ex. effects of alcohol on children of pregnant mothers
prospective (longitudinal) study
set up to collect data in the future from groups that share common factors
ex. harvard nurses' health study
treatment group
the group of subjects who receive the treatment being tested
control group
subjects who do not receive the treatment being tested
confounding
when different variables are mixed so we cannot determine the specific effects of the variables of interest
confounding variables
variables that lead to the confusion
placebo effect
when people improve because they believe that they are receiving useful treatment
placebo
something that looks or feels just like the treatment being tested but lacks its active ingredients
experimenter effect
when a researcher or experimenter somehow influences subjects through such factors as facial expression, tone of voice, or attitude
single-blind experiment
when the participants don't know which group they belong to, but the experimenters do
double-blind
neither the participants nor any experimenters know who belongs to the treatment group and who belongs to the control group
meta-anaylsis
researchers review many past studies
qualitative data
consist of values that can be placed into nonnumerical categories
quantitative data
consist of values representing counts or measurements
can be classified as continuous or discrete
continuous data
data can take on any value in a given interval
discrete
data can take on only particular, distinct values and not other values in between
nominal(names for categories) level of measurement
characterized by data that consist of names, labels or categories only. cannnot be ranked or ordered
ordinal (order) level of measurement
applies to qualitative data that can be arranged in some order (low to high)
interval level of measurement
applies to quantitative data in which intervals are meaningful, but ratios are not. data at this level have an arbitrary zero point
ex. farenheight scale
ratio level of measurement
applies to quantitative data in which both intervals and ratios are meaningful. data at this level have a true zero point
ex. weight
random error
occurs because of random and inherently unpredictable events in the measurement process
ex. trying to get a moving babies weight
systematic error
occurs when there is a problem in the measurement system that affects all measurements in the same way
absolute error
describes how far a claimed or measured value lies from the true value
relative error
compares the size of the absolute error to the true value
r.e.= a.e. / true value x 100
accuracy
describes how closely a measurement approximates a true value
precision
describes the amount of detail in a measurement
absolute change
the actual increase or decrease from a reference value to a new value
a.c. = new value- reference value
relative change
the size of the absolute change in comparison to the reference value and can be expressed as a percentage
r.c.= new value- ref value/ ref value x 100%
absolute difference
the difference between the compared value and the reference value
relative difference
describes the size of the absolute difference in comparison to the reference value and be be expressed as a percentage
index numbers
provides a simple way to compare measurements made at different times or in different places
mean
average value
median
middle value
mode
most common value
outlier
value that is much higher or much lower than almost all other values
symmetric, normal, bell-curved
if its left half is a mirror image of its right half
ex. heights of women
left-skewed
if its values are more spread out on the left side (tail-end if left)
ex. olympic qualifying long jumps results (meters)
right-skewed
if its values are more spread out on the right side (tail end right)
ex. family incomes
range
difference between its highest and lowest data values
first quartile (lower q)
divides the lowest fourth of a data set from the upper three fourths. it is the median of the data values in the lower half of the data set
second quartile (middle)
overall median
third q (upper q)
divides the lowest three-fourths of a data set from the upper fourth. it is teh median of the data values in the upper half of a data set
five- number summary
low value, lower quartile, media, upper quartile, high value
nth percentile
divides the bottle n% of data values from the top
standard deviation
quantity calculated to indicate the extent of deviation for a group as a whole
z-score (standard score)
the number of standard deviations a data value lies above or below the mean
positive for data values above the mean and negative for data values below the mean
z= data value- mean
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standard dev.
empirical rule
within 1 s.d. of mean = 68
within 2 s.d. of mean= 95
within 3 s.d. of mean= 99.7
central limit therom
as sample size increases, the shape of sampling distribution becomes more normal