This document discusses various time series forecasting methods. It begins by defining time series forecasting as making projections about future performance based on historical and current data. The goals of time series analysis are identified as identifying patterns in observed data and making forecasts. Smoothing techniques are then discussed as a way to remove random noise from time series data to better identify trends and seasonality for forecasting. Several smoothing methods are covered in detail, including simple moving averages, weighted moving averages, simple exponential smoothing, Holt's trend exponential smoothing, and Holt-Winters methods for seasonal data. The components of time series data and advantages/disadvantages of different methods are also summarized.
Introduction to Statistics - Basic Statistical Termssheisirenebkm
Statistics is the study of collecting, organizing, and interpreting numerical data. It has two main branches: descriptive statistics, which summarizes and describes data, and inferential statistics, which is used to analyze samples and make generalizations about populations. The key concepts in statistics include populations, samples, parameters, statistics, qualitative and quantitative data, discrete and continuous variables.
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. The most common method for fitting a regression line is the method of least squares, which minimizes the vertical deviations between observed data points and the fitted line. Outliers and influential observations are data points far from the regression line that can significantly impact the slope and strength of the linear relationship. Residual plots are used to investigate the validity of assuming a linear relationship between variables and to identify potential lurking variables not included in the model.
Statistics can be defined in both a singular and plural sense. In the singular sense, it refers to statistical methods for collecting, analyzing, and interpreting numerical data. In the plural sense, it refers to the actual numerical facts or data collected. Statistics involves systematically collecting, organizing, presenting, analyzing, and interpreting numerical data to describe features and characteristics. It allows for comparing facts, establishing relationships, and facilitating policymaking and decision making. However, statistics only studies aggregates and averages, not individual cases, and results are true only on average. It also requires properly contextualizing and referencing results.
This document provides information about quality management tools and strategies that can be used for quality management in nursing. It discusses six common quality management tools - check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. For each tool, it provides a brief definition and explanation of how it works and how it can be used to measure and manage quality in a healthcare setting. The document also provides additional resources and links for learning more about topics related to quality management systems.
This document discusses several definitions of economics provided by prominent economists over time. It begins by summarizing Adam Smith's definition from 1776 that viewed economics as the science of wealth. It then discusses Alfred Marshall's 1890 definition that considered economics the study of mankind in business. Next, it outlines Lionel Robbins' 1932 definition that defined economics as studying human behavior related to scarce means and alternative uses. Finally, it provides Paul Samuelson's modern definition from 1948 that viewed economics as concerning how society employs its resources. The document then briefly discusses the main divisions of economics as consumption, production, exchange, distribution, and public finance.
This document discusses exponential smoothing techniques for time series forecasting. It introduces simple, double, and triple exponential smoothing. Simple exponential smoothing works for stationary time series, double exponential smoothing adds a trend component for trending time series, and triple exponential smoothing (the Holt-Winters method) further adds seasonal components to handle seasonality. The document discusses parameters, components, extensions, and evaluation metrics for exponential smoothing models.
This document discusses food quality management. It provides an overview of useful resources for food quality management including forms, strategies, and additional materials. It also summarizes a Master's program in food quality management that takes a techno-managerial approach to studying quality processes across the agrifood supply chain. Finally, it outlines several common quality management tools used in food quality control including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and quality management systems.
This document discusses various time series forecasting methods. It begins by defining time series forecasting as making projections about future performance based on historical and current data. The goals of time series analysis are identified as identifying patterns in observed data and making forecasts. Smoothing techniques are then discussed as a way to remove random noise from time series data to better identify trends and seasonality for forecasting. Several smoothing methods are covered in detail, including simple moving averages, weighted moving averages, simple exponential smoothing, Holt's trend exponential smoothing, and Holt-Winters methods for seasonal data. The components of time series data and advantages/disadvantages of different methods are also summarized.
Introduction to Statistics - Basic Statistical Termssheisirenebkm
Statistics is the study of collecting, organizing, and interpreting numerical data. It has two main branches: descriptive statistics, which summarizes and describes data, and inferential statistics, which is used to analyze samples and make generalizations about populations. The key concepts in statistics include populations, samples, parameters, statistics, qualitative and quantitative data, discrete and continuous variables.
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. The most common method for fitting a regression line is the method of least squares, which minimizes the vertical deviations between observed data points and the fitted line. Outliers and influential observations are data points far from the regression line that can significantly impact the slope and strength of the linear relationship. Residual plots are used to investigate the validity of assuming a linear relationship between variables and to identify potential lurking variables not included in the model.
Statistics can be defined in both a singular and plural sense. In the singular sense, it refers to statistical methods for collecting, analyzing, and interpreting numerical data. In the plural sense, it refers to the actual numerical facts or data collected. Statistics involves systematically collecting, organizing, presenting, analyzing, and interpreting numerical data to describe features and characteristics. It allows for comparing facts, establishing relationships, and facilitating policymaking and decision making. However, statistics only studies aggregates and averages, not individual cases, and results are true only on average. It also requires properly contextualizing and referencing results.
This document provides information about quality management tools and strategies that can be used for quality management in nursing. It discusses six common quality management tools - check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. For each tool, it provides a brief definition and explanation of how it works and how it can be used to measure and manage quality in a healthcare setting. The document also provides additional resources and links for learning more about topics related to quality management systems.
This document discusses several definitions of economics provided by prominent economists over time. It begins by summarizing Adam Smith's definition from 1776 that viewed economics as the science of wealth. It then discusses Alfred Marshall's 1890 definition that considered economics the study of mankind in business. Next, it outlines Lionel Robbins' 1932 definition that defined economics as studying human behavior related to scarce means and alternative uses. Finally, it provides Paul Samuelson's modern definition from 1948 that viewed economics as concerning how society employs its resources. The document then briefly discusses the main divisions of economics as consumption, production, exchange, distribution, and public finance.
This document discusses exponential smoothing techniques for time series forecasting. It introduces simple, double, and triple exponential smoothing. Simple exponential smoothing works for stationary time series, double exponential smoothing adds a trend component for trending time series, and triple exponential smoothing (the Holt-Winters method) further adds seasonal components to handle seasonality. The document discusses parameters, components, extensions, and evaluation metrics for exponential smoothing models.
This document discusses food quality management. It provides an overview of useful resources for food quality management including forms, strategies, and additional materials. It also summarizes a Master's program in food quality management that takes a techno-managerial approach to studying quality processes across the agrifood supply chain. Finally, it outlines several common quality management tools used in food quality control including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and quality management systems.
This document discusses home quality management and provides resources on the topic. It outlines tools for home quality management including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. These tools can help nursing homes and other healthcare facilities implement quality management systems to enhance quality, compliance, and organizational efficiencies. The document also lists additional related topics and provides links to downloadable PDFs on quality management systems and other aspects of the subject.
Machine learning is a form of artificial intelligence that allows systems to learn from data and experience without being explicitly programmed. The machine learning process involves finding patterns in data through examples in order to make better decisions. Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems by combining prior beliefs about a population with evidence from new data to guide inferences. Prior probabilities are updated based on new evidence to determine posterior probabilities.
This document discusses quality management tools and strategies for nursing. It provides examples of six commonly used quality management tools: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It also lists additional topics related to quality management in nursing such as quality management systems, courses, techniques, standards, policies and strategies. The document aims to provide useful information and resources for quality management in the nursing field.
This document discusses various forecasting methods including:
- Calculating forecasts using moving averages, weighted moving averages, and exponential smoothing
- Choosing the appropriate forecasting model based on data availability, time horizon, required accuracy, and resources
- Comparing forecast accuracy using metrics like forecast error which measure the difference between actual and forecasted values
This document discusses various time series forecasting techniques in R, including scaling data, checking for stationarity, decomposing time series, and using Holt-Winters exponential smoothing. It provides examples of using the scale(), acf(), decompose(), and hw() functions in R to preprocess time series data and generate forecasts. Key transformations covered include scaling data to the same range, decomposing an air passenger time series into trend, seasonal and error components, and forecasting air passenger data using Holt-Winters exponential smoothing.
The document discusses project management quality and provides resources on the topic. It discusses the role of quality management in project management and examines international perspectives on quality practices. It also outlines several quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. These tools can help identify sources of variation and determine whether processes are statistically in control.
This document provides information about quality management education including forms, tools, and strategies for quality management education. It discusses six common quality management tools - check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Each tool is defined and its purpose and use in quality management is explained in 1-2 sentences. The document is intended to assist those developing and implementing rigorous self-evaluation of education functions through a quality management approach.
This document discusses using the Analytic Hierarchy Process (AHP) and Saaty method to choose the best decision alternative. It provides examples of using AHP to estimate software sizes by comparing to a known example, and prioritize system requirements by comparing costs and benefits. Measurement data can support AHP by providing attributes and sizes of past projects to estimate new projects. AHP is useful for ranking choices based on criteria in a relatively short time and detects inconsistencies in rankings.
This document provides an overview of basic statistics concepts including:
- Statistics is used to determine the difference between chance and real effects by analyzing numerical data
- Averages can be calculated and presented in different ways (mean, median, mode), which can provide different perspectives on the data
- Statistics is applied in many fields to help with planning, assessing programs, and proving or disproving economic theories
- Common statistical tools include variables, matrices, frequency tables, probability calculations
- Limitations include that statistics deals with aggregates and variability rather than individual accuracy, and numbers can be misused out of context
This document discusses different levels of measurement used in research. There are four main levels - nominal, ordinal, interval, and ratio. Nominal measurement involves categorizing items without rank or order. Ordinal measurement ranks items but the distances between ranks are unknown. Interval measurement involves equal distances between ranks. Ratio measurement has a true zero point and allows for proportional comparisons. The key differences between quantitative and qualitative measurement are that quantitative research involves pre-defined variables while qualitative explores concepts during data collection.
Point estimates provide a single value to estimate an unknown population parameter based on sample data, while interval estimates calculate a range of plausible values using confidence intervals. Confidence intervals consist of a confidence level, sample statistic, and margin of error. They indicate the precision and uncertainty of the estimate by defining an interval that is expected to contain the true population parameter a given percentage of the time, such as 90% for a 90% confidence interval. The confidence level describes the likelihood the confidence interval includes the true value, with a higher level indicating more certainty.
This document discusses quality management policy and provides resources on the topic. It includes the contents of a sample quality management policy, which states the company's commitment to quality standards and compliance. It also lists several quality management tools, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Additional related topics on quality management are provided for further reference.
The document provides guidance on summarizing categorical data through frequency tables, bar charts, pie charts, and contingency tables. It emphasizes that data displays should follow the area principle to accurately show distributions and not be misleading. Conditional distributions and marginal distributions from contingency tables allow examining relationships between variables. Common errors include violating the area principle, misleading scales, small sample sizes, and overstating conclusions.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and the textbook and references used. Later sections define important statistical terms like population, sample, variable types, data collection methods, and ways of presenting data through tables and graphs. It provides examples of each variable scale and ends with assignments for students.
This document discusses quality management posters and tools. It provides examples of quality management posters including a quality management tree poster. It also lists and describes six common quality management tools: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Additional links are provided for free quality management resources.
This document provides an introduction to time series forecasting in R. It discusses why forecasting is useful, the types of data that can be forecast, and advantages of using R. It describes common time series classes in R like ts and introduces the forecast package for implementing forecasting models. The document outlines simple forecasting methods like mean, naive, and seasonal naive forecasts. It also discusses evaluating forecast accuracy and error measures. Finally, it presents linear trend models and mentions additional topics like decompositions and exponential smoothing that will be covered in future sessions.
This document provides information about quality management journals and tools. It discusses the purpose of quality management journals in publishing research relevant to quality management practices. It also describes several commonly used quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Links to additional quality management resources are also provided.
This document provides information about ISO 9001 quality management standards. It discusses what ISO 9001 is, how it can help businesses standardize and improve their quality processes, and some common quality management tools used in ISO 9001 implementation like Ishikawa diagrams, histograms, Pareto charts, scatter plots, check sheets, and control charts. The document also includes links to additional ISO 9001 resources and lists related topics like certification, requirements, training, auditing and more.
The document provides information about the syllabus for the Data Analytics (KIT-601) course. It includes 5 units that will be covered: Introduction to Data Analytics, Data Analysis techniques including regression modeling and multivariate analysis, Mining Data Streams, Frequent Itemsets and Clustering, and Frameworks and Visualization. It lists the course outcomes and Bloom's taxonomy levels. It also provides details on the topics to be covered in each unit, including proposed lecture hours, textbooks, and an evaluation scheme. The syllabus aims to discuss concepts of data analytics and apply techniques such as classification, regression, clustering, and frequent pattern mining on data.
This document discusses home quality management and provides resources on the topic. It outlines tools for home quality management including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. These tools can help nursing homes and other healthcare facilities implement quality management systems to enhance quality, compliance, and organizational efficiencies. The document also lists additional related topics and provides links to downloadable PDFs on quality management systems and other aspects of the subject.
Machine learning is a form of artificial intelligence that allows systems to learn from data and experience without being explicitly programmed. The machine learning process involves finding patterns in data through examples in order to make better decisions. Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems by combining prior beliefs about a population with evidence from new data to guide inferences. Prior probabilities are updated based on new evidence to determine posterior probabilities.
This document discusses quality management tools and strategies for nursing. It provides examples of six commonly used quality management tools: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It also lists additional topics related to quality management in nursing such as quality management systems, courses, techniques, standards, policies and strategies. The document aims to provide useful information and resources for quality management in the nursing field.
This document discusses various forecasting methods including:
- Calculating forecasts using moving averages, weighted moving averages, and exponential smoothing
- Choosing the appropriate forecasting model based on data availability, time horizon, required accuracy, and resources
- Comparing forecast accuracy using metrics like forecast error which measure the difference between actual and forecasted values
This document discusses various time series forecasting techniques in R, including scaling data, checking for stationarity, decomposing time series, and using Holt-Winters exponential smoothing. It provides examples of using the scale(), acf(), decompose(), and hw() functions in R to preprocess time series data and generate forecasts. Key transformations covered include scaling data to the same range, decomposing an air passenger time series into trend, seasonal and error components, and forecasting air passenger data using Holt-Winters exponential smoothing.
The document discusses project management quality and provides resources on the topic. It discusses the role of quality management in project management and examines international perspectives on quality practices. It also outlines several quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. These tools can help identify sources of variation and determine whether processes are statistically in control.
This document provides information about quality management education including forms, tools, and strategies for quality management education. It discusses six common quality management tools - check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Each tool is defined and its purpose and use in quality management is explained in 1-2 sentences. The document is intended to assist those developing and implementing rigorous self-evaluation of education functions through a quality management approach.
This document discusses using the Analytic Hierarchy Process (AHP) and Saaty method to choose the best decision alternative. It provides examples of using AHP to estimate software sizes by comparing to a known example, and prioritize system requirements by comparing costs and benefits. Measurement data can support AHP by providing attributes and sizes of past projects to estimate new projects. AHP is useful for ranking choices based on criteria in a relatively short time and detects inconsistencies in rankings.
This document provides an overview of basic statistics concepts including:
- Statistics is used to determine the difference between chance and real effects by analyzing numerical data
- Averages can be calculated and presented in different ways (mean, median, mode), which can provide different perspectives on the data
- Statistics is applied in many fields to help with planning, assessing programs, and proving or disproving economic theories
- Common statistical tools include variables, matrices, frequency tables, probability calculations
- Limitations include that statistics deals with aggregates and variability rather than individual accuracy, and numbers can be misused out of context
This document discusses different levels of measurement used in research. There are four main levels - nominal, ordinal, interval, and ratio. Nominal measurement involves categorizing items without rank or order. Ordinal measurement ranks items but the distances between ranks are unknown. Interval measurement involves equal distances between ranks. Ratio measurement has a true zero point and allows for proportional comparisons. The key differences between quantitative and qualitative measurement are that quantitative research involves pre-defined variables while qualitative explores concepts during data collection.
Point estimates provide a single value to estimate an unknown population parameter based on sample data, while interval estimates calculate a range of plausible values using confidence intervals. Confidence intervals consist of a confidence level, sample statistic, and margin of error. They indicate the precision and uncertainty of the estimate by defining an interval that is expected to contain the true population parameter a given percentage of the time, such as 90% for a 90% confidence interval. The confidence level describes the likelihood the confidence interval includes the true value, with a higher level indicating more certainty.
This document discusses quality management policy and provides resources on the topic. It includes the contents of a sample quality management policy, which states the company's commitment to quality standards and compliance. It also lists several quality management tools, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Additional related topics on quality management are provided for further reference.
The document provides guidance on summarizing categorical data through frequency tables, bar charts, pie charts, and contingency tables. It emphasizes that data displays should follow the area principle to accurately show distributions and not be misleading. Conditional distributions and marginal distributions from contingency tables allow examining relationships between variables. Common errors include violating the area principle, misleading scales, small sample sizes, and overstating conclusions.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and the textbook and references used. Later sections define important statistical terms like population, sample, variable types, data collection methods, and ways of presenting data through tables and graphs. It provides examples of each variable scale and ends with assignments for students.
This document discusses quality management posters and tools. It provides examples of quality management posters including a quality management tree poster. It also lists and describes six common quality management tools: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Additional links are provided for free quality management resources.
This document provides an introduction to time series forecasting in R. It discusses why forecasting is useful, the types of data that can be forecast, and advantages of using R. It describes common time series classes in R like ts and introduces the forecast package for implementing forecasting models. The document outlines simple forecasting methods like mean, naive, and seasonal naive forecasts. It also discusses evaluating forecast accuracy and error measures. Finally, it presents linear trend models and mentions additional topics like decompositions and exponential smoothing that will be covered in future sessions.
This document provides information about quality management journals and tools. It discusses the purpose of quality management journals in publishing research relevant to quality management practices. It also describes several commonly used quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Links to additional quality management resources are also provided.
This document provides information about ISO 9001 quality management standards. It discusses what ISO 9001 is, how it can help businesses standardize and improve their quality processes, and some common quality management tools used in ISO 9001 implementation like Ishikawa diagrams, histograms, Pareto charts, scatter plots, check sheets, and control charts. The document also includes links to additional ISO 9001 resources and lists related topics like certification, requirements, training, auditing and more.
The document provides information about the syllabus for the Data Analytics (KIT-601) course. It includes 5 units that will be covered: Introduction to Data Analytics, Data Analysis techniques including regression modeling and multivariate analysis, Mining Data Streams, Frequent Itemsets and Clustering, and Frameworks and Visualization. It lists the course outcomes and Bloom's taxonomy levels. It also provides details on the topics to be covered in each unit, including proposed lecture hours, textbooks, and an evaluation scheme. The syllabus aims to discuss concepts of data analytics and apply techniques such as classification, regression, clustering, and frequent pattern mining on data.
This document discusses different types of data and how to classify them. It defines attributes data as focusing on specific non-numerical characteristics of a population, while variables data measures characteristics on a continuous scale. Attributes data is counted as discrete events, while variables data derives a numeric estimate. The document also discusses distributional models and their relationship to different types of charts that can be used for attributes data analysis.
Assessing relative importance using rsp scoring to generateDaniel Koh
This document proposes a new method called Driver's Score (DS) to assess the relative importance of variables in regression models. DS combines measures of a variable's reliability, significance, and power into a single composite score. Reliability is measured using residual errors, significance uses F-ratios of residual errors, and power uses standardized regression coefficients. DS is calculated at the observation level as the geometric mean of these three scores. The document argues DS provides a more intuitive and practical understanding of variable importance than existing methods. An example using industry data demonstrates how to generate DS scores and classify variables by level of importance. The methodology aims to independently measure importance while accounting for interrelationships between variables.
Assessing Relative Importance using RSP Scoring to Generate VIFDaniel Koh
This document proposes a new method called Driver's Score (DS) to assess the relative importance of variables in regression models. DS combines measures of a variable's reliability, significance, and power into a single composite score. Reliability is measured using residual errors, significance uses F-ratios of residual errors, and power uses standardized regression coefficients. DS is calculated at the observation level as the geometric mean of scores for each of these three properties. The document suggests that DS provides a more intuitive and practical understanding of variable importance than existing single-measure methods. An example using industry data demonstrates how to generate DS scores and classify variables by level of importance.
Data Science - Part IV - Regression Analysis & ANOVADerek Kane
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Storm Prediction data analysis using R/SASGautam Sawant
• Performed data cleaning and analysis using R, SAS to predict financial loss caused due to storms also predict when a storm will occur depending upon previous storm data
• Implemented algorithms like Logistic Regression, Multiple Regression, Linear Discriminant Analysis, PCA to obtain insights from the Storm Dataset from 1950-2007
Linear regression is a statistical method used to model the relationship between a scalar dependent variable and one or more explanatory variables. The document discusses linear regression in R, including simple linear regression with one explanatory variable and multiple linear regression with two or more explanatory variables. It also covers evaluating linear regression models using measures like residual standard error, R-squared, and p-values. The document provides an example of modeling bond prices with coupon rates and advertising sales data with multiple advertising expenditures.
The document describes a project applying machine learning techniques to forecast bike rental demand using the Capital Bikeshare program in Washington D.C. Multiple techniques are evaluated including linear regression, lasso regression, elastic net, ensemble learning, neural networks and local linear regression. Ensemble learning with regularized bagging had the best performance with a root mean squared logarithmic error of 0.63302 on validation data. Further tuning of methods and additional analysis of features could potentially improve predictions.
Feature extraction and selection are important techniques in machine learning. Feature extraction transforms raw data into meaningful features that better represent the data. This reduces dimensionality and complexity. Good features are unique to an object and prevalent across many data samples. Principal component analysis is an important dimensionality reduction technique that transforms correlated features into linearly uncorrelated principal components. This both reduces dimensionality and preserves information.
This document summarizes a study that used logistic regression to predict the probability of a second date between speed dating participants. It used variables like age, attractiveness ratings, and shared interests to build a model. The best model used only shared interests rated by the male and female as predictors. A threshold of 48% probability maximized sensitivity of predicting positive matches at 89%, though overall accuracy was only 67%. While not perfect, the model provides a reasonable way to forecast speed dating success based on participant ratings.
The document discusses different types of mathematical models, including deterministic and probabilistic models. It provides examples of each. It also discusses building, verifying, and refining mathematical models. Additionally, it covers optimization models, their components including objective functions and constraints. Finally, it discusses specific types of optimization models like linear programming, network flow programming, and integer programming.
Statistics for Managers pptS for better understandingShamshadAli58
This document outlines the topics to be covered in a statistics course for managers. It includes 5 units: introduction to statistics, measures of central tendency and dispersion, tabulation, small sample tests, and correlation, regression, and time series analysis. The course aims to help managers make effective use of data analysis in business decision making. It will teach statistical and graphical techniques to organize and understand sample data. Students will learn to select the appropriate statistical method for different data analysis needs and build models for business applications.
Machine Learning statistical model using Transportation datajagan477830
As the world is growing rapidly the people and the vehicles we use to move from one place to another, so the transportation is playing a vital role in making human lives easiest to travel from one place to another, everyday more and more vehicles are being produced and being bought by the people around the world, be it Electric, Hydrogen, petrol, diesel or solar powered.
This document compares four recent boundary detection methods: Boundary Detection with Sketch Tokens, Crisp Boundary Detection Using Pointwise Mutual Information, Oriented Edge Forests for Boundary Detection, and Fast Edge Detection Using Structured Forests. It evaluates these methods on the Berkeley Segmentation Dataset using precision-recall metrics to determine which method achieves the best balance of high precision and high recall. It also considers the time complexity and simplicity of each method to help researchers select the most appropriate approach for their needs.
The document discusses the importance of data quality, proper use of statistics, and correct interpretation of results in statistical analysis. It provides a 3 step approach: 1) Ensuring high quality data by addressing issues like missing values and outliers. 2) Appropriate use of statistical techniques after defining the variables and objectives clearly. Considering issues like correlation, normality, and model assumptions. 3) Careful interpretation of results while preserving the multidimensional nature of phenomena and considering partial correlations between variables. It emphasizes the need for collaboration between data miners, statisticians and domain experts for successful knowledge discovery.
This document provides a guide for building generalized linear models (GLMs) in R to accurately model insurance claim frequency and severity. It outlines steps for data preparation, including handling missing data, transforming variables, and removing outliers. It then discusses modeling count/frequency data with Poisson or negative binomial models including an offset for exposure. Severity is typically modeled with a gamma or normal distribution. The document provides examples of investigating interactions and comparing models using AIC, BIC, and residual analysis.
This document provides an overview of machine learning concepts including feature selection, dimensionality reduction techniques like principal component analysis and singular value decomposition, feature encoding, normalization and scaling, dataset construction, feature engineering, data exploration, machine learning types and categories, model selection criteria, popular Python libraries, tuning techniques like cross-validation and hyperparameters, and performance analysis metrics like confusion matrix, accuracy, F1 score, ROC curve, and bias-variance tradeoff.
[KAIST DFMP CBA] Analyze price determinants and forecast Seoul apartment pric...경록 박
Analyzed price determinants and forecasted Seoul apartment prices with correlations, regressions (linear, decision tree, random forest, XGB), and time series models (Auto ARIMA, Holt-Winters) using Samsung Brightics Studio.
Similar to Taras Firman "How to build advanced prediction with adding external data." (20)
Sergiy Lunyakin "Cloud BI with Azure Analysis Services"DataConf
This document provides an overview of using Azure Analysis Services for cloud business intelligence (BI). It discusses the key components of Azure that work with Analysis Services, including Data Factory, SQL Database, SQL Data Warehouse, and Power BI. It also covers the architecture and performance levels of Analysis Services in Azure, how to connect various data sources, and tools for management, development, and troubleshooting. The document demonstrates how Analysis Services provides a fully managed tabular model engine in the cloud for enterprise-grade data modeling and analytics.
Sergiy Lunyakin "Azure SQL DWH: Tips and Tricks for developers"DataConf
This document provides tips and tricks for working with Azure SQL Data Warehouse (SQL DW). It discusses scaling options like scaling up vs. scaling out. It describes the architecture of SQL DW including its distributed database structure. It covers sizing factors, distributions like round robin and hash, and loading data in parallel. The document also discusses limitations of SQL DW like a lack of support for primary keys, triggers, cross-database joins, and cursors. Workarounds are provided for limitations using techniques like CTAS and row numbering.
Oles Petriv "Semantic image segmentation using word embeddings."DataConf
Semantic image segmentation uses word embeddings to provide context from images. Convolutional neural networks are used to extract visual features from images at different levels, from low level to higher semantic levels. Models like SegNet and Pyramid Pooling are used for segmentation. Word embeddings represent words as vectors in a continuous concept space, where cosine distance reflects semantic similarity. Image classes are mapped to word embeddings to provide semantic context during training, with the loss measuring cosine distance between predicted and true word vectors for each image region. This allows vocabulary-free semantic segmentation based on conceptual relationships between words and image regions.
Vitalii Bashun "First Spark application in one hour"DataConf
The document provides an introduction to creating a first Spark application in one hour. It begins with an overview of Hadoop and why Spark became an industry standard due to its ability to keep intermediate data in memory for faster processing. The key concepts covered are Spark Session, which acts as the entry point for Spark programming, and Resilient Distributed Datasets (RDDs), DataFrames, and Datasets, which are the main abstractions Spark uses for distributed data. The document concludes by stating it will demonstrate creating a hands-on first Spark application using the Spark Shell.
Vitalii Bondarenko "Machine Learning on Fast Data"DataConf
This document discusses machine learning on fast data. It presents an agenda covering ML on production systems, TensorFlow, Kafka, Docker and Kubernetes. It then describes the machine learning process and shows how an enterprise analytics platform can integrate data sources, a machine learning cluster using Kafka, and data destinations. Details are provided on using TensorFlow for linear regression and neural networks. Apache Kafka is explained as a distributed streaming platform using topics, brokers, and consumer groups. The Confluent platform, KStream and KTable APIs are also summarized. Docker and Kubernetes are mentioned for containerization.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
2. M:
Advantages – Fast to compute, easier to model, easier to identify changes in trends, better for strategic long term forecasting.
Disadvantages – If you need to plan as the daily level for capacity, people and spoilage of product then higher levels of forecasting
won’t help understand the demand on a daily basis as a 1/30th ratio estimate is clearly insufficient.
W:
Advantages – When you can’t handle the modeling process at a daily level you “settle” for this. When you have very systematic cyclical
cycles like “articice extents” that follow a rigid curve and not need for day of the week variations.
Disadvantages – Floating Holidays like Thanksgiving, Easter, Ramadan, Chinese New Year change every year and disrupt the estimate for
the coefficients for the week of the year impact which can be handled by creating a variable for each.
D:
Advantages – Weekly data can’t deal with holidays and their lead/lag relationships. If a holiday has days 1,2,3 before the holiday as very
large volume a daily model can forecast that while the weekly won’t be able year in and year out model and forecast that impact as the
day of the week that the holiday occurs changes every year.
Disadvantages – Slower to process, but this can be mitigated by reusing models.
Monthly VS weekly VS daily
4. Forecasting’s short history
● Generic models
(Moving Average Process MA(q), Exp Smoothing, Autoregressive Process AR(p), Autoregressive
Moving Average ARMA(p, q), Autoregressive Integrated Moving Average ARIMA (p, d, q))
● State Space models and Kalman Filter
● Multivariate vector models
● Feature extraction & ML
● DL approaches
(LSTM Recurrent Neural Networks)
8. Trend aproximation
The approximation of the trend can be found from the formula below
where Pn
(t) is a degree polynomial and Ak
is a set of indexes, including the first k indexes
with highest amplitudes.
9. Seasonality VS Cycles
Canadian lynx data
Aperiodic population cycles of approximately 10 years
Monthly sales of new one-family houses sold in USA
Strong seasonality within each year and strong cycles with
period 6-10 years
Half-hourly electricity demand in England
Multi-seasonality with daily and weekly patterns
13. Correlation types
● Pearson correlation is statistic to measure the degree of the relationship between
linearly related variables.
Assumptions: both variables should be normally distributed and have linearity
and homoscedasticity relationship (normally distributed about the regression line)
● Spearman rank correlation is non-parametric test that is used to measure the degree
of association between two variables.
Assumptions: it doesn’t make any assumptions about the distribution.
● Kendall tau is a statistic used to measure the ordinal association between two
measured quantities.
Assumptions: data must be at least ordinal and scores on one variable must
be montonically related to the other variable.
where
where s1
/s2
is number of
concordant/discordant pairs
14. How to work with a short history?
Stochastic Simulation (Monte-Carlo)
Predicting the Past and Predicting
the Future
15. Error measuring
is an accuracy measure based on percentage (or relative) errors. One
supposed problem with SMAPE is that it is not symmetric since over- and
under-forecasts are not treated equally.
is scale-dependent
is scale-dependent
is the computed average of percentage errors. The formula can be
used as a measure of the bias in the forecasts
usually expresses accuracy as a percentage. It puts a heavier penalty
on negative errors, than on positive errors.
16. Robustness. Model selection
If n/k < 40:
where
- the set of model parameters;
- the likelihood of the candidate model given the data;
- the number of estimated parameters in the candidate model;
- the number of observations.
19. Inspired by Technology.
Driven by Value.
Find us at eleks.com
Have a question? Write to eleksinfo@eleks.com
Taras Firman
email: taras.firman@eleks.com
skype: tarasinho_318
AI&BigData 2017
4 November, Lviv