Multiple regression analysis , its methods among which multiple regression analysis one of the popular method. also discuss the applications and purposes
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.
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'Criterion Variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution.
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.
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'Criterion Variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution.
A brief description of F Test and ANOVA for Msc Life Science students. I have taken the example slides from youtube where an excellent explanation is available.
Here is the link : https://www.youtube.com/watch?v=-yQb_ZJnFXw
A brief description of F Test and ANOVA for Msc Life Science students. I have taken the example slides from youtube where an excellent explanation is available.
Here is the link : https://www.youtube.com/watch?v=-yQb_ZJnFXw
Discriminant analysis is a technique that is used by the researcher to analyze the research data when the criterion or the dependent variable is categorical and the predictor or the independent variable is the interval in nature. The term categorical variable means that the predictor variable is divided into a number of categories.
DA is typically used when the groups are already defined prior to the study.
The end result of DA is a model that can be used for the prediction of group memberships. This model allows us to understand the relationship between the set of selected variables and the observations. Furthermore, this model will enable one to assess the contributions of different variables.
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
Data Analysis: Statistical Methods: Regression modelling, Multivariate Analysis - Classification: SVM & Kernel Methods - Rule Mining - Cluster Analysis, Types of Data in Cluster Analysis, Partitioning Methods, Hierarchical Methods, Density Based Methods, Grid Based Methods, Model Based Clustering Methods, Clustering High Dimensional Data - Predictive Analytics – Data analysis using R.
It is about Data analyzis that refers to sifting, organizing, summarizing and synthesizing the data so as to arrive at the results and conclusions of the research.
This presentation explains in detail, the classification of living organisms,nomenclature systems, what is taxonomy, its parts, how organisms are classified in the taxonomical hierarchy, etc.
Zero waste - an initiative towards the sustainability of the world ... this poster / infograph speaks the importance, need and easier ways to go zero waste .
This presentation is about the antibiotics and their classification based on various aspects such as -
their chemical structure
mode of action
activity
route of administration
origin
their effect etc.
Active edible films - An emerging trend in Food Packing technologyRaihanathusSahdhiyya
Due to the environmental impacts caused by various types of packaging material (like plastic wraps, packs), Edible films are being developed with beneficial characteristics for both human and environment
Cream separators are used in Dairy industries to separate the cream and obtain fat-reduced or fat-free milk. This presentation is about the types of cream separators employed in the dairy industry
Homogenization is a process of micronization of fat globules in milk to achieve the uniform consistency and taste. This presentation discuss the types of Homogenizers used
Icecream is well-likes dairy product due to its smooth creamy texture which is achieved by various types of processing and freezing. This presentation is about the types of freezers used in Icecream manufacture
Microbial spoilage by Anaerobic Microorganisms pose higher risks in canned foods. This presentation discuss the microbial spoilage of canned foods by various group of microbes
Nanotechnology is the emerging technology in almost all fields of science ..It is preferred and studied due to its high efficiency in all fields of its application... Also being used in overcoming or eliminating environmental pollution to a greater level, this presentation is all about how Nanotechnology is useful in treating polluted water
Sanger sequencing is one of the DNA sequencing methods used to identify and determine the sequence (Nucleotide) of DNA .This is an enzymatic method of sequencing developed by Fred Sanger.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
1. Multivariate Analysis
Basic Principles and Applications of Multiple Regression Analysis
Presented by,
A.Raihanathus Sahdhiyya,
II M.Sc.,Microbiology,
TBAK College
Submitted to,
Dr. F. Arockiya Aarthi Rajathi,
Asst. Professor,
Dept. of Microbiology &
Biotechnology,
TBAK College
2. What is Multivariate Analysis ??
Multivariate analysis (MVA) is based on the statistical principle of
multivariate statistics, which involves observation and analysis of
more than one statistical outcome variable at a time
It is used to address the situations where multiple measurements are
made on each experimental unit and the relations among these
measurements and their structures are important
6. What is Regression Analysis ??
▪ Regression analysis is used in stats to find trends in data
▪ will provide you with an equation for a graph so that you can make predictions about your
data
▪ For example, if you’ve been putting on weight over the last few years, it can predict how much
you’ll weigh in ten years time if you continue to put on weight at the same rate
▪ t will also give you a slew of statistics (including a p-value and a correlation coefficient) to tell
you how accurate your model is
Essentially, regression is the “best guess” at using a set of data to
make some kind of prediction. It’s fitting a set of points to a graph
7. Multiple Regression Analysis
- most commonly utilized multivariate technique and often used as a forecasting tool
- is used to see if there is a statistically significant relationship between sets of
variables. It’s used to find trends in those sets of data
Multiple regression analysis is almost the same as simple linear regression. The only
difference between simple linear regression and multiple regression is in the number
of predictors (“x” variables) used in the regression.
● Simple regression analysis uses a single x variable for each dependent “y”
variable. For example: (x1, Y1).
● Multiple regression uses multiple “x” variables for each independent
variable: (x1)1, (x2)1, (x3)1, Y1).
8. Multiple Regression Analysis Output
Regression analysis is always performed in software, like Excel or SPSS. The output
differs according to how many variables you have but it’s essentially the same type of
output you would find in a simple linear regression. There’s just more of it:
● Simple regression: Y = b0 + b1 x.
● Multiple regression: Y = b0 + b1 x1 + b0 + b1 x2…b0…b1 xn.
The output would include a summary, similar to a summary for simple linear regression,
that includes: R (the multiple correlation coefficient), R squared (the coefficient of
determination), adjusted R-squared, The standard error of the estimate.