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ASS 2 > Finals > Flashcards

Finals Flashcards

1
Q

The practice or science of collecting and
analyzing numerical data in large quantities,
especially for the purpose of inferring
proportions in a whole from those in a
representative sample

A

Statistics

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2
Q

The study and manipulation of data,
including ways to gather, review, analyze,
and draw conclusions from data.

A

Statistics

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3
Q

a branch of mathematics dealing with the
collection, analysis, interpretation, and
presentation of masses of numerical data

A

Statistics

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4
Q

simply defined as the study and manipulation of data

A

Statistics

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5
Q

Statistics in Education

A

❖ Measurement and evaluation are essential
part of teaching learning process

❖ In this process we obtained scores and then
interpret these score in order to take
decisions

❖ Statistics enables us study these scores
objectively

❖ It makes the teaching learning process more
efficient

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6
Q

Roles of Statistics in Education

A
  1. It helps the teacher to provide the most
    exact type of description

✓ When we want to know the student, we
administer a test or observe the child

✓ Then from the result we describe about the
students performance or trait

✓ Statistics helps the teacher to give an
accurate description of the data.

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7
Q

Roles of Statistics in Education

A
  1. It makes the teacher definite and exact in
    procedure and thinking.

✓ Sometimes due to lack of technical
knowledge, the teachers become vague in
describing students’ performance

✓ But Statistics enable him/her to describe the
performance by using some language and
symbols which makes interpretation definite
and exact.

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8
Q

Roles of Statistics in Education

A
  1. It enables the teacher to summarize the
    results in a meaningful and convenient form.

✓ Statistics give order to data

✓ It helps the teacher to make the data precise
and meaningful and to express it in
understandable and interpretable manner

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9
Q

Roles of Statistics in Education

A
  1. It enables the teacher to draw general
    conclusions.

✓ Statistics help to draw conclusions as well as
extracting conclusions.

✓ Statistical steps help to say about how much
faith should be placed in any conclusion and
about how far we may extend our
generalization

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10
Q

Roles of Statistics in Education

A
  1. It helps the teacher to predict the future
    performance of the students.

✓ Statistics enables the teacher to predict how
much of a thing will happen under
conditions we know and have measured.

✓ For example the teacher can predict the
probable score of the student in the finale
examination from his entrance test score

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11
Q

Roles of Statistics in Education

A
  1. Statistics enables the teacher to analyze some
    of the casual factors underlying complex and
    otherwise be-wildering(confusing) events;

✓ It is common factor that the behavioral
outcome is a resultant of numerous casual
factors.

✓ The reason why a particular student
performs poor in a particular subject are
varied and many

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12
Q

Roles of Statistics in Education

A
  1. Statistics enables the teacher to analyze some
    of the casual factors underlying complex and
    otherwise be-wildering(confusing) events;

✓ So with the appropriate statistical methods,
we can keep the extraneous variables
constant and can observe the cause of failure
of the pupil in a particular subject.

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13
Q

Values that the variables can assume

A

Data

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14
Q

Characteristics that is observable or
measurable in every unit of universe

A

Variable

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15
Q

The set of all possible values of a
variable

A

Population

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16
Q

A subgroup of a population

A

Sample

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17
Q

❖ Words or codes that represent a class
or category

❖ Express as a categorical attribute
(gender, religion, marital status,
highest educational attainment)

A

Qualitative variables

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18
Q

Number that represent an amount or
a count.

A

Quantitative variables

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19
Q

❖ Numerical data, sizes are meaningful
and answer questions as “how many”
or “how much”

❖ Example are height, weight,
household size, number of registered
cars

A

Quantitative variables

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20
Q

Data that can be counted (number of
days, number of siblings, usual
number of text messages sent in day)

A

Discrete variables

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21
Q

It can assume all values between any
two specific values like 0.5, 1.2 etc
and data can be measured (weight,
height, body temperature

A

Continuous variables

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22
Q

Data created by assigning observations into
various independent categories and then
counting the frequency of occurrence within
each of the categories.

A

Nominal

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23
Q

This is the most primitive level of measurement.

A

Nominal

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24
Q

It is used when we want to distinguish one
object from another for identification
purposes.

A

Nominal

25
Q

In this level, we can say that one object is
different from another, but the amount of
difference between them cannot be
determined

We cannot tell that one is better or worse
than the other.

❖ Gender, Nationality, and civil status are
nominal scale.

A

Nominal

26
Q

A scale in which scores indicate only relative amounts or rank order

In this level, data are arranged in some specified order or rank

A

Ordinal

27
Q

When objects are measured in this level, we can say that one is better or greater than the other. But we cannot tell how much more or how much less of the characteristics one object has than the other.

The ranking of contestants in a beauty contest, of
siblings in the family, or of honor students in the
class are of ordinal scale.

A

Ordinal

28
Q

A scale in which equal differences in scores represent
equal differences in amount of the property
measured, but with an arbitrary zero point.

A

Interval

29
Q

If the data are measured in this level, we can
say not only one object is greater or less than another,
but we can also specify the amount of difference.

A

Interval

30
Q

Interval

A

The scores in an examination are of the interval scale
of measurement.

❖ To illustrate, suppose Maria got 50 in Math
examination while Martha got 40. We can say that
Maria got higher than Martha by 10 points.

31
Q

All the properties of an interval scale with the
additional property of zero indicating a total
absence being measured

A

Ratio Scale

32
Q

It is like the
interval level. The only difference is that this
level always starts from an absolute or true zero
point.

A

Ratio Scale

33
Q

If the data are measured in this level, we can say
that one object is so many times as large or small
as the other.

A

Ratio Scale

34
Q

Example of Ratio Scale

A

For example, suppose Mrs. Reyes weighs 50 kg, while
her daughter weighs 25 kg. We can say that Mrs.
Reyes is twice as heavy as her daughter . Thus, weight
is an example of data measured in the ratio scale.

35
Q

A single value that attempts to describe a set of data by identifying the central position within that set of data

A

Measures of central tendency

36
Q

measures of central tendency are sometimes called

A

measures of central location

37
Q

Measures of central tendency

A

They are also classed as summary statistics.
The mean (often called the average) is most
likely the measure of central tendency that you
are most familiar with, but there are others,
such as the median and the mode

38
Q

most commonly used measure
of central position

A

Mean

39
Q

It is the sum of measures divided by the
number of measures in a variable.

A

Mean

40
Q

It is also used to describe a set of data
where measures cluster or concentrate at a
point

A

Mean

41
Q

Occasionally, we want to find the mean of a set
of values wherein each value or measurement
has a different weight or degree of importance.

A

Weighted mean

42
Q

the middle entry or term in a
set of data arranged in either increasing or
decreasing order.

A

Median

43
Q

It is a positional measure. Thus,
the values of the individual measures in as
set of data do not affect. It is affected by the
number of measures and not by the size of
the extreme values.

A

Median

44
Q

❖ It is the measure or value which occurs most
frequently in a set of data.

❖ It is the value with the greatest frequency.

A

Mode

45
Q

a continuous probability distribution that is
symmetrical on both sides of the mean, so the
right side of the center is a mirror image of
the left side.

A

Norman Distribution Curve

46
Q

The normal distribution is often called the
______ because the graph of its probability
density looks like a ___.

A

bell curve

47
Q

Also called spread or dispersion refers to
how spread out a set of data is.

A

Variability

48
Q

It gives you a way to describe how
much data sets vary and allows you to use
statistics to compare your data to other
sets of data.

A

Variability

49
Q

The four main ways to
describe variability in a data set

A

range
interquartile range
variance
standard deviation

50
Q

The difference between the highest and
lowest value in a set.

A

Range

51
Q

Quartiles segment any distribution that’s
ordered from low to high into four equal
parts.

A

Interquartile range

52
Q

A measure of dispersion, meaning it is a
measure of how far a set of numbers is
spread out from their average value.

A

Variance

53
Q

A measure of the amount of variation or
dispersion of a set of values.

A

Standard deviation

54
Q

It is the square root of the variance

A
55
Q

determined by
the number of peaks it contains. Most
distributions have only one peak but it is
possible that you encounter distributions
with two or more peaks.

A

Modality

56
Q

It is a measure of the symmetry of
a distribution. The highest point of a
distribution is its mode. The mode marks
the response value on the x-axis that occurs
with the highest probability.

A

Skewness

57
Q

A distribution
is skewed if the tail on one side of the mode
is fatter or longer than on the other: it is
______

A

asymmetrical

58
Q

Statistical measure that defines how heavily
the tails of a distribution differ from the tails
of a normal distribution.

A

Kurtosis

59
Q

This identifies whether the tails of a
given distribution contain extreme values

A

Kurtosis

ASS 2 (3 decks)

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