STATS

STATS

1. Chapter TWO

   • Frequency Distribution
     • a table that shows classes or intervals of data entries with a count of the number of entries in each class.
       • The Frequency of a class is the number of data entries in the class.
   • Cumulative frequency- the sum of the frequencies of that class and all previous classes.
   • types of charts and Graphs
     • Frequency histogram
     • frequency polygon
     • relative frequency histogram
     • ogive
     • stem and leaf plot
     • dot pilot
     • Pie Chart
     • Pareto
     • Scatter Plot
     • Time series Chart
   • Mean: Sum of the data entries divided by the number of entries
   • Meadian: the value that lies in the middle of the data when the data set is ordered
   • Mode: the data entry with the greater frequency

2. Chapter FOUR

   • vocabulary
     • -random variable: represents a numerical value associated with each outcome of a probability experiment.  -continuous: if a ransoms variable has an uncontrollable number of possible outcomes, represented by an interval on the number line.  -discrete probability distribution: lists each possible value the random variable can assume, together with its probability. -mean: represents the “theoretical average” of a probability experiment and sometimes is not a possible outcome. -expected value: is equal to the mean of the random variable.  -(binomial experiment: should be on a different little square) -binomial probability distribution: found by listing the possible values of x with the corresponding probabilities.  -(geometric distribution: ) -(poisson distribution)
   • The word random indicates that x is determined by chance.
   • Different random variables:
     • Discrete: x can have only whole number value: 0,1,2,3….
     • Continuous: x can have any value between 0 and 24.
   • it’s important to distinguish between discrete and continuous random variables because different statistical techniques are used to analyze each.
   • -Binomial Experiments satisfy the following conditions:
     • 1. The experiment is repeated for a fixed number of trials, where each trial is independent of the other trials.
     • 2. There are only two possible outcomes of interest for each trial. There can either be a success of a failure.
     • 3. The probability of a success P(S) is the same for each trial.
     • 4. The random variable x counts the number of successful trials.
   • -Geometric distribution is a discrete probability distribution of a random variable x that satisfies the following conditions:
     • 1. A trial is repeated until a success occurs
     • 2. The repeated trials are independent of each other.
     • 3. The probability of success p is constant for each trial.
     • 4. The random variable x represents the number of the trial in which the first success occurs.
   • -Poisson distribution is a discrete probability distribution of a random variable x that satisfies the following conditions:
     • 1. The experiment consists of counting the number of times x an event occurs in a given interval. The interval can be an interval of time, are, or volume.
     • 2. The probability of the event occurring is the same for each interval.
     • 3. The number of occurrences in one internal is independent of the number of occurrences in other intervals.

3. Chapter ONE

   • Vocabulary
     •  vocabulary:  Data- information coming from observations, counts, measurements, or responses Statistics- the scince of collecting, organizing, analyzing, and interpreting data in order to make decisionsPopulation- the collection of all outcomes, responses, measurements, or counts that are of interest Sample- a subset, or part, of a population Parameter- numerical description of a population characteristic Statistics- a numerical description of a sample characteristic Qualitive- attributes, labels, or nonnumerical entries Quantative-  numerical measurements or counts
   • Statical Study
     • Do and observational study
     • Perform and experiment
     • use a simulation
     • usa a surgery
   • Simple Random Sample
     • Stratified
     • Cluster
     • Systematic
     • Convenience

4. Chapter THREE

   • Vocab
     • -Probability experiment: an action through which specific results are obtained -Outcome: the result of a single trial in a probability experiment. -sample space: the set of all possible outcomes of a probability experiment  -event: a subset of the sample space. -tree diagram: gives a visual display of the outcomes of a probability experiment by using branches that originate from a starting point.  -simple event: event that consists of a single outcome  -classical probability: used when each outcome in a sample space is equally likely to occur.  -empirical probability: based on observations obtained from probability experiments.  -subjective probability: results from intuition, educated guesses, and estimates.  -complement of event E: the set of all outcomes in a sample space that are no included in event E.  -conditional probability: the probability of an event occurring, given that another event has already occurred. -independent: if the occurrence of on of two evens doesn’t affect the probability of the occurrence of the other event.  -dependent: if an event is not independent -mutually exclusive: if A and B cannot occur at the same time.  -permutation: an ordered arrangement of objects
   • Simple examples of the use of:
     • -Probabilty Experiment: Roll a six-sided die.
     • -Sample Space: (1,2,3,4,5,6)
     • -Event: Roll an even number—> (2,4,6)
     • -Outcome: Roll a 2—-> (2)
   • -Events are often represented by uppercase letters, such as A,B,and C.
   • -The Fundamental Counting Principle: If one event can occur in m ways and a second event can occur in n ways, the number of ways the two events can occur in sequence in mXn.
   • -The umber of ways that events can occur in sequence is found by multiplying the number of ways one event can occur by the number of ways the other events can occur.
   • -Probabilties can be written as Fractions
   • -Independent: P(A/B) = P(B) or if P(A/B) = P(A)
   • -Multiplication Rule for the probability of A and B: the probability that two events A and B will occur in sequence is: P(A and B) = P(A) x P(B/A)
   • -How to use the Multiplication Rule:
     • 1. Find the probability that the first event occurs
     • 2. Find the probability that the second event occurs given that the first event has occurred
     • 3. Multiply these two probabilities.
     • -one can think of a combination of n objects chosen r at a time as a permutation of n objects in which the r selected objects are alike and the remaining n-r (not selected) objects are alike.
   • -to find the probability that one event or the other will occur, add the individual probabilities of each event and subtract the probability that they both occur.

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