Math Terms Quiz chapter 4ab and 5

Biostatistics

is the application of statistical

principles to questions and problems in

medicine, public health, and biology

The discipline of biostatistics

provides

tools and techniques

for collecting data and

then summarizing,

analyzing, and

interpreting it.

statistics

It is 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.

plural sense: Statisticss

numerical facts, e.g. NBApoints

per game, pesodollar

exchange rate

singular sense: Statistics

scientific discipline

consisting of theory

and methods for

processing numerical

information that one

can use when making

decisions in the face

of uncertainty.

“ratio status”

The term statistics came from the Latin phrase______which means study of practical politics or the statesman’s art.

statistik

In the middle of 18th century, the term_____(a term due

to Achenwall) was used, a German term defined as “the

political science of several countries”.

correct, valid, reliable information

Correct statistical process

leads to?

1. published data

2. survey result

3. research output

What are the 3 examples of information on statistics?

• news and information

• making decisions

Everyday we use_________sources to

gather facts that we need in_____

Agriculture

comparing

the effects of five kinds of

fertilizers on the yield of a

particular variety of corn.

Economics

determining

the income distribution of

Filipino families.

Health

comparing the

effectiveness of two diet

programs.

Physical Science

prediction

of daily temperatures.

Education

evaluation of student performance

AIMS OF STATISTICS

uncover structure

in data to explain variation

Descriptive Statistics

methods concerned

with collecting,

describing, and

analyzing a set of data

without drawing

conclusions

(inferences) about

a large group

Inferential Statistics

methods concerned

with the analysis of a

subset of data

leading to

predictions or

inferences

(conclusions)

about the entire set

of data

universe

is the collection of things or

observational units under consideration

variable

is a characteristic observed or

measured on every unit of the universe

population

is the set of all possible

values of the variable.

sample

is a subset of the population

parameter

descriptive measure

of the population

sampling

process of collecting

a sample from a

population

statistic

descriptive measure

of the sample

inferential statistics

make generalizations

about parameters

using statistics

Variables

any characteristic that

can vary in measure

example:

age

sex

blood type

data value

a number or text response

obtained upon measurement

example:

54 years

female

Type A

Variables

are the column headings – “subject”, “age”,

“sex”, “bloodtype”

Data values

are the

table entries – “54”,

“female”, “A”, etc.

1. Qualitative

2. Quantitative

   -discrete

   -continuous

what are the 2 types of variables?

QUALITATIVE

categorical responses, non-numerical

characteristics or label

QUANTITATIVE

numerical responses, measurements,

or quantities

discrete

assumes a finite number

of values

continuous

assumes an infinite number of values

associated with values within an interval

Nominal

consists of finite set of possible

values in terms labels or categories

which have no particular order.

Ordinal

consists of finite set of possible values

in terms labels or categories which do

have a particular order.

Interval

set of data wherein differences

between measurements can be

described but NO true zero

Ratio

set of data wherein differences

between measurements can be

described and true zero EXISTS.

Independent Variables

It is the variable that the experimenter

changes or controls and is assumed to have

a direct effect on the dependent variable.

Dependent Variables

It is the variable being tested and measured

in an experiment, and is 'dependent' on the

independent variable.

1. Independent

   -fertilizer

2. dependent

   -plant growth

   -no. of leaves

   -no. of fruits

   -size

What is an example of independent and dependent variables?

sampling technique

is the manner by which

the samples are drawn from the population

sampling

process of collecting

a sample from a

population

inferential statistics

make generalizations

about parameters

using statistics

Reduced Cost

A sample often

provides useful and

reliable information

at a much lower cost

than a census

Speed or Timeliness

A sample usually

provides more timely

information because

fewer data are to be

collected and

processed.

Efficiency and Accuracy

A sample often

provides information

as accurate, or more

accurate, than a

census, because data

errors typically can

be controlled

better in smaller

tasks.

Greater Scope

Sampling has a

greater scope

regarding the variety

of information by

virtue of its flexibility

and adaptability

Convenience

The use of a sample

provides greater

convenience to the

researcher as it

reduces the amount

of work to process

data.

Necessity

Sampling is an

essential tool in

statistics and

research. Moreover,

it makes reliable

inferences about

population

sampling frame

The use of a complete listing of the

elements of the universe called the_____is required.

SIMPLE RANDOM SAMPLING

It is the most basic method of drawing a probability

sample which assigns equal probabilities of selection

to each possible sample.

1. Lottery

2. Use of table of random numbers

3. Use of electronic generated random

numbers

The samples are obtained through

strata

The universe is divided into L mutually

exclusive sub-universes called

1. Are there different groups within the

population?

2. Are these differences important to

the investigation?

When thinking of using stratification, the

following questions must be asked:

STRATIFIED RANDOM SAMPLING

is done when the

population is divided into several subgroups

with common characteristics.

Examples:

The population may be divided into…

o urban and rural locations

o year level of learners;

o workers in a hospital categorized by

occupations

STRATIFIED RANDOM SAMPLING

Advantages

1. It gives a better cross-section of the population.

2. It simplifies the administration of the survey/data

gathering.

3. The nature of the population dictates some inherent

stratification.

4. It allows one to draw inferences for various subdivisions

of the population.

5. Generally, it increases the precision of the estimates.

SYSTEMATICS RANDOM SAMPLING

-It adopts a skipping pattern in the selection of

sample units.

-It gives a better cross-section if the listing is

linear in trend but has high risk of bias if there is

periodicity in the listing of units in the sampling

frame.

-It allows the simultaneous listing and selection

of samples in one operation.

CLUSTER RANDOM SAMPLING

o It considers a universe divided into N mutually

exclusive sub-groups called clusters.

o A random sample of n clusters is selected, and their

elements are completely enumerated.

o It has simpler frame requirements.

o It is administratively convenient to implement.

CLUSTER RANDOM SAMPLING

Advantages:

1. Less complex and relatively easier

2. Increase efficiency and ease of data collection

CLUSTER RANDOM SAMPLING

Disadvantages:

1. May not fully represent all individual elements.

2. The method is prone to bias.

multistage random sampling

a subset is obtained from a population using smaller and

smaller groups (units) at each stage. The data used is commonly collected from a

large disperse population in national surveys

Simple two-stage sampling (First Stage – determine a primary sampling unit)

The units are grouped into N sub-groups,

called primary sampling units (psu’s) and a

simple random sample of n psu’s are selected

Simple two-stage sampling (Second Stage – take a secondary sampling unit)

In the second stage, from each of the n psu’s selected

with Mi elements, simple random sample of mi units,

called secondary sampling units ssu’s, will be obtained

MULTISTAGE RANDOM SAMPLING

Advantages:

-A sampling frame of target population is not needed

-Other sampling methods can be used between methods

MULTISTAGE RANDOM SAMPLING

Disadvantages:

-A large sample size is needed to achieve the same statistical

inference properties.

-May have unrepresentative samples since large sections

of populations may not be selected for sampling.

NONPROBABILITY SAMPLING

-Samples are obtained haphazardly, selected

purposively, or taken as volunteers.

-The probabilities of selection are unknown.

PURPOSIVE SAMPLING

The participants are obtained based on the

characteristics that needed for the sample.

Here, the units are selected “on purpose”.

CONVENIENCE SAMPLING

This involves using

respondents who

are “convenient” to

the researcher.

QUOTA SAMPLING

The participants are selected

based on specific

characteristics, ensuring

they represent certain

attributes in proportion

to their prevalence in

the population

SNOWBALL SAMPLING

Additional sample units

are identified by asking

previously picked sample

units for people they

know who can be added

to the sample.