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78 Cards in this Set

  • Front
  • Back
statistics
science of collecting, summarizing, analyzing, and interpreting data.
descriptive statistics
summarizing data
inferential statistics
analyzing and interpreting
probablility
determining relative frequency of events
deductive thought process
known facts imply new facts
inductive thought process
observations infer properties
frequency distribitution
2 column table including values of the variable and its frequency
stem and leaf plot
displays distribution of values of quantitative variables.
skewness
direction of longest tail
histogram
graphs values of quantitative variables on x-axis and frequency on Y-axis
Sample mean (formula)
X/=Exi/n
variance (symbol)
s^2, distance from mean
sample Standard deviation (symbol)
s, square root of variance
scatterplot
plot elements as a point on 2-d axis
linear correlation coefficient
stregnth of relationship between x and y (r)
probability experiment
random process, distinct outcomes
outcome
result of probability experiment
sample space
S, set of all possible outcomes of probability experiment
event
collection of outcomes from S
null event
no outcomes
simple event
one outcome
union event (U)
or
Intersection event ()
and
mutually exclusive
no outcomes in common
formula for equally likely outcomes
P(A)=# of outcomes in A/# of outcomes in S
Independent events
knowing whether one event occurs doesn't change the probability that the other event occurs
complement rule (formula)
P(A)=1-P(A')
addition rule (formula)
P(AUB)=P(A)+P(B)
multiplication rule (formula)
P(AB)=P(A)P(B)
condtional probability rule (formula)
P(BlA)=P(AB)/P(A)
random variable
assign numbers to each outcome of a probability experiment.
discrete random variable
random variable that takes finite number of values
probability distribution of a discrete random variable
assigns probabilities to each value of a drv
mean of a discrete random variable (formula)
E(all y)yp(y)
continuous random variable
random variable whose value can be any number in 1 or more intervals
probability density funtion of a continuous random variable
f(y) likelihood
standard normal random variable (symbol)
Z, table-zx
standardizing random variable (formula)
z=Y-u/e
population
large set of elements of interest
parameter
characteristic of a population
sample
draw random with replacement from a population
statistic
characteristic of a sample
point estimator
random process used to obtain guess for a parameter
estimate
number obtained from point estimator
unbiased estimator
statistic used to estimate a parameter if the mean of its sampling distribution is equal to its parameter
sampling distribution X/(formula)
N(u,e/SRn)
interval estimator
point estimator +- MoE
MoE
table value x standard error
hypothesis testing
decision making process between two alternative statements about a population
type 1 error
rejecting true null hypothesis
type 2 error
failing to reject false null hypothesis
level of significance
compares severity of errors
test statistic
describes Ho
rejection region
values of the test statistic that reject Ho and support Ha
S.v. of T.s.
computed value of the test statistic using data collected
p-value
probablity of test statistic being more supportive of Ha
critical value
cut off between rejection region and acceptance region
independent random samples
random samples taken from each popuation with none of the elements in either sample related to any element in other samples
pooled estimator
weighted average of 2 sample variances
sampling distribution X/(formula)
N(u,e/SRn)
interval estimator
point estimator +- MoE
MoE
table value x standard error
hypothesis testing
decision making process between two alternative statements about a population
type 1 error
rejecting true null hypothesis
type 2 error
failing to reject false null hypothesis
level of significance
compares severity of errors
test statistic
describes Ho
rejection region
values of the test statistic that reject Ho and support Ha
S.v. of T.s.
computed value of the test statistic using data collected
p-value
probablity of test statistic being more supportive of Ha
critical value
cut off between rejection region and acceptance region
independent random samples
random samples taken from each popuation with none of the elements in either sample related to any element in other samples
pooled estimator
weighted average of 2 sample variances
response variable
random variable whose value is determined by a random process
explanatory variable
variable controlled by experimenter
goals of regression analysis
determine relationship between the explanatory variable and response variable and find the best fit line
outlier (formula)
Q1-1.5 x IQR, Q3+1.5 x IQR
sample proportion (formula)
p^, x1+x2+x3.../n