Statistics Basics and Probability
Statistics Basics and Probability
M chon
I'
pop mean
: =
+
basics Ax s --
...
: - # of terS
add all numbers ->
N ->
assigning probabilities :
-
mean :
sample mean :
I
most frequent
mode ↓ possible of
:
outcomes
/sample
#
[(yY
2
variance : =
n e e
: #of times an event
n occurred
Ch02 1
-
guessing
- e
subjective
:
class midpoint =
Fancy
-
total #items in dataset
cumulative total frequency
total o f frequency distribute i s expirement process that produces outcomes
equency
=
-
· event
=
outcome of an explrement
I
- can't be broken down
law of addition RY
aggregate
elementary -
X &
I
population =
M Ch-03 broken down
:
I can be
sample
=
I measures of variability describe
sample space
=
COMPLETE roster of all
levels of data : the spread/dispersion of data
elementary events for an expirement
multiplication as X AND Y
see ->
independent
(v) combo of events (X &RY)
E. xMome
union
=
happen
oraialerarglordering
in e must
3 . MAD =
u nominal
categorize R
④ elements
sets
of squared intersection (f) common both
-
=
to
occurrence of event
actexcursive
one
Velocity coefficent mutually
:
prevents occurrence a ene
veracity standard deviation baye's rule :
·
(No intersection) &P(X(y) =
0
-
volume
-o
=
/Ex
now to create a FD
n complementary
A
P(A) =
1 -
P(A) & YA(B) P(B(A) X P(A)/P(B)
I
events A +
Not a - =
:
able : ,
sample size
↓
->
~
1 find range size
:
.
sampling
E
w
# of classes -
determine N!
sampling wo replacement
:
2
Crangel
.
Width * I 10 - 68 %
3 Find class
-
classes) TN-n) !
.
Itzocass
n
for
boundaries
.
n .
find
7 %
each class
* =
30599
-
set
frequencies t
5-count
- We
I
y matrix :
up table
-
I
↓
-
discrete distribution
himdismbuto
probability of
+
normal distribution
It
Chos is always I : A
-
I
vanable mean and sk for i X
random variable
A
continuous
:
M
=
X ·
upper t lower bound Es
mean
i
(a) (b) of
parameters (M
and X
discrete random variable
I
:
& =
2 -
values that a re
produce
auda aene
numbers · rules stuff :
negative whole
)
non
+
b - a
continuous random variables
:
s
M
=
# of
discrete distributions
tota
pos sible valued 2 zbyz =
2 1 .
list / calculate regular/given
5= P(Xi) probabilities
word
"
P(z( )
x;
mean
:
M =
G(X) =
1
↓
·8
= 2 p(Xxa) conditional
weighted arg ! calculate regular
· =
possible
KeraP
↳ a
2
2
-
value
variance of DD
:
-
using probability to solve for mean
,
SD ,
or probabilities
distribution
exponential
joint probabilites
2N((Xi-m)2] calculate
-
·
PIX In a normal distribution : 3
an X-value
·
events
.
· single in
add
I
and
-
3 I
'n' identical trials Ak & >0
involves - sample
size f(x) =
Re-NY for x = 0 Where
'sucess' outcome ,
fail' or
Vn
·
o =
p q
customers
P
. .
Q =
1 -
=
Prob of
fallure
·
·
P(Xx 8) =
calculate x =
1 - X 7
Meant Standard Deviation
=
M Y/x ; 0
=
1/x
poisson distribution
=
>
could be # of discrete
=
events in a of time *
given period ↑ p(X(x) = 1 -
·
X = # of events PER time period xx x2
P(X X(x2)
- -
, =
e -
e
·
can also be
given intervals ,
distance
or area
e
X
pX
n -
nCy q
·
· .
poisson formula :
treal
! probability of
·
=n
p=
et
-
-
-
Hofthals . e
H -
interval sex-times x =
# of successes desired f or fails
in 5
I
Ch07
-
from samples +
sampling data gathered
:
random sampling
:
every pop .
unit has the same
probability of
being selected
nourandom sampling :
sampling distribution of X :
·
X
=
sample mean
normal If 30
= :
n
central limit theorem : ->
standard /Ze
mean X M
My
of = =
of mean) =
LaKa standard
error the op
=
SD
Fr
n
=
sample size
If I is normal ,
standardize to find probability
answer on z table
and look up
the characterish
n
= sample size
p(wq
1 -
P)
proportion is
=
If population
then
is normal If npLS and ng>5
of B M P
= =
· mean
proportion) op /
=
=