Lecture 5 Thoery

8/8/2019 Lecture 5 Thoery

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Normality analysis of data in

Minitaby P-Value > 0.05 Null hypothesis accepted

y P-Value


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Normality analysis of data in

Minitaby P value 0.035 means 3.5% probability that data is

normally distributed.

y P value 1 means there is 100 percent probability thatdata is normally distributed


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Correlation of data variablesy Q. What is correlation analysis?

A. Correlation analysis looks at the indirectrelationships in survey data

y Q. When would you use it?A. To objectively establish which variables are mostclosely associated with a given action or mindset

y Q. What are the advantages?A. It can provide a more discriminatory analysis thanasking a direct question


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Correlation of data variablesy In statistics, correlation and dependence are any of a

broad class of statistical relationships between two or

more random variables or observed data values.

y General examples are

y Correlation between physical properties and offspring

y Correlation between product demand and supply


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Correlation of data variablesy Formally, dependence refers to any situation in which

random variables do not satisfy a mathematical

condition of probabilistic independence

y P value for variables dependencies and concept ofsignificant correlation between two variables.

y Correlation between two variables represented by 1and no correlation is represented by 0.


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Different outputs of correlation


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The concept of modeling

yWhat is meant by modeling?

y Types of modeling

y Advantages of modeling

y Mathematical modeling

y Statistical modeling

y AI based Modeling


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Regression analysisy The concept of dependent and independent variables

y The relationship of dependent and independentvariables in form of an equation.

y The concept of linearity and non linearity.

y The concept of confidence level or accuracy percentage


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Regression analysisY

Y = mX + b

b = Y-intercept

X

Change

in Y

Change in X

m = Slope


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Regression analysis

YY XXii ii ii!! FF FF II00 11

y 1. Relationship Between Variables Is a LinearFunction

DependentDependent(Response)(Response)VariableVariable(e.g., income)(e.g., income)

IndependentIndependent(Explanatory)(Explanatory)VariableVariable(e.g., education)(e.g., education)

PopulationPopulationSlopeSlope

PopulationPopulationYY--InterceptIntercept

RandomRandomErrorError


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Regression analysisPath Diagram of A Linear Regression

i iY k b x b x b x e!

1 1 2 2 3 3


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Regression analysis

UnknownRelationship

PopulatioPopulationn

Random SampleRandom Sample

Y Xi i i! F F I0 1

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Lecture 5 Thoery

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