basic knowledge about the types of statistics
DESCRIPTION
Basic Knowledge About the Types of StatisticsTRANSCRIPT
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Variable comes from Vary + Able So variable is anything that its values can be varied. Variables are the phenomena or the topics that
researchers want to investigate.
Variable
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Variable
Describe
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VariableCorrelational Relationship
Variable
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Difference Testing or Measure of Association
Descriptive Statistics or Inferential Statistics
Univariate, Bivariate, Multivariate
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(Attribute)
(Object)
(Number)
(Measure)
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(Attribute)
(Object)
(Number)
(Measure)
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(Test, Measure) (Construct Variable) (Subject) (Variable Values)
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TOEFLS
JDI
(Job Description Index)
(Job Satisfaction)
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Nominal Scale Ordinal Scale Interval Scale Ratio Scale
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Nominal , = 1, = 2Ordinal ,
= 1 = 2 = 3
Interval , 0
0
1 = 2.84 2 = 3.65 3 = 4.12
Ratio , 0
0
1 = 0 2 = 7,650 3 = 9,000
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(Categorical or Discrete Variable) Nominal Scale Ordinal Scale
(Numerical or Continuous Variable) Interval Scale Ratio
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(Descriptive Statistics) (Inferential Statistics)
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2
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(Descriptive Statistics)
Population Parameter , ,m s r
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(Descriptive Statistics) (Inferential Statistics)
Population
Sample
Parameter
Statistic Mean, SD., r.
, ,m s r
Hypothesis Testing
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Parameter Statistic
Parameter and Statistic
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(Central Tendency) (Variation) (Distribution)
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CentralTendency
VariationDistribution
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Ordinal Scale Interquartile Range
Interval and Ratio Scale Range (Max. - Min.) Variance Standard Deviation
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(Skewness) (Kurtosis)
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(Normal Distribution)
MeanMedian
Mode
Symmetric Bell Shaped Mean = Median = Mode
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(Negatively Skewed)
Mode
Median
Mean
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(Positively Skewed)
Mode
Median
Mean
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(Kurtosis)
Platykurtic Leptokurtic Mesokurtic
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What is random variable?
(Random Variable)
Random Variable = Random Sample + Variable
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(Probability) 100 20 80
1 100 ?
1
Probability
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= 20/100 = .20 = 80/100 = .80 1
Probability
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2 1 20 80 2 80 20
10 3 7 ?
2
Probability
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10 2
Probability
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0 1 2
1 1 2
::
HH
m mm mm m
=
1 20 80
2 80 20
7 3 (p=.03)
H1 2
= 10
(p=.03)
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E K 4 7
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H0 : = H1 :
E K 4 7
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Verification Karl Poppers Falsification
Logic of Disconfirming Null Hypothesis Null hypothesis can never be proved to be true, only
falsifying or disconfirming
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Type I and Type II Error
Type I Error H0 H0 Type II Error H0 H0
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Type I and Type II Error
Type I Error False Alarm (Alpha)
Type II Error Failed Alarm (Beta)
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Type I and Type II Error
Effect Size
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Null Hypothesis
(Effect Size)
Power
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Type I and Type II Error
H0 : H1 : Type I Error =
Type II Error =
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Type I and Type II Error
H0 : A 1 .. H1 : A 1 .. Type I Error = A
1 .. Type II Error = A
1 ..
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Type I and Type II Error
H0 : HIV H1 : HIV Type I Error =
HIV
Type II Error = HIV
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Type I and Type II Error
H0 : H1 : Type I Error =
Type II Error =
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Type I and Type II Error
Type I Type II Error
Type I Type II Type II
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Significance Testing
Confidence Interval
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Point Estimate Interval Estimate
Confidence Interval
Confidence Interval = Statistics + Sampling Error (Margin Error)
Confidence Interval
X Zn
s
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(Precision)
H0
Confidence Interval
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Confidence Interval
X Zn
s
200010,200 1.96400
10,200 196 10,004 10,396 = -
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Statistical Significance Practical Significance
Significance
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Types of Statistics
Difference Testing Statistics (Descriptive Statistics) t-test, ANOVA, ANCOVA, Chi-Square
Measures of Association Statistics Pearsons r, Regression, Canonical Correlation
Interdependence Statistics Factor Analysis, Cluster Analysis, Multidimensional
Scaling
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Types of Statistics
Parametric Statistics t-test, ANOVA, Pearsons r, Regression
Nonparametric Statistics Mann-Whitney U, Wilcoxon, Kruskal Wallis
Robust Statistics Trimmed Mean, Winsorized Mean, Winsorized Variance,
Resampling Statistics
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(Bivariate Statistics)
t-test Y = X Y X
2 Independent Sample
Assumptions Mann-Whitney U Dependent Sample
Assumptions Wilcoxon Signed Rank
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(Bivariate Statistics)
ANOVA Y = X Y X
2 ANOVA
Assumptions Kruskal Wallis ANOVA for Repeated Measures
Assumptions Friedman Test
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(Bivariate Statistics)
Pearsons r Y = X Y X
Chi-square Y = X Y X
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(Multivariate Statistics)
Multiple Regression Manova (Multivariate Analysis of Variance) Discriminant Analysis Canonical Correlation Factor Analysis
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(Multivariate Statistics)
Multiple Regression Y = X1+X2+X3+...+Xn Y X
( Dummy) Manova (Multivariate Analysis of Variance)
Y1+Y2+Y3+...Yn = X1+X2+X3+...+Xn Y X
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(Multivariate Statistics)
Discriminant Analysis Y = X1+X2+X3+...+Xn Y X
( Dummy) Canonical Correlation
Y1+Y2+Y3+...Yn = X1+X2+X3+...+Xn Y X
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(Multivariate Statistics)
Factor Analysis X1+X2+X3+...+Xn X
Multilevel Analysis Y = X1+X2+X3+...+Xn Y X
( Dummy) X
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Multilevel Analysis
(OCB)
1. (JS) 2. (ORCOM) 3.
(POS) 4. (MOTIV)
1. (LEADER)
2. (CULTURE)
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Structural Equation Modeling
EDU
DURA
EXP
TF
CH INS INT IND
QUAL
Q4
Q3
Q2
Q1
Q5
Q6
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