intro to stat-pdf
TRANSCRIPT
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STATISTICSSTATISTICS
the science of collecting, organizing,the science of collecting, organizing,presenting, analyzing, and interpretingpresenting, analyzing, and interpreting
data to assist in making more effectivedata to assist in making more effective
decisions.decisions.
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WHY STUDY STATISTICS?
3 Reasons:
1.) Data are everywhere
2.) Statistical techniques are used to
make many decisions that affect ourlives (examples: Medicine, Water
quality, teaching methodologies, etc.)
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3.)No matter what your future line of
work, you will make decisions that
involve data.
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WHAT IS MEANT BY
STATISTICS?
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Numerical
Information
Examples:
1Average starting salary ofcollege graduates
2Average number of Fords
sold per month at Ford
Cagayan
3Percentage of
undergraduates attending
CU who will attend graduate
school
4The number of deaths due
to alcoholism last year
5etc.
Statistics
Graphical Information
Examples:
+1000
0
-1000
86 87 88 89 90 91 92
Net Income of PAL
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TYPES OF STATISTICS
Descriptive Statistics is a scientific
method of dealing with data. It is
the collection, organization,
presentation and interpretation of
numerical data. Statistics are also
quantities calculated fromobservations.
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Inferential Statistics involves the
interpretation of values resulting
from the descriptive techniques. It
also involves making inferences,
conclusions, or decisions about
the population of which thesample is a part, again using
sample results. The objective of
inferential statistics is to draw
inferences from a small group(sample) to a large group
(population) and to do so with a
well defined degree of confidence.
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VARIABLES AND
MEASUREMENTVariable. Characteristics or phenomenon
which may take on different values.The set values that the variable cantake is called its domain.
Example: weight, grades, income, age, jobperformance
Constant. Characteristics which assumeonly one value.
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Quantitative or Numerical Variable.Variables which are expressednumerically in terms of magnitude.
Example: height, income
Qualitative or Categorical Variables.Variables expressed in quality or kind.
Example: sex, color, type of school
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Continuous Variable. One which assumesall values between two points in acontinuous scale.
Example: weight, income
Discrete Variable. One which can onlyassume a finite number of values most
frequently integers.
Example: number of respondents in astudy
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Observation. Numerical recording ofinformation on a variable.
Example: variable weight
110 lbs. 100 lbs. 135 lbs.
9.85 lbs. 112.78 lbs.
Data. A collection of observations.
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Scales of Measurement
1. Nominal or Classificatory Scale
Numbers are used as codes simply toclassify an object, persons orcharacteristics into certain categories(Equivalence).
Example : Red = 0Blue = 1
Green = 2
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2. Ordinal or Ranking Scale
Numbers are used as codes, categoriesare not just different but be put in order(Equivalence, or)
Example: poor = 0
Midclass = 1
Rich = 2
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3. Interval Scale
Has all the characteristics of ordinal scaleand nominal scale and in addition, the
distance between 2 points on the scale isknown. However, the zero point isartificial. (Equivalence, or, differencebetween two points can be compared)
Example: 320 C, 900F (Variable-Temperature)
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4.) Ratio Scale
Has all the characteristics of the interval
scale and in addition has a true zero point.
Example: weight, height
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DATA COLLECTION
Primary Data. Gathered by the Researcher.
Secondary Data. Using data of other
sources
Census. Complete enumeration in whichevery member of the population is
included.
Sample Survey. Survey of a portion of thepopulation.
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Elements in a Survey Design:
1. Set of Objectives
2. Sampling Design
3. Data of gathering plan
4. Plan for analysis of collected data
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Sampling Procedures
1. Probability Sampling. A samplingmethod, which makes use of the
knowledge of the characteristics of theindividual element in the population
and thus, the chance that, each element
has of being drawn as sample.
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2. Non Probability Sampling. Asampling method which does not specify theprobability of selection of the elements in thepopulation.
Examples:
a.) accidents or haphazard samples items which come inhandy are taken as samples.
- TV commercial of a certain product where a buyer in asuperstore is interviewed
b.)judgment or purposive sampling sample is selected withthe researchers subjective judgment.
c.) quota sampling - purposive sampling with the addedspecification that sample is proport ioned to the population.
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Probability Sampling Techniques:
1. Random Sampling. Process of selectinga sample wherein every element in the
sampled population is given an equal non zero chance of entering the sample.
2. Systematic Random Sampling.
Sampling wherein every kth
unit isincluded after a random start is taken forthe sample.
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3. Stratif ied Sampling. Population isdivided into homogenous groups of strata
and selection is done within each strata.
4. Multi Stage Sampling. Sampling donein several stages.
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Sampling Techniques
SLOUVINS FORMULA
N
n = ---------------
1 + Ne2 where N = population
n = no. of sample
e = margin in of error or level of signi ficance
(0.01, 0.05, 0.001)
Example:
N = 100,000
e = 0.05
n = ?
100,000
n = ----------------------------- =398
1 + 100,000 (0.05)2
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Proportionate Sampling: (sampling Proportionate to Size)
Men 3,000 X
Women 20,000 Y
________________________________________
N = 23,000 n = 393
23, 000
n = ----------------------- = 393
1 + 23,000 (0.05)2
X 393
---------- = ---------- ; X = 51
3000 23,000
Y 393
---------- = -----------; Y = 342
20,000 23,000
or Y = 393 51 = 342