PRELIM LEC 2-4: Flashcards
✔ gathered body of facts
✔ central thread of any activity
✔ Understanding the nature of data is most fundamental for
proper and effective use of statistical skills
DATA OR VARIABLE
DATA
TYPES OF DATA
o According to Source:
- interview, registration, experiment, questionnaire, etc
PRIMARY OR SECONDARY?
PRIMARY DATA
TYPES OF DATA
o According to Source:
- book, journal, newspaper, thesis, dissertation, etc.
PRIMARY OR SECONDARY?
SECONDARY DATA
Properties of the Mean
- UNIQUENESS
- SIMPLICITY
- AFFECTED BY EXTREME VALUES
TYPE OF MODE:
- - A data set that has ONLY ONE VALUE that occurs with the greatest frequency
A. UNIMODAL
B. BIMODAL
C. MULTIMODAL
D. NO MODE
UNIMODAL
TYPE OF MODE:
- TWO VALUES that occur with the same greatest frequency, both values
are mode
A. UNIMODAL
B. BIMODAL
C. MULTIMODAL
D. NO MODE
BIMODAL
TYPE OF MODE:
- MORE THAN TWO VALUES that occur with the same greatest frequency, each value is used as the mode
A. UNIMODAL
B. BIMODAL
C. MULTIMODAL
D. NO MODE
MULTIMODAL
TYPE OF MODE:
- no data value occurs more than once
A. UNIMODAL
B. BIMODAL
C. MULTIMODAL
D. NO MODE
NO MODE
summarizes a data set by giving a “typical value” within the range of the data values that describes its location relative to entire data set
A. MEASURES OF LOCATION
B. MEASURES OF DISPERSION
A. MEASURES OF LOCATION
o single value that is used to describe the SPREAD OF THE DISTRIBUTION
o A measure of central tendency alone does not uniquely describe a distribution
A. MEASURES OF LOCATION
B. MEASURES OF DISPERSION
B. MEASURES OF DISPERSION
Absolute Measures of Dispersion:
- distance or range between the 25th
percentile and the 75th percentile
A. RANGE
B. INTERQUARTILE RANGE
C. VARIANCE
D. STANDARD DEVIATION
B. INTERQUARTILE RANGE
Absolute Measures of Dispersion:
- it measure dispersion to the SCATTER OF VALUES about there mean
A. RANGE
B. INTERQUARTILE RANGE
C. VARIANCE
D. STANDARD DEVIATION
C. VARIANCE
Relative Measure of Dispersion
– is a measure use to COMPARE THE DISPERSION in two sets of data which is independent of the unit of the measurement
A. VARIANCE
B. KURTOSIS
C. COEFFICIENT OF VARIATION
D. STANDARD DEVIATION
C. COEFFICIENT OF VARIATION
Relative Measure of Dispersion
– measure of whether the data are peaked or flat relative to a normal distribution
A. VARIANCE
B. KURTOSIS
C. COEFFICIENT OF VARIATION
D. STANDARD DEVIATION
B. KURTOSIS
POSITIVE KURTOSIS
- high/fat tails
A. Leptokurtic
B. Mesokurtic (Normal)
C. Platykurtic
A. Leptokurtic
NEGATIVE KURTOSIS
- low/thin tails
A. Leptokurtic
B. Mesokurtic (Normal)
C. Platykurtic
C. Platykurtic
TYPES OF PROBABILITY:
- based upon an educated guess
SUBJECTIVE OR OBJECTIVE
SUBJECTIVE PROBABILITY
the probability that event A has occurred in a trial of a random experiment for which it is known
that event B has occurred.
CONDITIONAL OR JOINT PROBABILITY?
CONDITIONAL PROBABILITY
Calculates the LIKELIHOOD of two events occurring together and at the same point in time
CONDITIONAL OR JOINT PROBABILITY?
JOINT PROBABILITY
tail is more pronounced on the RIGHT side than it is on the left
A. POSITIVELY SKEW
B. NEGATIVELY SKEW
A. POSITIVELY SKEW
tail is more pronounced on the LEFT side than it is on the right
A. POSITIVELY SKEW
B. NEGATIVELY SKEW
B. NEGATIVELY SKEW
Types of Probability Distribution:
Random variables can take only LIMITED number of values
Discrete Probability Distribution
OR
Continuous Probability Distribution
Discrete Probability Distribution
Types of Probability Distribution:
Random variables can take ANY VALUE
Ex. Height of students in the class
Discrete Probability Distribution
OR
Continuous Probability Distribution
Continuous Probability Distribution
✔ There are certain phenomena in nature which can be identified as Bernoulli’s processes
✔ expresses the probability of ONE SET of ALTERNATIVES– success (p) and failure (q)
BINOMIAL DISTRIBUTION
OR
POISSON DISTRIBUTION
BINOMIAL DISTRIBUTION
✔ When there is a LARGE NUMBER OF TRIALS, but a small probability
of success, binomial calculation becomes impractical
✔ If ƛ = mean no. of occurence of an event per unit interval of time/space, then probability that it will occur exactly ‘x’ times is given by
✔ P(x) = ƛ x e -ƛ where e is napier constant and e = 2.7182 x!
BINOMIAL DISTRIBUTION
OR
POISSON DISTRIBUTION
POISSON DISTRIBUTION
- shows the statistic values from all the possible samples of the same size from the population.
A. SAMPLE DISTRIBUTION
B. DISTRIBUTION OF A SAMPLE DATA
C. POPULATION DISTRIBUTION
SAMPLE DISTRIBUTION
gives the values of the variable for all the individuals in the population.
A. SAMPLE DISTRIBUTION
B. DISTRIBUTION OF A SAMPLE DATA
C. POPULATION DISTRIBUTION
C. POPULATION DISTRIBUTION
shows the values of the variable for all the individuals in the sample.
A. SAMPLE DISTRIBUTION
B. DISTRIBUTION OF A SAMPLE DATA
C. POPULATION DISTRIBUTION
B. DISTRIBUTION OF A SAMPLE DATA
statistics enable us to judge the probability that our inferences or
estimates are close to the truth
A. STATISTICAL INFERENCE
B. SAMPLING DISTRIBUTIONS
C. INTERVAL ESTIMATE
D. POINT ESTIMATTE
A. STATISTICAL INFERENCE
Statistic whose calculated value is used to estimate a population parameter
ESTIMATOR
A particular realization of an estimator
ESTIMATE
Types of Estimators:
▪ single number that can be regarded
as the MOST PLAUSIBLE VALUE
▪ SPECIFIC NUMERICAL VALUE estimate of a parameter.
▪ The BEST______ of the population mean is the SAMPLE MEAN
POINT ESTIMATE
OR
INTERVAL ESTIMATE
POINT ESTIMATE
Types of Estimators:
▪ a range of numbers, called a confidence interval indicating, can be regarded as likely containing the true value
▪ USED TO ESTIMATE THE PARAMETER
▪ This estimate MAY OR MAY NOT CONTAIN THE VALUE of the parameter
being estimated
POINT ESTIMATE
OR
INTERVAL ESTIMATE
INTERVAL ESTIMATE
Three properties of a good estimator:
- UNBIASED ESTIMATOR
- CONSISTENT ESTIMATOR
- RELATIVELY EFFICIENT ESTIMATOR
Methods of Point Estimates:
▪ Advantage: simplest approach for
constructing an estimator
▪ Disadvantage: usually are not the
“best” estimators possible
A. METHOD OF MOMENTS
B. MAXIMUM LIKELIHOOD
C. BAYESIAN
A. METHOD OF MOMENTS
Methods of Point Estimates:
Before an experiment is performed
the OUTCOME IS UNKNOWN. Probability allows us to predict unknown outcomes based on known parameters
▪ After an experiment is performed the
outcome is known. Now we talk
about the LIKELIHOOD that a parameter would generate the observed data
A. METHOD OF MOMENTS
B. MAXIMUM LIKELIHOOD
C. BAYESIAN
B. MAXIMUM LIKELIHOOD
Methods of Point Estimates:
The classic philosophy (frequentist) assumes parameters are fixed quantities that we want to estimate
as precisely as possible
▪ ________ perspective is different:
parameters are RANDOM VARIABLES
with probabilities assigned to particular values of parameters to reflect the degree of evidence for that value
A. METHOD OF MOMENTS
B. MAXIMUM LIKELIHOOD
C. BAYESIAN
C. BAYESIAN
Interval Estimates:
Is the probability that the interval
estimate will CONTAIN the parameter
CONFIDENCE LEVEL
OR
CONFIDENCE INTERVAL
CONFIDENCE LEVEL
Interval Estimates:
is a SPECIFIC INTERVAL ESTIMATE of a parameter determined by using data obtained from a sample
CONFIDENCE LEVEL
OR
CONFIDENCE INTERVAL
CONFIDENCE INTERVAL
