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BIOSTAT > PRELIM LEC 2 (2): DESCRIPTIVE STATISTICS > Flashcards

PRELIM LEC 2 (2): DESCRIPTIVE STATISTICS Flashcards

1
Q

Deals with the collection and presentation of data and collection of summarizing values to describe its group characteristics

A

DESCRIPTIVE STATISTICS

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2
Q

✔ central thread of any activity
✔ Understanding the nature of data is most fundamental for proper and effective use of statistical skills

A

DATA

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3
Q

2 TYPES OF DATA

A

ACCORDING TO SOURCE
ACCORDING TO FUNCTIONAL RELATIONSHIP

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4
Q

2 TYPES OF DATA ACCORDING TO SOURCE

A

Primary Data
Secondary Data

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5
Q

interview, registration, experiment, questionnaire, etc

A

PRIMARY DATA

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6
Q

book, journal, newpaper, thesis, dissertation, etc.

A

SECONDARY DATA

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7
Q

2 TYPES OF DATA ACCORDING TO FUNCTIONAL RELATIONSHIP

A

Independent Data
Dependent Data

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8
Q

refers to any controlling data

A

Independent Data

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9
Q

refers to any data that is affected by controlling data

A

Dependent Data

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10
Q

METHODS OF COLLECTING DATA

A

o Objective Methods
o Subjective Methods
o Use of Existing Records

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11
Q

METHODS IN PRESENTING DATA

A

o Textual
o Tabular
o Graphical

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12
Q

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

Measure of Location

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13
Q

MIN is the smallest value in the data set while MAX is the largest value in the data set

A

Minimum and Maximum

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14
Q

it is the average of the data

A

Mean

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15
Q

Properties of the Mean

A

o Uniqueness
o Simplicity
o Affected by extreme values

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16
Q

Divides the observations into two equal parts
o If n is odd, the median is the middle number.
o If n is even, the median is the average of the 2 middle number

A

Median

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17
Q

Value that occurs most often

A

Mode

18
Q

A data set that has only one value that occurs with the greatest frequency

A

Unimodal

19
Q

If a data set has two values that occur with the same greatest frequency, both values are mode

A

Bimodal

20
Q

If a data set has more than two values that occur with the same greatest frequency, each value is used as the mode

A

Multimodal

21
Q

When no data value occurs more than once

A

No mode

22
Q

values that divide the distribution into 100 equal parts. P10 or tenth percentile locates the point that is greater than 10 percent of the items in the distribution

A

Percentiles

23
Q

values that divide a distribution into 10 equal parts. The 1st decile is the 10th percentile; the 2nd decile is the 20th percentile…

A

Deciles

24
Q

Divide an array into four equal parts, each part having 25% of the distribution of the data values. The 1st quartile is the 25th percentile; the 2nd quartile is the 50th percentile, also the median and the 3rd quartile is the 75th percentile.

A

Quartiles

25
Q

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 Dispersion

26
Q

2 TYPES OF MEASURES OF DISPERSION

A

ABSOLUTE MEASURES OF DISPERSION
RELATIVE MEASURE OF DISPERSION

27
Q

difference between the maximum and minimum value in a data set

A

Range

28
Q
  • distance or range between the 25th percentile and the 75th percentile
A

Interquartile range

29
Q

it measure dispersion to the scatter of the values about there mean

A

Variance

30
Q

is the square root of variance
● ±1SD = 68.3% ● ±2 SD = 95.4% ● ±3SD = 99.7%

A

Standard Deviation

31
Q

is a measure use to compare the dispersion in two sets of data which is independent of the unit of the measurement

A

Coefficient of Variation

32
Q

A distribution is said to be symmetric about the mean, if the distribution to the left of mean is the “mirror image” of the distribution to the right of the mean

A

symmetry

33
Q

measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point

● Positively Skew
● Negatively Skew
● Symmetrical Distribution/Equal

A

Skewness

34
Q

measure of whether the data are peaked or flat relative to a normal distribution.
● Leptokurtic
● Mesokurtic (Normal)
● Platykurtic

A

Kurtosis

35
Q

a branch of mathematics which deals with the study of possible outcomes of an event or set of events together with the outcomes’ relative likelihood and distributions

A

Probability

36
Q

2 TYPES OF PROBABILITY

A

OBJECTIVE PROBABILITY
SUBJECTIVE PROBABILITY

37
Q

calculated by the process of abstract reasoning

A

Classical probability

38
Q

depends on the repeatability of some process and the ability to count

A

relative frequency probability

39
Q

based upon an educated guess

A

Subjective probability

40
Q

3 PROPERTIES OF PROBABILITY THEORY

A
  1. Given some process ( or experiment) with n mutually exclusive outcomes ( called events), E1, E2, . . . , En, the probability of any event Ei is assigned a nonnegative numbers. That is, P(Ei) ≥ 0
  2. The sum of the probabilities of the mutually exclusive outcomes is equal to 1. P(E1) + P(E2) + … + P(En) = 1
    ✔ This is the property of EXHAUSTIVENESS
  3. Consider any two mutually exclusive events, Ei and Ej. The probability of the occurence of either Ei or Ej is equal to the sum of their individual probabilities. P(Ei + Ej) = P(Ei) + P(Ej)
41
Q

Calculating the probability of an event

A
  1. Conditional Probability
    ✔ The condtional probability of A given B, denoted P(A\B), is the probability that event A has occurred in a trial of a random experiment for which it is known that event B has occurred.
  2. Joint Probability
    ✔ Calculates the likelihood of two events occurring together and at the same point in time
  3. The Multiplication Rule
  4. The Addition Rule
  5. Independent Events
    ✔ When P(A\B) = P(A) * P(B) holds, which in turn is true if and only if P(B\A) = P(B)
  6. Complementary Events
  7. Marginal Events

BIOSTAT (4 decks)

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