Please enable JavaScript.
Coggle requires JavaScript to display documents.
OVERVIEW OF DESCRIPTIVE STATISTIC - Coggle Diagram
OVERVIEW OF DESCRIPTIVE STATISTIC
INTRODUCING OF STATISTIC
is a science of conducting studies
method that organize, summarize, and presenting data
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
statistic is for
to be able to read and understand the various statistical studies
statistical studies are basic to research
TYPE OF STATISTICAL DATA
Descriptive Statistics
its involve, summarize data, organize and display the data collection
but not infer the properties of the population from which sample was drawn
describe only to observed
Inferential Statistics
its knows as a ways to conclusion data
based the characteristic of the population
make inference from the sample to populations
performing estimation and hypothesis test on the data
make prediction
Type of Variable
Numerical/Quantitative
Discrete
an infinite value of number between specific value. usually obtained by measuring.
such as weight, time, height, etc
a data in numerical form
age, height or body temperature, time
Deals with number that can be measured
Continuos
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.
Categorized/Qualitative
a data that can be categorize to some characteristic
gender, religion. race, colours, shape, texture
deals with description that data can be observed but not measured
LEVELS OF MEASUREMENT
Determine which measurement that are meaningful for that particular statistical measurement.
Ordinal
can be arranged in a ranking order. an A,B,C rating scale
Interval
a data that involves score or mathematics or temperature that the number are in between
a zero has a value
Nominal
categorical data such as gender, religious
Ratio
a data about height, weight, time. this type is data that can be measured
a zero has no value
POPULATION VS SAMPLE
CENSUS
collection of information from element of population. Sample
banci
a study done on the entire populations
REPRESENTATIVE SAMPLE
represent the characteristic of the populations
SAMPLE SURVEY
sample form a survey
collection of data that are from portion of the population
RANDOM SAMPLE
every element in populations has being selected randomly
common terminologies
size 'N' : number of populations
size 'n' : number of samples
a research
research/survey
a study that done with statistical method in order to understand certain problem
pilot study
a study done before the actual study
element
respondent/ on which data is taken
sample survey
a data that involves subgroup of a populations being chosen
data
values that can be obtain from measurement
variable
characteristic of the population under study
PARAMETERS AND STATISTIC
SAMPLING TECHNIQUES
ADVANTAGE
saves money, times, manpower. and its suitable for a large population research and detailed can be carried out
IMPORTANCE
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
PROCESS
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
SIMPLE RANDOM SAMPLING
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
easily applied and the result can be projected on population
weakness
expensive and it is difficult to obtain sampling frame
SYSTEMATIC SAMPLING
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
strength
easier to implement to simple random sampling and less expensive with a simple design
weakness
can decrease representativeness if certain pattern exist in sampling frame and with this biases are possible
STRATIFIED SAMPLING
divided to mutually exclusive strata and then randomly sample from each strata
sample are selected according to the required sizes.
best use when
within strata are HOMOGENEOUS ( similar characteristic)
between strata are HETEREGENOUS as possible
Strength
includes all importance subpopulations and researchers can control sample size in strata
Weakness
its expensive and more time consuming than other sampling technique
CLUSTER SAMPLING
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
cost effective and work is reduced , economically more efficient than simple random
weakness
imprecise and difficult to compute and to interpret result
is a numerical description of a population characteristic
Parameter - population
Statistic - sample