PRELIM LEC 2-4: Flashcards by Sunwoo's Berry

✔ 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

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TYPES OF DATA
o According to Source:
- interview, registration, experiment, questionnaire, etc

PRIMARY OR SECONDARY?

PRIMARY DATA

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TYPES OF DATA
o According to Source:
- book, journal, newspaper, thesis, dissertation, etc.

PRIMARY OR SECONDARY?

SECONDARY DATA

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Properties of the Mean

UNIQUENESS
SIMPLICITY
AFFECTED BY EXTREME VALUES

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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

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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

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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

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TYPE OF MODE:
- no data value occurs more than once

A. UNIMODAL
B. BIMODAL
C. MULTIMODAL
D. NO MODE

NO MODE

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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

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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

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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

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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

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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

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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

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POSITIVE KURTOSIS
- high/fat tails

A. Leptokurtic
B. Mesokurtic (Normal)
C. Platykurtic

A. Leptokurtic

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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