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Stats Final Exam Terms
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Terms in this set (69)
Statistics
science of making predictions about a population parameter based on a sample statistic; number that represents the sample
Population
large group that is too big to study every individual
Parameter
number that represents the population
sample
small group that we have information about
Statistics depends on
variation and data
Variations
natural differences between objects
data
information in contect
individual
any single object from a population
variable
characteristic of an individual
types of variables
numerical and categorical
frequency
the number of times a data point is observed
two way tables
common way to display categorical variables
placebo effect
when people think something is happening because they expect it to
observational studies
individuals self select which group they're in
controlled experiment
researchers decide the group a participant is in
histogram
a bar graph where vertical axis is frequency
spread
a measurement of the variation in a data set
Standard Deviation
measures typical deviation
deviation
something different from typical
x bar
man of variable
Sx
standard deviation of variable
Empirical rule
predicts the percentage of the data that will be found in a given range
z score
how many standard deviations a data point is away from the center
The farther a Z score is from O
the more rare the data point is
Standard Unit
how many standard deviations away an observation is from the mean
median
value in the middle
quartiles
divides the data into quarters; length of the line represents how spread out the data is
Interquartile range
Q3-Q1
The median and the IQR are
resistant to outliers
box plots
common way to display data
Whiskers
extend to largest data point inside the fence
outliers
anything outside a fence
Fences
1.5 IQR away from the interquartiles
Upper Fence
Q3 + 1.5 IQR
Lower Fence
Q1 - 1.5 IQR
Probability Experiment
any reputable process where the outcome is unknown
Sample Space of a Probability Experiment
collection of all equally likely outcomes
Equation of Probability of E
P(E) = Size of E / Size of S
The Probabilty of any event is
between 0 and 1
Trial
each time a probability experiment is done
As the number of trials increases
the closer the empirical probability gets to the theoretical probability
A and B are independent when
knowledge of event B does not change the probability of event A
Independence
variables are not related
Law of Large numbers
as the number of trials increase, the empirical probability gets closer to the theoretical probability
Probability distribution function
a function that gives the probability of each event in the sample space
random variable
the outcome of a probability experiment; can be discrete or continuous
intermediate value
a number between two values
Binomial Distribution
a pdf that gives the probability of a proportion from a sample
bias
the statistic provides an untrue value for the parameter
sampling bias
when the sample is collected in a way that it doesn't represent the bias
measurement bias
when the poll question is asked in a way to produce untrue values
Response bias
people have a tendency to lie about a subject
non response bias
when a small proportion of a sample responds
Probability distribution function
tells us the chance of getting any of these events
central limit theorem
distribution of samples is normal and will always create a bell curve
CLT must have
large sample size; large population
confidence intervals
a range of plausible values for the parameter
Null Hypothesis
a conservative, status-quo, business as usual statement about a population parameter
Sample statistics
calculated values which are facts that cannot be disputed by hypothesis
alternative hypothesis
hypothesis contrary to the null hypothesis
Significance level
the probability of rejecting the null hypothesis when, in fact, the hypothesis is true
the null hypothesis is assumed to be
true unless observation strongly indicates otherwise
The further a test statistic is from 0
the more the null hypothesis is discredited
A p value smaller than the Sig. Level
discredits the null hypothesis; reject
A p value larger than the Sig Level
indicates the null hypothesis is probably true; accept
Type I error
Ho isnt rejected but should be
Type II error
Ho is rejected by shouldn't be
association
when categorical variables are related
linear regression
the process of finding a best fit line for a scatter plot and measurement of how well the line fits the scatter plot
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