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Chapter 1-6, 10

STATS 13 Chapter Notes - Chapter 1-6, 10: Statistical Inference, Statistic, Sampling Distribution


Department
Statistics
Course Code
STATS 13
Professor
Tsiang, Mike
Chapter
1-6, 10

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ph
introduction
Steps
of
a
stats
investigation
:
i
.
ask
a
research
Question
that
can
be
answered
by
data
2.
design
a
study
and
collect
data
the
HOW
3
.
explore
data
patterns
4
.
draw
inferences
from
data
is
the
pattern
reliable
?
(
sample
size
)
5
.
formulate
conclusions
a
prude
o
no
Negara
una
conclusion
?
g.
look
back
and
ahead
limitations
,
im
pgµme
Statistical
inference
pillars
:
effect
1.
Significance
:
how
singer
is
the
evidence
?
pattern
2.
estimation
:
what
is
the
size
of
the
effect
?
3.
generalization
:
how
broadly
do
conclusions
apply
?
4.
Causation
:
can
we
determine
the
Caza
?
Basic
terminology
:
data
:
values
measured
/
categories
observational
units
:
individual
entities
on
which
data
are
recorded
eg
.
participants
*
variables
:
measured
quantities
>
quantitative
:
numbers
>
categorical
(
qualitative
)
:
categories
distribution
:
pattern
of
outcome

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

Pt
exploring
data
variability
:
how
predictable
is
a
certain
process
leituatnst
shape
of
dist
.
:
bimodal
?
k¥1
?
"
d
"
'
tht
dat
"
"
"
"
ha
"
mode
?
¥he
center
of
the
distribution
is
the
mean
,
nuhich
is
not
always
the
mode
>
Variability
is
measured
by
the
standard
deviation
t
variance
how
far
is
the
data
from
the
mean
Unusual
observations
:
outliers
don't
fit
pattern
pre
random
processes
difficult
to
predict
exactly
opposite
-
-
deterministic
random
processes
the
chances
of
any
of
the
events
/
possibilities
outcome
always
happening
is
the
same
-
he
can
be
repeated
the
same
infinitive
by
.
probability
:
proportion
of
outcomes
=
expected
in
the
long
run
total
probability
model
:
assumption
of
how
random
processes
may
generate
data
.

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

1
significance
is
the
effect
evidently
caused
by
the
suspected
cause
,
or
are
there
other
factors
?
(
on
,
strong
is
the
relationship
of
the
variables
4
the
data
?
>
how
do
we
know
if
results
were
caused
by
chance
?
null
hypothesis
:
there
is
no
relation
us
up
.
hypothesis
:
there
is
a
relation
I
are
results
statistically
significant
?
7
.
7
chance
models
are
the
Agata
points
far
apart
enough
to
be
statistically
significant
?
x
what
is
the
probability
of
.
.
.
if
random
event
?
Wil
compare
the
results
to
the
hull
Karen
doffs
)
t
what
is
the
actual
probability
?
within
a
J
.
Sample
:
set
of
observational
units
that
the
data
is
collected
from
.
relevant
population
statistic
:
number
I
percentage
that
summarizes
results
.
eg
.
"
416
upon
P
of
PM
.
sample
size
:
#
of
sets
of
data
.
eg
.
#
trials
parameter
:
long
run
numerical
property
of
a
process
.
STATISTIC
't
PARAMETER
otsttammpu
emoting
went
priosbaabi
lily
parameter
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