OVERVIEW OF DESCRIPTIVE STATISTIC
INTRODUCING OF STATISTIC
is a science of conducting studies
statistic is for
method that organize, summarize, and presenting data
to be able to read and understand the various statistical studies
statistical studies are basic to research
on convenient ways and informative ways
as using graphical technique
may consist of
collecting data
organizing data collection
summarize the data
presenting it
in chart, graph or table
TYPE OF STATISTICAL DATA
Descriptive Statistics
Inferential Statistics
its involve, summarize data, organize and display the data collection
but not infer the properties of the population from which sample was drawn
its knows as a ways to conclusion data
based the characteristic of the population
make inference from the sample to populations
describe only to observed
performing estimation and hypothesis test on the data
make prediction
Type of Variable
Numerical/Quantitative
Categorized/Qualitative
Discrete
a data in numerical form
a data that can be categorize to some characteristic
age, height or body temperature, time
gender, religion. race, colours, shape, texture
Continuos
Deals with number that can be measured
deals with description that data can be observed but not measured
a value that can be counted, such as 0, 1, 2, 3
such as number of children at the playground, number of sibling in the house.
an infinite value of number between specific value. usually obtained by measuring.
such as weight, time, height, etc
LEVELS OF MEASUREMENT
Determine which measurement that are meaningful for that particular statistical measurement.
Ordinal
Interval
Nominal
Ratio
categorical data such as gender, religious
can be arranged in a ranking order. an A,B,C rating scale
a data that involves score or mathematics or temperature that the number are in between
a data about height, weight, time. this type is data that can be measured
a zero has no value
a zero has a value
POPULATION VS SAMPLE
CENSUS
REPRESENTATIVE SAMPLE
SAMPLE SURVEY
RANDOM SAMPLE
collection of information from element of population. Sample
banci
sample form a survey
collection of data that are from portion of the population
represent the characteristic of the populations
every element in populations has being selected randomly
common terminologies
size 'N' : number of populations
size 'n' : number of samples
a study done on the entire populations
a research
research/survey
pilot study
element
sample survey
data
a study that done with statistical method in order to understand certain problem
a study done before the actual study
respondent/ on which data is taken
a data that involves subgroup of a populations being chosen
variable
characteristic of the population under study
values that can be obtain from measurement
PARAMETERS AND STATISTIC
SAMPLING TECHNIQUES
ADVANTAGE
IMPORTANCE
PROCESS
SIMPLE RANDOM SAMPLING
SYSTEMATIC SAMPLING
STRATIFIED SAMPLING
CLUSTER SAMPLING
is a numerical description of a population characteristic
Parameter - population
Statistic - sample
saves money, times, manpower. and its suitable for a large population research and detailed can be carried out
1st, define the population
2nd, identify the sampling frame, listing all units in the population. define from which the sample will be selected
3rd. choose the suitable sampling technique according which design of your research and the number of populations.
4th, determine the exact/appropriate sample size
5th, execute the sampling process
HUMOGENOUS in nature
all the participant has the same chance of being selected as a sample
all population have the characteristic that we are looking for
order is by, lottery, random number generated from computer
strength
weakness
easily applied and the result can be projected on population
expensive and it is difficult to obtain sampling frame
populations has to HUMIGENOUS and sampling frame has to be random, but not necessarily complete.
make sure the list is random and numbered all the operator.
for calculation, example, fir every 20 operator, only one will be selected
divided to mutually exclusive strata and then randomly sample from each strata
strength
weakness
easier to implement to simple random sampling and less expensive with a simple design
can decrease representativeness if certain pattern exist in sampling frame and with this biases are possible
sample are selected according to the required sizes.
best use when
within strata are HOMOGENEOUS ( similar characteristic)
between strata are HETEREGENOUS as possible
Strength
Weakness
includes all importance subpopulations and researchers can control sample size in strata
its expensive and more time consuming than other sampling technique
this method is use when the population is scattered over large area (district or villages)
best use
element between cluster is HOMOGENOUS
within cluster HETEROGENOUS
suitable
even when the sampling frame are incomplete or unavailable
all member in selected group are used
strength
weakness
cost effective and work is reduced , economically more efficient than simple random
imprecise and difficult to compute and to interpret result
sampling bias could occur if the sample does not suitable with the research type
sample frame error occur when the wrong sub-populations is used
systematic errors occur when the result form the sample differ significantly from the result of the populations