Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx
Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx
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
This theory relies on the market behaviour of the consumer to know about his preferences with regard to the various combinations for the two reactions and responses of the consumer.
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
This theory relies on the market behaviour of the consumer to know about his preferences with regard to the various combinations for the two reactions and responses of the consumer.
This presentation covers important topics such as
Multiple Independent Random Variables or i.i.d samples.
Expectations or Expected values
T-Distribution
Central Limit Theorem
Asymptotics & Law of Large Numbers
Confidence Intervals
This ppt includes basic concepts about data types, levels of measurements. It also explains which descriptive measure, graph and tests should be used for different types of data. A brief of Pivot tables and charts is also included.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. WHAT IS AN ESTIMATOR?
• In statistics, an estimator is a rule for calculating an
estimate of a given quantity based on observed data
• Example-
i. X follows a normal distribution, but we do not know the
parameters of our distribution, namely mean (μ) and
variance (σ2 )
ii. To estimate the unknowns, the usual procedure is to
draw a random sample of size ‘n’ and use the sample
data to estimate parameters.
3. TWO TYPES OF ESTIMATORS
• Point Estimators
A point estimate of a population parameter is a single value of a
statistic.
For example, the sample mean x is a point estimate of the
population mean μ. Similarly, the sample proportion p is a point
estimate of the population proportion P.
• Interval Estimators
An interval estimate is defined by two numbers, between which
a population parameter is said to lie. For example, a < x < b is
an interval estimate of the population mean μ. It indicates that
the population mean is greater than a but less than b.
4. PROPERTIES OF BLUE
• B-BEST
• L-LINEAR
• U-UNBIASED
• E-ESTIMATOR
An estimator is BLUE if the following hold:
1. It is linear (Regression model)
2. It is unbiased
3. It is an efficient estimator(unbiased estimator with least
variance)
5. LINEARITY
• An estimator is said to be a linear estimator of (β) if it is a
linear function of the sample observations
• Sample mean is a linear estimator because it is a linear
function of the X values.
6. UNBIASEDNESS
• A desirable property of a distribution of estimates iS that
its mean equals the true mean of the variables being
estimated
• Formally, an estimator is an unbiased estimator if its
sampling distribution has as its expected value equal to
the true value of population.
• We also write this as follows:
Similarly, if this is not the case, we say that the estimator is
biased
7. • Similarly, if this is not the case, we say that the estimator
is biased
• Bias=E( ) - β
8.
9. MINIMUM VARIANCE
• Just as we wanted the mean of the sampling distribution to be
centered around the true population , so too it is desirable for
the sampling distribution to be as narrow (or precise) as
possible.
– Centering around “the truth” but with high variability might be of
very little use
• One way of narrowing the sampling distribution is to increase
the sampling size
Suppose there is a fixed parameter that needs to be estimated. Then an "estimator" is a function that maps the sample space to a set of sample estimates. An estimator of is usually denoted by the symbol .