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AP Stats Chapter Notes (Ch 1-6) (1)

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ADVANCED
PLACEMENT
Statistics
CHAPTER 1: DATA ANALYSIS
*
EEEEE
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SECTION 1.1:
categorical
the distribution of
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CHAPTER
MODELING
2:
DISTRIBUTIONS
QUANTTATIVE
SECTION 2.1:Describing Location
in
Distribution
a
Percentiles
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to it,
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us
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·
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The area
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=
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you
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left-skewed
right-skewed
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-
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density
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completely
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specified by
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and
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A
CHAPTER
EXPLORING TWO-VARIABLE
QUANTIATIVE DATA
3:
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irection (positive, negative, none
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outcome
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a
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appropriate.
simple random sample (SRS)
·
CHAPTER
4: COLLECTING DATA
SRS?
·
Technology:
individual
Label: Label each
n
-
RNG to
an
get
that
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to the integers.
the sample.
strata
are
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with a distinct
with the same number of digits
two digits use
using
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the appropriate
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across a
length from left to right
line in table D, lignore repeats,
if necessary) until number of
selected.
size desired are
sample
Label:
estimates
of
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population
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paper:
·
letters
or
in
a
bowl
hat, shuttle the papers
let
individuals take
easier
(no
replacement).
based
Select: Group individuals
paper they got.
on the slip of
from
observes
-
but
of interest
a
to
ttempt
the
influence
20
treatments
of
size
2.
In
units
are
paired
two treatments
assigned
individuals
unit receives
a
compare
Group
(10)
F
number
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&
additionalencompare
->
worker
Group 2
->
a productivity
the same
lighting
DESION
(h)
I
Treatment I
combine
(n)
(n)
-
random
assignment
&
compare
Treatment)
compare
(r)
f
t
->
treatments
(x)
example:
random
eshmen
#
a
randomly
grade
levels
-
->
number
↑
generator -
(100)
- &ophomores
->
(400)
within each pair.
In others, each
in
->
&
Block ->
MPD,
and the
are
assigned
7
2->Treatment 2
Ch)
very similar experimental
two
(n)
Group
uses blocks
some
to assign
effective.
Treatment 1
->
number
generator X
I
compare
1
sources
a
inClass
combine results
&
compare
X
compare
in-class -> scores
a
2208)
A track coach wants to know whether his long-distance
runners are faster running the track clockwise or
counterclockwise. Design an experiment that uses a
matched-pairs design to investigate this question. Explain
your method of pairing.
in such
effects
way
a
cannot be
distinguished from
each other.
·
that
placebo: treatment
active
otherwise
no
ingredient,
an
·
has
is
like other treatments.
·treatment:
applied
but
a
specific
condition
the individuals in
to
to which
a
subjects:
human
are
experimental units.
variable
Factors: an explanatory
that
cause
is
manipulated
a
change
variable.
survey
question.
Description:
- Form blocks based on grade level
(Individuals + Blocks) because scores
on the geometry final exam are
likely to vary by grade level since
Freshmen who takes geometry tend
to be more advanced in their math
coursework.
- Assign each individual student
from 1 to 100 for Freshmen. Use a
random number generator to obtain
50 random integers (random
assignment), select these students
and assign them to online (Block 1 +
Treatment 1). The remaining
students are assigned to teacher
taught (Block 1 + Treatment 2).
- Assigned each individual student
from 1 to 400 for Sophomores. Use
a random number generator again to
obtain 200 random integers (random
assignment), select these students
and assign them to online (Block 2 +
Treatment 1). The remaining
students are assigned to teacher
taught (Block 2 + Treatment 2).
- At the end of the course, let them
take the same geometry final exam
and compare the scores (compare).
- Once all students have taken the
test, and the scores have been
compared for each treatment for
each block, then combine the results
and compare (combine and compare).
and may
in the response
levels
inactive treatment.
·double-blind:
neither the
·
·
can
subject is receiving.
either the subjects
the people who interact
or
with them and measure the
response variable don't know
which treatment
a
units
using
are
a
subject
is
experimental
assigned to treatments
chance process.
experimental
each
a
treatment
group of experimental
units that
are
experiment
before
known
the
to be similar in some
to affect
way that is expected
the response to the treatments.
·
single-blind:
divingassignment:
rare
all
distinguished.
be
block:
subj
a
for
replication: giving
units so
enough experimental
that any difference in the effects
·
those
treatment
control: keeping other variables
constant
units.
who interact with
them and measure the
which
response variable know
nor
·
beings
x
control group: used to provide
baseline for comparing the
a
even an
treatment is
the
·
a
effects of other treatments.
placebo effect: describes the
fact that some subjects in an
experiment will respond
favorably to any treatment,
unit: the object
randomly assigned.
·
Factors
·
experiment.
experimental
a
*
response variable
on
systematic
to
combination of treatments?
when
associated
that their
are
two variables
values of
Factor-
occurs
·confounding:
levels: different
·
VOCABS988
random order.
Description:
Have each long distance runner race 1 mile in each
direction. Some runners are faster than others, so using
each runner as his or her own “pair” accounts for variation
in 1-mile race times among the runners. For each runner,
randomly assign the order in which the treatments
(clockwise a nd counterclockwise) are assigned — by
flipping a coin. Heads indicates the runner will race
clockwise first and counterclockwise second; tails indicates
the runner will race counterclockwise first and clockwise
second. Allow adequate recovery time between the races.
For each runner, record the 1-mile race times for each
direction.
1503
online
(200)
random
experimental
both treatments
example:
online
a
Description:
- Number the companies from 1 to 20
(20 individuals)
- Use a random number generator to
produce 10 different random integers
from 1 to 20 (random assignment)
- Select the first 10 different
integers (Group 1) and assign them to
additional lighting (Treatment 1)
- Select the remaining companies
(Group 2) and assign them to the
same lighting (Treatment 2).
- Compare the increase in worker
productivity between the two groups.
treatments.
groups.
For
pattern of inaccurate
(n)
BLOCK
DESIGN
design for comparing two
that
X
Group"
is
there
on
between
differences
chance
random
experimental
common
a
is
treatment
a
treatment
chosen
sample can't be contacted.
answers
tabled)
likely
when
response bias: occurs
Treatments
ED
moman
PAIRS
more
of the
less
are
individual
the
not
(slips of paper, RN6,
units to treatments. This helps create
Block
individuals to
measure their responses.
on
an
is
population
units so
enough experimental
treatments can be distinguished
effects of the
assignment
companies
BOMIZED
delibaretly impose
treatments
or
members
population
monomn andreassignmenttorepeaterintothebroch
(conditions)
->
scompare
tex
random
↑
respons. Experimental
-
if
decide
each
random
individuals
does not
some
representative of
a
chance process
example:
measures variables
and
a
WRONG ???
occurs when
undercoverage:
population.
compares two
that
design
->
Observational
·
pattern
a
of the study.
be
be chosen or cannot
to
chosen in a sample.
individual.
when
occurs
in -nonresponse:
the sample may
the
ordered,
-
RANDOMIZED DESIGN
STUDIES
OF
iF there's
over or
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AVOID BIAS,
WE MIGHT
else can go
but what
k to
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the way the
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from Ito
first individual,
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mmmminLEET experimental
individuals
TYPES
to
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paper,
method
to
which leads
EXPERIMENTAL DESIGN:
a
Replication: Impose
and
one
the
open invitation.
sampling
of these
BIAS,
study b/c of
sampling
and choose every kth
be
sampling
individuals choose
be a part of the
both
*
treatments are imposed
roughly equivalent groups before
avoid
Control: keep other variables the same for all groups. Control helps
variation in responses, making it
confounding and reduces
·
Randomize: Put
random
value
a
identify
time & money.
Random Assignment: Use
identical
on
s lips of paper.
select
all the
experimental
Write corresponding
numbers
Use
Comparison:
·
·
OF
to
individual
selects every 4th
&
based on the population size
desired sample size. Randomly
but similar between
BASIC PRINCIPLES
-
-
select
Randomly
ichooses individuals
to reach
·voluntary
systematic
·
different
within (HETEROGENEOUS
saves
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paper
of
sampling
Convenience
easiest
x
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c lusters are
than SRSs.
randomly
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and
precise
more
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selected
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stratified
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x
ter
in the sample.
included
are
samples tend to give
individuals that
Select: Choose the
correspond
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identical slips
clusters
individuals in the chosen
diFF
same
·
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these clusters and
similar within
CHOMOGENEOUS), but
individual
each
Label: Label
to
SRSS
these
combine
form
near each other.
SAMPLES
remains
SAMPLING
sure to do
WITHOUT REPLACEMENT
doing SRS.
when
-
into non-overlapping groups
individuals that are located
of
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to be chosen
chance
sampling
the population
divide
-
their
response. Then choose a
stratum & then
select: Choose the individuals
↑able D:
way)
in some
affect
the
given size
cluster
·
separate
necessary).
who correspond
sampling
divide the population in
different integers (ignore
repeats, if
random
Strata (similar
N.
1 to
From
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stratified
possible sample
#
+
HOW TO CHOOSE AN
a
every
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gives
of
MPLING
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TYPES OF SAMPLING
POPULATION
-
sampling variability different
random samples
size from the
& iFF.
·
of
the same
same
pop. produce
estimates.
Statistically significant: the
·
bserved
are
by
results of
too unusual to be
chance alone.
a
study
explained
SAMPLING VARIABILIN & SAMPLE
random samples tend to
Larger
true
the
C RITERIA
closer
that are
produce estimates
to
SIZE
the
1)
value than
population
FOR
and the response variable is
larger
words, estimates from
samples are
2)
the
or
STAMSTICALLY SIGNIFICANT
% <5%, yes,
IF
*
3)
and
significant
have happened
Alleged
4)
by chance alone
0> 5%,
*
IF
and it
the alleged
PERCENAGE CP-VALUES
Identify
the difference in
1)
2
make
a
simulation
Identify
in
or
equal to
4)calculate the
or
equal
compare
5)
State if
to the
to the
the
significant
of
the
greater
in the
OF
From
than
Random
to
context
plausible.
All
INFERENCE
of
the individuals
were
will
much
7
used
be
Assignment
of individuals
of error:
margin
creates
interval
an
of
values.
↑lausible
+margin of
sample
error
-
estimates
conducting
*
random
re
the
use
Randomize
(sliprointeC
select
a
effect.
WERE
lot
INDIVIDUALS
SELECTED?
NO
and
INDIVIDUALS
who
& ata First.
are
RANDOMLY
RANDOM
ALWAYS
IS
IMPORTANT 888
ASSIGNED
TO
GROUPS?
NO
YES
YES
Inference about causedeFFect:
Inference about population:
projects
collecting your
information
Inference about population:
YES
your future
collecting
of
know
do
you cannot
studies
is all
data. There
it's very
things since
these
12 since
through
about
so
YES
RAN DOML
the
essential to
chapters
in
Chapter it
and
sampling variability
In
*
here, but
these notes
*
individuals
SUMMARY:
WERE
The cause is believable, not
f
about
groups allows inference
cause
is
cause
- All individual data must be kept
confidential. Only statistical summaries
for groups of subjects
and
chosen.
·
in the
effect
assignment:
inference about the population
which
shows the
rull
problem.
Random selection
a low
might
be
difference.
5%rule
not
continued application
time. The
- All individuals who are subjects in a
study must give their informed consent
before data are collected.
study is statistically
or
ME SCOPE
·
the difference
mean
in
individuals don't.
- All planned studies must be reviewed
in advance by an institutional review
board charged with protecting the
safety and well-being of the subjects.
dotplot
percentage of
many dots are
now
mean.
step 1.
from
mean
some
and
ADexam
how many dots are
3)
greater
and
group
associated with
are
DATA ETICS
IDENTIFYING THE
OF
cause
to one
have consistency in the
remain It
by coincidence only.
PROCESS
variable specific
possible.
happened
may have
of what the
5)
significant
not statistically
precedes effect
long
it is
no,
cause
link
the association.
responses. The individuals
variable,
a
response variablethis reduces
other
some
of the explanatory variable
values
stronger
explanatory
it is
Statistically
itmay
larger
that
study explains
one
explanatory variable
Many studies of different kinds shows
consistent.
the chance
the
strong.
between
the explanatory and
precise
more
is
association
EXPERIMENT:
WAH WE CAN'T DO AN
The association between
strong.
is
association
random samples. In other
smaller
ESTABLISHING CAUSATION
No
YES
Inference about causedeFFec:
Inference about population:
Inference about
cause
deFFect: NO
Inference about population:
Inference about
cause
YES
No
dCFFer: No
5: PROBABILITY
CHAPTER
.
efinitions:
D
Formulas:
·
·
of outcomes
number
total
of outcomes
number
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=
↑(A)
space
in sample
1
·
probability
law of
·
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Rule:
P(AUB)
·
P(A)
+
=
P(B)
-
·
mutually exclusive
·
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simulation
·
to 1
if
-
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trials of any
more and more
observe
proportion
process, the
P(ANB)
-
no
-
imitates
event can
random
a
outcomes are
approaches the true
happen at
process
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such
time.
the same
simulated
way that
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consistent with real-world
outcomes.
simulation process:
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*
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·
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sample space
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purely
probability.
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determined
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between
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that
outcomes
generates
by chance.
outcome
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events:
=
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random
P(AVB) P(A) + P(B)
·
process
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=
for mutually exclusive
Rule
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random
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rule:
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conditional
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list
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probability
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the
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answer
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that
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probability
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probability
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least
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convincing
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From
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2
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3
4
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if there is
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CHAPTER6:
Probability Distributions
EFINITIONS:
D
FORMULAS:
·
Discrete
variables
Random
P(X
*
P(X ()
P(X 1k)
P(x
=
*
Mx E(X)
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kth)
P(X
+
+ (xi)(Pi)
+ ...
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-
k + D +... +
=
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size, number
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mean:
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and
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CONTEXT:
IN
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variable
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