Exam 1 Material Flashcards
In words, express what the Union formula means using events A and B.
The probability that A or B occurs is the P that A occurs plus the P that B occurs minus the P that both A and B occurs (A intersection B).
Two events are mutually exclusive if:
they do not intersect. They have no outcomes in common.
Express when two events, A and B, are mutually exclusive.
P(A or B)= P(A)+P(B)-0
Two events are independent if:
the occurence or non-occurence of one does not change the P that the other will occur.
Express when the P of two events, A and B, are mutually independent.
P(A and B)= P(A)*P(B)
Express when the P of two events, A and B, are mutually dependent.
P(A and B)= P(A)*P(B/A)
Addition Law of Probability:
For any events A and B:
P(A U B)=P(A)+P (B)-P(A ∩ B)
Addition Law of Probability:
For A, B, mutually exclusive:
P(A U B)=P(A)+P(B)
Addition Law of Probability:
For A, B, independent:
P(A U B)=P(A)+P (B)-P(A)*P(B)
The probability that someone who is known to be overweight is hypertensive would be written as
P(Hypertensive/Overweight)=P(H/O).
If P(A/B)=P(A)
Then they are independent.
If P(A∩B)=P(A)*P(B)
Then they are independent.
When two events are mutually exclusive, they are ALWAYS
Dependent.
If P(A/B)=P(A), then what can be said about B?
B had no impact on A, and A and B are independent.
If P(A and B)=P(A)*P(B), then what can be said about events A and B?
They are independent.
In probability theory, a set of events is collectively exhaustive if:
it includes all possible outcomes.
Descriptive Statistics deals with _____ and contains a ____ and ____
the organizing and summary of data; parameter and statistic.
A parameter is
a number that describes some characteristic of a population.
A statistic
is a number that we can calculate purely from a sample.
Inferential Statistics deals with
Using sample statistics to draw inferences about population parameters
Sampling techniques include:
Simple random sampling, stratified random sampling, 1 in k systematic sampling, cluster sampling, and convenience sampling..
In simple random sampling,
Each member of the population has the same chance of being selected for the sample, and Every possible sample of the size n has the same chance of being selected to represent the population.
In stratified random sampling:
members of the population are divided into two or more homogeneous subsets, called strata, that share a similar characteristic such as age, gender, ethnicity, political preference.
In 1 in k systematic sampling:
A starting number is randomly selected from amongst the first k members, and then every “kth” member is selected from the starting number,
In cluster sampling:
divide the population into groups called clusters, then randomly select some of these clusters, and include each member of that cluster in the sample.
The cost per sample point is less for cluster sampling than for other sampling methods.
Convenience sampling:
simply uses results or data that are conveniently and readily available. This technique is biased.
A categorical variable:
places an individual into one of a group of categories. (Cannot be measured on a numerical scale.)
A quantitative variable
takes on numerical values on which arithmetic operations can be performed.
Categorical data includes:
Nominal, and Ordinal information.
Quantitative data includes:
Interval, and ratio information.
Nominal information
consists of names only or qualities with no implied criteria by which we can identify which data item is greater than or less than another. (No numerical computation is possible.)
Ordinal information
may be arranged in some order but actual differences between data values either cannot be determined, or are meaningless.
Interval information
have meaningful differences between data values. However, there is no Intrinsic ‘0’ or start point, and ratios of data values are not meaningful.
Ratio information
has Meaningful differences exist between data values. Includes an inherent ‘0’ or start point. ‘0’ reflects the absence of the measured characteristic, Ratios are meaningful.
