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Introduction To Statistics part 1 | Darwin Ong | YouTubeToText
YouTube Transcript: Introduction To Statistics part 1
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Video Transcript
let's
discuss the introduction to the statistics
statistics
there are three kinds of lies according
to me these are any
lies damn lies and
statistics because
how to lie how to lie with statistics
are everywhere and the statistical techniques
techniques
are used to make many decisions
that are that affect our lives
no matter what your career you will make
professional decisions that involve data
and understanding of statistical methods
will help you
make these decisions effectively
so these are the application of statistical
statistical
concepts in the business world so in finance
finance
marketing personal and operating
well so statistics is the science of collectioning
collectioning
organizing presenting
analyzing and interpreting data to
assist in making more effective
decisions statistical
analysis is used to manipulate
summarize and investigate data so that
useful decision-making informations
results so on
that end that statistics amounts of
so into the qualities of a good statisticians
statistics or s stands for scientific
t for talented a for active
t for tenacious i for intensive
menti by mean s for skillful
t is for a specific i for innovative
c for creative and i interpretative
a accurate and n for noble
so these are the scope of statistics so
the heart of that is
scientific researches are its use in all
field of endeavors
for example we have fisheries agriculture
agriculture
commerce trade and industry education biology
biology
economics psychology sociology chemistry physics
physics
engineering computer and food technology
in commerce trade in industry we have
the statistical techniques that
are vital in the planning production and marketing
marketing
of commodities prices costs and profits
the statistical results serves as
basis for making policies on
in engineering statistics is vital
in the analysis and interpretation on
the durability of the buildings
roads and bridges evidently
material testing requires knowledge in statistics
statistics
for example so we have standard
deviation mean and analysis of variance
in so the programs have better
understanding in another
analyzed and interpreted through
statistical techniques
experiments in computer are analyzed
interpreted through statistics for
better understanding
of the results so in food technology
researchers are on the acceptability
nutritive values sales
sales ability and profitability of foods are
are
imperative with the use of statistical tools
tools
likewise the shelf life the return is
determination of any food product
employs a statistical tool
statistics is also used in food products
research and development
so these are the functions of statistics
so it provides research tests that means
of to
satisfact scientifically measure
the conditions that may be involved in the
the
given results questions and evaluation
the way in which
these conditions are related and to show
the laws
underlying facts and events that cannot be
be
determined means by individual operating observations
observations
and it reveals the relation of cause and effect
effect
that otherwise may remain unknown and it
observes trends and behavior in related
conditions which
and it permits the most exact
kind of description and it assists the researcher
researcher
to be definite and exact in his
procedures and his thing in his thinking
and it enables the researcher to
summarize results in a meaningful and
convenient form and it also enables the
researcher to draw
general conclusions the process of extracting
extracting
conclusion in carried out according to
accepted rules and then it also enables
the researcher
to predict how much of a thing will
happen under his conditions he knows
and has measured
and our objective for the research in
studying statistics
it comprehends the logic of statistics
to determine whether to apply
appropriate statistical tools
tools in different research questions
and where not to apply them and to
interpret statistical results correctly and
and
vividly and to determine the basic
mathematics in statistics lastly we have to
to
master the language of statistics
there are two types so we have
descriptive statistics
and inferential statistics for the
descriptive statistics it is methods of organizing
organizing
summarizing and presenting data in the in
in
informative way for the informative
inferential statistics
the methods used to determine something
about the population on the basis of a sample
sample
so population the entire set of
individual or
objects interest or the measure measurements
measurements
obtained from all individuals or objects
of the interest
so sample or a portion or a part of the
population of
interest so you sample monagar is a population
represent the population refers to the entire
entire
group of set of individuals or
items to which the researchers would
like to generalize
the results of the study it will be used
by the researchers in order to determine
the scientific output of research
and target population refers to an
entire set
of individual about which we require information
information
for example all 20 year olds in batanga city
city
and accessible population is the basic
field set
of individual from which example is drawn
solving for the sample size so we have sorry
sorry
if you want to solve for the original
sample size is a general
of individuals in research study on which
which
information or generalization about the
population is drawn
and is the sample size and big
capital n is the population size and
obviously that is 0.05 or 0.05 or
5 percent for the
sample size we can use also the n
z equals n z squared p multiply one
minus p
all over n d squared plus z squared p
multiplied by one minus p where z is the
normal variable or
relativity level of 95
or nine percent that is usually that 1.96
1.96
where p is the largest possible
proportion result is 0.5
and this sampling error which is 0.05
so these are the population items
so if you're computing the sufficient
sample size integrate population
consisting of one thousand five twenty
four six graders
in a given school district using
sloven's formula
so where n is one thousand
five hundred twenty four your e remember that's
that's
five percent so it's getting
where n at n is one thousand by 124
over one plus five one thousand five
months when you multiply the quantity of
zero point zero five raise the squared
so we get three hundred sixteen point
eight four
so same pray we can approximate
at least 317 adding betting intervention
out of 1524
next in the previous example using the lynch
lynch
formula at the end sq squared
p of quantity one minus p so using that
so one thousand five two four multiply
one point nine z squared
multiplied by zero point five multiply 1
minus 0.5
all over 1524
multiply 0.05 squared plus 1.6
1.96 squared multiply 0.05
multiplied the quantity of 1 minus 0.5
where n is 306.72 more or less 307
so same going same problem
so 317 using slopes formula
using lynch formula we have 300
so for the inferential statistics estimation
estimation
estimate the population win weight using
the sample weight
mean example mean rate and
for the hypothesis testing test claim
the population
mean width is 70 kilos so inference is
the process of drawing conclusion or making
making
decision about a population based on a
sample results
results a sampling or example
should have the same characteristic as
the population it is representing
sampling can be with replacement a
member of the population may be chosen
more than once
speaking by candy from the bowel or demon
demon
or without replacement a number of the
population may be chosen only once
so sampling methods so we have the
random each member of the population has
an equal
chance of selected or non-random the
actual process of sampling
cause some causes sampling errors for example
example
the sample may not be large enough or representative
representative
of the population factors that related
to the
sampling process cause non-sampling errors
errors
a defective counting the device can
so these are the examples
but you go into the research you can use
simple random sample
such stratified cluster or systematic
if you're using this simple random
sample so
each sample of the size has an equal
divide the population into strata and
then randomly select
some of strata all the members from this
parameter systematic or randomly
selected starting point and take
every end piece of data from listing of
the population but systematic demand for example
so next we have to determine the sample
size of each
level given this table below competition
of sample size according to the year level
level
so we're first year setting your third
year and fourth year so if you want
members so we have two thousand and four
hundred seventy five
so we can use this lobe's formula so
2475 over one plus two four seven
multiplied by 0.05 degrees squared so
1 344 roughly 344
the the question is the first year it
has a second year
so it's the first year whereas the
five multiply three four four so we have
one added one
so for here three hundred over three
four seven five multiply three four four
so forty two so nothing equal
so determine the sample size for each
level given in the table below
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