Module 2 Flashcards
Statistics
The science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions
Descriptive Statistics
Methods of organizing, summarizing, and presenting data in an informative way
Population
The entire set of individuals or objects of interest or the measurements obtained from all individuals or objects of interest
Sample
A portion, a part, of the population of interest
Inferential Statistics
The methods used to estimate a property of a population based on a sample
Nominal Level of Measurement
Data recorded at this level of measurement are represented as labels or names. They have no order. They can only be classified and counted.
Ordinal Level of Measurement
Data recorded at this level of measurement is based on a relative ranking or rating of items based on a defined attribute or qualitative variable. Variables based on this level of measurement are only ranked and counted.
Interval Level of Measurement
For data recorded at this level of measurement, the interval or the distance between values is meaningful. This level of measurement is based on scale with a known unit of measurement.
Ratio Level of Measurement
Data recorded at this level of measurement are based on a scale with a known unit of measurement and a meaningful interpretation of zero on the scale
Nominal
Data is only classified (jersey numbers, make of a car)
Ordinal
Data is ranked (ranking in class, team standings in conference)
Interval
Meaningful difference between values (temperature, dress size)
Ratio
Meaningful 0 point and ratio between values (profit, fuel efficiency, distance to class)
Parameter
A characteristic of a population
Statistic
A characteristic of a sample
Median
The midpoint of the values after they have been ordered from the minimum to the maximum values
Mode
The value of the observation that appears most frequently
Range equation
Maximum value – Minimum Value
Variance
The arithmetic mean of the squared deviations from the mean
Chebyshev’s Theorem
For any set of observations (sample or population), the proportion of the values that lie within k standard deviation of the mean is at least 1 -1/k^2, where k is any value greater than 1
Empirical Rule
For a symmetrical, bell-shaped frequency distribution, approximately 68% of the observations will lie within plus and minus one standard deviation of the mean, about 95% of the observations will lie within plus and minus two of the standard deviation of the mean, and practically all (99.7%) will lie within plus and minus three of the standard deviation of the mean
Dot Plot
This summarizes the distribution of one variable by stacking dots at points on a number line that shows the values of the variable. This shows all values.
Quartiles
Values of an ordered (minimum to maximum) data set that divide the data into four intervals
Deciles
Values of an ordered (minimum to maximum) data set that the data is divided into 10 equal parts
Percentiles
Values of an ordered (minimum to maximum) data set that divide the data into 100 intervals
Box Plot
A graphic display that shows the general shape of a variable’s distribution. It is based on five descriptive statistics: the maximum and minimum values, the first and third quartiles, and the mean.
Interquartile Range
The absolute numerical difference between the first and third quartiles. Fifty percent of a distribution’s values occur in this range.
Outlier
A data point that is unusually far from the others. An accepted rule is to classify an observation as an outlier if it is 1.5 times the interquartile range above the third quartile or below the first quartile
Scatter Diagram
Graphical technique used to show the relationship between two variables measured with interval or ratio scales
Contingency Table
A table used to classify sample observations according to two identifiable characteristics
A probability must be between 0 and 1, but can sometimes be 100?
False
An outcome is the result of an experiment such as rolling the dice and getting a 7?
True
Classical Probability is calculated by …. Probability of an Event = Count of Favorable Outcomes / The Count of all Outcomes?
True
Mutually Exclusive events mean that the occurrence of one event means that none of the other events can occur at the same time.
True
The Law of Large Numbers means as the experiment increases in sample size the empirical probability moves closer to 100?
False
P (A or B) = P (A) + P(B) - P (A / B) … Like in a Venn Diagram?
False
The Complement of A = P (1 - A)?
True
Statistical Independence means the occurrence of one event has no impact on the occurrence of the next event?
True
A statistical combination means order matters in the fundamental rules of counting.
False
A Probability Distribution is a listing of all the outcomes of an experiment and the probability associated with each outcome?
True
