The learning outcomes of this topic are:
- Evaluate results from regression analysis
- Interpret results from regression analysis
- Recognise the possibility to extend regression analysis (dummy variables)
The learning outcomes of this topic are:
- Understand a straight line fit to bivariate data
- Calculate and interpret Pearson’s correlation coefficient
- Calculate and interpret Spearman’s correlation coefficient
The learning outcomes of this topic are:
- Find the derivative of variables raised to a power
- Use the rules of differentiation
- Relate differentiation to optimization (Obtain the economic order quantity formula)
This topic will cover:
- Gradient
- Definition of the derivative
- Rules of differentiation
The learning outcomes of this topic are:
- Carry out partial differentiation
- Relate partial differentiation to optimization
- Calculate partial point elasticities
- Recognize the total differential
This topic will cover:
- Partial Differentiation
- Total differential
The learning outcomes of this topic are:
- Understand a straight line fit to bivariate data
- Calculate and interpret Pearson’s correlation coefficient
- Calculate and interpret Spearman’s correlation coefficient
The learning outcomes of this topic are:
- Find the derivative of variables raised to a power
- Use the rules of differentiation
- Relate differentiation to optimization (Obtain the economic order quantity formula)
This topic will cover:
- Gradient
- Definition of the derivative
- Rules of differentiation
The learning outcomes of this topic are:
- Carry out partial differentiation
- Relate partial differentiation to optimization
- Calculate partial point elasticities
- Recognize the total differential
This topic will cover:
- Partial Differentiation
- Total differential
Pedagogy of Mathematics - Part II (Numbers and Sequence - Ex 2.3), Numbers and Sequence, Maths, IX std Maths, Samacheerkalvi maths, II year B.Ed., Pedagogy, Mathematics, Modular arithmetic, congruence module, connecting euclid's lemma and modular arithmetic, Module operations,
Pedagogy of Mathematics - Part II (Numbers and Sequence - Ex 2.1), Numbers and Sequence, Maths, IX std Maths, Samacheerkalvi maths, II year B.Ed., Pedagogy, Mathematics, Euclid's Division Lemma, Euclid's Division algorithm,
Machine learning in science and industry — day 1arogozhnikov
A course of machine learning in science and industry.
- notions and applications
- nearest neighbours: search and machine learning algorithms
- roc curve
- optimal classification and regression
- density estimation
- Gaussian mixtures and EM algorithm
- clustering, an example of clustering in the opera
Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL
Data Augmentation and Disaggregation by Neal FultzData Con LA
Abstract:- Machine learning models may be very powerful, but many data sets are only released in aggregated form, precluding their use directly. Various heuristics can be used to bridge the gap, but they are typically domain-specific. The data augmentation algorithm, a classic tool from Bayesian computation, can be applied more generally. We will present a brief review of DA and how to apply it to disaggregation problems. We will also discuss a case study on disaggregating daily pricing data, along with a reference implementation R package.
Pedagogy of Mathematics - Part II (Numbers and Sequence - Ex 2.3), Numbers and Sequence, Maths, IX std Maths, Samacheerkalvi maths, II year B.Ed., Pedagogy, Mathematics, Modular arithmetic, congruence module, connecting euclid's lemma and modular arithmetic, Module operations,
Pedagogy of Mathematics - Part II (Numbers and Sequence - Ex 2.1), Numbers and Sequence, Maths, IX std Maths, Samacheerkalvi maths, II year B.Ed., Pedagogy, Mathematics, Euclid's Division Lemma, Euclid's Division algorithm,
Machine learning in science and industry — day 1arogozhnikov
A course of machine learning in science and industry.
- notions and applications
- nearest neighbours: search and machine learning algorithms
- roc curve
- optimal classification and regression
- density estimation
- Gaussian mixtures and EM algorithm
- clustering, an example of clustering in the opera
Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL
Data Augmentation and Disaggregation by Neal FultzData Con LA
Abstract:- Machine learning models may be very powerful, but many data sets are only released in aggregated form, precluding their use directly. Various heuristics can be used to bridge the gap, but they are typically domain-specific. The data augmentation algorithm, a classic tool from Bayesian computation, can be applied more generally. We will present a brief review of DA and how to apply it to disaggregation problems. We will also discuss a case study on disaggregating daily pricing data, along with a reference implementation R package.
The K-Nearest Neighbors (KNN) algorithm is a robust and intuitive machine learning method employed to tackle classification and regression problems. By capitalizing on the concept of similarity, KNN predicts the label or value of a new data point by considering its K closest neighbours in the training dataset. In this article, we will learn about a supervised learning algorithm (KNN) or the k – Nearest Neighbours, highlighting it’s user-friendly nature.
What is the K-Nearest Neighbors Algorithm?
K-Nearest Neighbours is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining, and intrusion detection.
It is widely disposable in real-life scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about the distribution of data (as opposed to other algorithms such as GMM, which assume a Gaussian distribution of the given data). We are given some prior data (also called training data), which classifies coordinates into groups identified by an attribute.
The learning outcomes of this topic are:
- Recognise the concept of constrained optimisation
- Formulate a two variable linear programme (maximisation and minimisation problems)
- Find a graphical solution to a two variable LP
- Appreciate the process of sensitivity analysis
The learning outcomes of this topic are:
- Perform a single sample t-test of the mean
- Perform a two sample t-test
- Interpret significance probabilities
- Perform a x2 goodness of fit test
This topic will cover:
- Hypothesis testing with a sample (confidence intervals, fixed level, significance testing)
- Two sample t-test
- Significance, errors and power
- Frequency data and the x2 test
The learning outcomes of this topic are:
- Recognize the terms sample statistic and population parameter
- Use confidence intervals to indicate the reliability of estimates
- Know when approximate large sample or exact confidence intervals are appropriate
This topic will cover:
- Sampling distributions
- Point estimates and confidence intervals
- Introduction to hypothesis testing
The learning outcomes of this topic are:
- recall the rules of simple probability
- use key probability distributions (Binomial distribution, Poisson distribution, Exponential distribution, Normal distribution)
- calculate z-scores
This topic will cover:
- Simple probability revision
- Probability distributions
- Standard scores (z-scores)
The learning outcomes of this topics are:
- recognize nominal, ordinal, interval and ratio data types
- recognize and use mode, median, mean, range, standard deviation and coefficient of variation
- calculate Laspeyres and Paasche index numbers
- use index numbers to calculate percentage changes and to deflate series
This topic will cover:
- data types
- a revision of summary statistics
- index numbers
The learning outcome is to describe linear cost functions, to explain the importance of causality in estimating cost functions, to understand various methods of cost estimation, and to outline six steps in estimating a cost function using quantitative analysis.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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?
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
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!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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2. This topic will cover:
◦ Regression using software
◦ Multiple linear regression
Extending regression models with dummy
variables
Interpreting models
3. By the end of this topic students will be able
to:
◦ Evaluate results from regression analysis
◦ Interpret results from regression analysis
◦ Recognise the possibility to extend regression
analysis
Dummy variables
4. • For the least SSE straight line,
y = mx + c
m =
n xy − x y
n x2 − x 2
c = y − mx
R =
n xy− x y
n x − x 2 n y2 − y 2
5. x y
25 1.44
50 5.58
75
14.6
4
100 6.94
x y
25 3.07
50 5.64
75 9.63
100
10.2
6
6. ◦ Many software packages
OpenOffice, Gretl
MS Excel
SPSS, Minitab, SAS
R, S
◦ MS Excel
2007/2010 Data/Data Analysis/Regression
Older Tools/Data Analysis/ Regression
Then all versions are similar
7.
8.
9. ◦ Reasons Not to Set Constant Term to Zero
It prevents model being biased
Usually interested in the predictor variables anyway
You don’t need to collect data for it
Can help if data is only locally linear
◦ Reason to Set constant Term to Zero
If it is supposed to be zero
Strong theoretical grounds
But care needed with calculation and
interpretation of R2
10.
11. ◦ Models such as
y = c + b1x1+ b2x2+ ...
◦ How are they developed?
an expert task
◦ Managers
understand
question
use results
12. ◦ Estate agent (realtor) is establishing an office in a
new location
◦ Wishes to build a model of advertised prices
◦ Collects competitor data on;
Internal area
Land
Distance from nearest school
City region
13. Property Price School Land Area District
1 457 3 1791 165 FD
2 487 1 800 177 FD
3 218 3 759 94 FD
4 300 4 829 137 FD
5 358 2 630 110 AC
6 658 1 655 201 AC
7 402 2 999 85 AC
8 541 2 920 146 AC
9 358 3 1185 112 Other
10 444 1 787 155 Other
11 298 3 597 180 Other
12 462 1 1447 200 Other
14. Property Price School Land Area AC FD District
1 457 3 1791 165 0 1 FD
2 487 1 800 177 0 1 FD
3 218 3 759 94 0 1 FD
4 300 4 829 137 0 1 FD
5 358 2 630 110 1 0 AC
6 658 1 655 201 1 0 AC
7 402 2 999 85 1 0 AC
8 541 2 920 146 1 0 AC
9 358 3 1185 112 0 0 Other
10 444 1 787 155 0 0 Other
11 298 3 597 180 0 0 Other
12 462 1 1447 200 0 0 Other
15. price =
constant +
a x (km from a school) +
b x (land in m2) +
g x (floor area in m2) +
d (if in AC) +
z (if in FD)
19. R2 =
y − y 2
y − y 2
R
2
= 1- (1 - R2)
n −1
n − k − 1
20.
21.
22.
23.
24. ◦ Equation
expected price = 45.72 + (2.179 x area) +
(148.8 x AC)
◦ Suppose property is in AC and is of 100m2 what is
expected advertised price?
expected price = 45.72 + (2.179 x 100) +
(148.8 x 1)
expected price = 412.42
25. By the end of this topic students will be able
to:
◦ Evaluate results from regression analysis
◦ Interpret results from regression analysis
◦ Recognize possibility to extend regression analysis
Dummy variables
26. ◦ Hinton, PR. Statistics Explained. Routledge
◦ Keast, S. and Towler M. Rational Decision Making
for Managers. Wiley
◦ Wisniewski, M. Quantitative Methods for Decision
Makers. FT Prentice Hall