- Regression analysis is a statistical technique for modeling relationships between variables, where one variable is dependent on the others. It allows predicting the average value of the dependent variable based on the independent variables.
- The key assumptions of regression models are that the error terms are normally distributed with zero mean and constant variance, and are independent of each other.
- Linear regression specifies that the dependent variable is a linear combination of the parameters, though the independent variables need not be linearly related. In simple linear regression with one independent variable, the least squares estimates of the intercept and slope are calculated to minimize the sum of squared errors.
this presentation defines basics of regression analysis for students and scholars. uses, objectives, types of regression, use of spss for regression and various tools available in the market to calculate regression analysis
Multiple regression analysis is a powerful technique used for predicting the unknown value of a variable from the known value of two or more variables.
Explaining correlation, assumptions,coefficients of correlation, coefficient of determination, variate, partial correlation, assumption, order and hypothesis of partial correlation with example, checking significance and graphical representation of partial correlation.
HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
this ppt gives you adequate information about Karl Pearsonscoefficient correlation and its calculation. its the widely used to calculate a relationship between two variables. The correlation shows a specific value of the degree of a linear relationship between the X and Y variables. it is also called as The Karl Pearson‘s product-moment correlation coefficient. the value of r is alwys lies between -1 to +1. + 0.1 shows Lower degree of +ve correlation, +0.8 shows Higher degree of +ve correlation.-0.1 shows Lower degree of -ve correlation. -0.8 shows Higher degree of -ve correlation.
this presentation defines basics of regression analysis for students and scholars. uses, objectives, types of regression, use of spss for regression and various tools available in the market to calculate regression analysis
Multiple regression analysis is a powerful technique used for predicting the unknown value of a variable from the known value of two or more variables.
Explaining correlation, assumptions,coefficients of correlation, coefficient of determination, variate, partial correlation, assumption, order and hypothesis of partial correlation with example, checking significance and graphical representation of partial correlation.
HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
this ppt gives you adequate information about Karl Pearsonscoefficient correlation and its calculation. its the widely used to calculate a relationship between two variables. The correlation shows a specific value of the degree of a linear relationship between the X and Y variables. it is also called as The Karl Pearson‘s product-moment correlation coefficient. the value of r is alwys lies between -1 to +1. + 0.1 shows Lower degree of +ve correlation, +0.8 shows Higher degree of +ve correlation.-0.1 shows Lower degree of -ve correlation. -0.8 shows Higher degree of -ve correlation.
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Job opening: Micro-agribusiness and food security specialist, BoliviaIS Bolivia
Micro-agribusiness and food security specialist
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Many children in rural communities in Bolivia face a lack of educational opportunities, including through a scarcity of secondary schools, distance from school, and a national curriculum that doesn't respond to local realities. K'anchay ("to illuminate" in Quechua) manages/co-manages 8 boarding schools that focus on validating Quechua traditions and agricultural knowledge through experiential learning. The K'anchay centre in Vila Vila provides training in sustainable agriculture, soil conservation, reforestation, nutritional crops, small animal husbandry and preventive health care, as well as preparing young people for leadership positions within their own community.
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See www.isbolivia.org/blog or www.internationalservice.org.uk for more information or to apply for this position.
Regression Analysis presentation by Al Arizmendez and Cathryn LottierAl Arizmendez
We present an overview of regression analysis, theoretical construct, then provide a graphic representation before performing multiple regression analysis step by step using SPSS (audio files accompany the tutorial).
This project looks at the abilities of GARCH family models to forecast stock market volatility. FTSE 100 stock market returns are covered over the 10 years period in attempt to contribute to wide range of studies made on GARCH models.
The dissertation received 93% and was highly appreciated at the University of Portsmouth.
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Econometrics notes (Introduction, Simple Linear regression, Multiple linear r...Muhammad Ali
Econometrics notes for BS economics students
Muhammad Ali
Assistant Professor of Statistics
Higher Education Department, KPK, Pakistan.
Email:Mohammadale1979@gmail.com
Cell#+923459990370
Skyp: mohammadali_1979
Utilitas Mathematica Journal is a broad scope journal that publishes original research and review articles on all aspects of both pure and applied mathematics.The journal publishes original research in all areas of pure and applied mathematics, statistics.
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Our journal has to a fully open-access format is a significant step toward advancing the principles of open science and equitable access to knowledge. However, this transition also brings challenges, such as ensuring sustainable funding models and maintaining rigorous peer-review standards.
A Clutch is a mechanical device which provides for the transmission of power (and therefore usually motion) from one component (the driving member) to another (the driven member). The opposite component of the clutch is the brake.
Benzene is an organic chemical compound with the molecular formula C6H6. Benzene is a colorless and highly flammable liquid with a sweet smell and a relatively high melting point
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2. Introduction to Regression Analysis
• Regression analysis is the most often applied technique of
•
•
•
statistical analysis and modeling.
If two variables are involved, the variable that is the basis of
the estimation, is conventionally called the independent
variable and the variable whose value is to be estimated+ is
called the dependent variable.
In general, it is used to model a response variable (Y) as a
function of one or more driver variables (X1, X2, ..., Xp).
The functional form used is:
Yi = 0 + 1X1i + 2X2i + ... + pXpi +
• The dependent variable is variously known as
explained variables, predictand, response and
endogenous variables.
• While the independent variable is known as
explanatory, regressor and exogenous variable.
3. Definition
The Regression Analysis is a technique of studying the dependence of one variable
(called dependant variable), on one or more variables (called explanatory variable),
with a view to estimate or predict the average value of the dependent ariables in terms
of the known or fixed values of the independent variables.
The regression technique is primarily used to :
• Estimate the relationship that exists, on the average, between the dependent variable
and the explanatory variable
• Determine the effect of each of the explanatory variables on the dependent variable,
controlling the effects of all other explanatory variables
• Predict the value of dependent variable for a given value of the explanatory variable
4. HISTORY
The term "regression" was coined by Francis Galton in the nineteenth century
to describe a biological phenomenon. The phenomenon was that the heights
of descendants of tall ancestors tend to regress down towards a normal average
(a phenomenon also known as .egression towards the mean] For Galton,
regression had only this biological meaning, but his work was later extended
by Uday Ule and Karl Pearson to a more general statistical context.I n the work
of Yule and Pearson, the joint distribution of the response and explanatory
variables is assumed to be Gaussian. This assumption was weakened by R.A
Fisher in his works of 1922 and 1925 Fisher assumed that the conditional
distribution of the response variable is Gaussian, but the joint distribution
need not be. In this respect, Fisher's assumption is closer to Gauss's
formulation of 1821.
5. Assumptions of the Linear
Regression Model
1.
2.
3.
4.
5.
6.
7.
8.
9.
Linear Functional form
Fixed independent variables
Independent observations
Representative sample and proper specification of
the model (no omitted variables)
Normality of the residuals or errors
Equality of variance of the errors (homogeneity of
residual variance)
No multicollinearity
No autocorrelation of the errors
No outlier distortion
6. Derivation of the
Intercept
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7. The term in the model is referred to as a
“random error term” and may reflect a number of
things including the general idea that knowledge
of the driver variables will not ordinarily lead to
perfect reconstruction of the response.
8. • If there is only one driver variable, X, then we
usually speak of “simple” linear regression analysis.
• When the model involves
• (a) multiple driver variables,
• (b) a driver variable in multiple forms, or
• (c) a mixture of these, the we speak of “multiple linear
regression analysis”.
• The “linear” portion of the terminology refers to
the response variable being expressed as a “linear
combination” of the driver variables.
9. EXAMPLE
An agronomist may be interested in studying the dependence of paddy on
temperature, rainfall, amo0nt of fertilizer and soil fertility. such a dependency
analysis maybe enable the forecasting of the average yield, given information
about the explanatory variables
10. In regression analysis, the data used to describe the relationship
between the variables are primarily measured on interval scale.
the chief advantage of using the interval level of measurement is
that, with such data it is possible to describe the relationship
between variables more exactly employing mathematical
equation. This in turn allows more accurate prediction of one
variable from the knowledge of the other variables, which is one
of the most important objectives of regression analysis.
11. It is important to note that if the relationship between
X and Y is curvilinear , the regression line will be a
curved line rather than straight line. The greater the
strength of relationships between X and Y the better
is the prediction.
12. The problem is presented to the mathematician
as follows: "The values of a and b in the linear
model Y'i = a + b Xi are to be found which
minimize the algebraic expression ."
The mathematician begins as follows:
16. THE REGRESSION MODEL
The situation using the regression model is analogous to that of the
interviewers, except instead of using interviewers, predictions are made by
performing a linear transformation of the predictor variable. Rather than
interviewers in the above example, the predicted value would be obtained by
a linear transformation of the score. The prediction takes the form
where a and b are parameters in the regression model.
17. EXAMPLE USES OF REGRESSION MODELS
Pregnancy
A woman in the first trimester of pregnancy has a great deal of
concern about the environmental factors surrounding her pregnancy
and asks her doctor about what to impact they might have on her
unborn child. The doctor makes a "point estimate" based on a
regression model that the child will have an IQ of 75. It is highly
unlikely that her child will have an IQ of exactly 75, as there is always
error in the regression procedure. Error may be incorporated into the
information given the woman in the form of an "interval estimate."
For example, it would make a great deal of difference if the doctor
were to say that the child had a ninety-five percent chance of having
an IQ between 70 and 80 in contrast to a ninety-five percent chance
of an IQ between 50 and 100. The concept of error in prediction will
become an important part of the discussion of regression models.
It is also worth pointing out that regression models do not make
decisions for people. Regression models are a source of information
about the world. In order to use them wisely, it is important to
understand how they work.
18. Types of regression analysis:
Regression analysis is generally classified into two kinds: simple and
multiple. Simple
regression involves only two variables, one of which is dependent variable
and the other
Is explanatory(independent) variable. The associated model in the case of
simple
regression will be a simple regression model.
•A regression analysis may involve a linear model or a nonlinear model.
The term linear can be interpreted in two different ways:
1. Linear in variable
2. Linearity in the parameter
19. Regression Analysis: Model Assumptions
Model assumptions are stated in terms of the random
errors, , as follows:
the errors are normally distributed,
with mean = zero, and
constant variance 2, that does not depend on the settings
of the driver variables, and
the errors are independent of one another.
This is often summarized symbolically as: is NID(0,
2)
20. LINEAR REGRESSION
In linear regression, the model specification is that the dependent variable,
yi is a linear combination of the parameters (but need not be linear in the
independent variables). For example, in simple linear regression for
modeling n data points there is one independent variable: xi, and two
parameters, β0 and β1:
Fig: Illustration of linear regression on a data set
21. In the case of simple regression, the formulas for
the least squares estimates are