Download Math 146-key terms and formulas

Math 146.08 – Key terms/lecture guide; Update 12 Jan 2013
Lecture 1 - Introduction
Parameter, statistics, types of data, levels of measurement
Data collection
Independent and dependent variables
Lecture 2 – Descriptive Statistics
Construct a frequency table, calculate classes (between 5 and 20) and frequencies
Width = Range / n-classes
Determine relative frequencies, midpoints, cumulative frequency
Construct 2 types of histograms (and distinguish between them), ogives
Construct/Interpret dot plot, stem and leaf plot, scatter plot, time series plot (X = time)
Identify positive and negative correlation based on plot/visualization
Central tendency and formulas for samples and populations (mean, median, mode), shape
of distributions (e.g. skew, kurtosis)

x
N
x
x
n
weighted mean

(xw
)
x

w
mean of grouped sample

(
x
f)
x

n


f
n
Population and sample variance and standard deviation

(x

)2

N
2
2

(
x


)


N
2

(xx)2
s
n
1
2
2

(
xx
)
s
s
n

1
2
The Empirical Rule (or “68-95-99.7” rule)
Chebychev’s theorem
1
1
k2
Fractiles/Quartiles/Percentiles (Cf. ogives)
z-score
value  mean
x
z

standard deviation

Lecture 3 - Probability
Probability experiments & sample space
Fundamental counting principle (m * n)
Classical, empirical, subjective probability
Number of outcomes in event E
Number of outcomes in sample space
Frequency of event E f
P( E ) 

Total frequency
n
P( E ) 
0 ≤ P(E) ≤ 1
P(E) + P(E ′) = 1
P(E) = 1 – P(E ′)
P(E ′) = 1 – P(E)
Conditional Probability, independent and dependent events (mutually exclusive events)
P(B | A)
P(B | A) = P(B) or P(A | B) = P(A)
Multiplication rule (probability of A and B)
P(A and B) = P(A) * P(B|A)
P(A and B) = P(A) * P(B)
Addition rule (probability of A or B)
P(A or B) = P(A) + P(B) – P(A and B)
P(A or B) = P(A) + P(B)
Odds
Ratio of success to failures
Permutation
n! (0! = 1)
nPr
n!
n1 ! n2 ! n3 !   nk !
n Cr 
n!
( n  r )! r !