This chapter discusses time-series analysis and forecasting. It covers computing and interpreting index numbers, testing for randomness in time series data, and identifying trend, seasonality, cyclical, and irregular components. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average (ARIMA) models. The chapter aims to help readers compute and interpret index numbers, test for randomness, and use various forecasting techniques.
Chap19 time series-analysis_and_forecastingVishal Kukreja
Trend + Seasonality + Cyclical + Irregular
Multiplicative Model
X t = Trend × Seasonality × Cyclical × Irregular
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness, and identifying trend, seasonality, cyclical and irregular components in a time series. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The chapter aims to help readers analyze time-series data and develop forecasts.
This document provides an overview of time-series analysis and forecasting techniques. It discusses computing and interpreting index numbers, testing for randomness in time series, and identifying trend, seasonality, cyclical, and irregular components. It also covers smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The goal is for readers to be able to decompose time series data into its underlying components and apply appropriate forecasting methods.
This document provides an introduction to statistics for small business administration (SBA) students. It outlines key topics in statistics theory and uses of statistics in business contexts. Examples of business data sources are also listed, such as Statistics Canada, Industry Canada, and CPA BC salary surveys, which provide income, education, and compensation data useful for SBA students. Exercises are included to help students apply statistical concepts.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
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This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness in time series, identifying trend, seasonality, and irregular components, and forecasting models including moving averages and exponential smoothing. The goals are to compute price and quantity indexes, use runs tests to detect non-randomness, decompose time series into components, and apply smoothing and autoregressive forecasting models.
Chap19 time series-analysis_and_forecastingVishal Kukreja
Trend + Seasonality + Cyclical + Irregular
Multiplicative Model
X t = Trend × Seasonality × Cyclical × Irregular
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness, and identifying trend, seasonality, cyclical and irregular components in a time series. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The chapter aims to help readers analyze time-series data and develop forecasts.
This document provides an overview of time-series analysis and forecasting techniques. It discusses computing and interpreting index numbers, testing for randomness in time series, and identifying trend, seasonality, cyclical, and irregular components. It also covers smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The goal is for readers to be able to decompose time series data into its underlying components and apply appropriate forecasting methods.
This document provides an introduction to statistics for small business administration (SBA) students. It outlines key topics in statistics theory and uses of statistics in business contexts. Examples of business data sources are also listed, such as Statistics Canada, Industry Canada, and CPA BC salary surveys, which provide income, education, and compensation data useful for SBA students. Exercises are included to help students apply statistical concepts.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
The document summarizes key points about multiple regression analysis from the chapter. It discusses applying multiple regression to business problems, interpreting regression output, performing residual analysis, and testing significance. Graphs and equations are provided to illustrate multiple regression concepts like predicting outcomes, determining variation explained, and checking assumptions.
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This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness in time series, identifying trend, seasonality, and irregular components, and forecasting models including moving averages and exponential smoothing. The goals are to compute price and quantity indexes, use runs tests to detect non-randomness, decompose time series into components, and apply smoothing and autoregressive forecasting models.
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This document provides an overview of index numbers. It defines index numbers as quantitative measures of changes in variables like prices, production, or inventory over time. The document outlines different types of index numbers like simple aggregative, simple average of relatives, weighted index numbers using methods like Laspeyres, Paasche, Fisher's ideal. It also discusses value index numbers, chain index numbers, and provides examples of calculating different types of index numbers.
This document discusses index numbers, which are statistical tools used to measure relative changes in variables such as prices or quantities over time. It defines index numbers and outlines their key features and types, including price, quantity, value, simple and composite index numbers. The document also describes several methods for constructing index numbers, such as Laspeyre's method, Paasche's method, Fisher's ideal method and consumer price indexes. Index numbers are expressed as percentages and measure the effect of changes over periods of time.
This document discusses index numbers, which are statistical tools used to measure relative changes in variables such as prices or quantities over time. It defines index numbers and outlines their key features and types, including price, quantity, value, simple and composite index numbers. The document also describes several methods for constructing index numbers, such as Laspeyre's method, Paasche's method, Fisher's ideal method and consumer price indexes. Index numbers are expressed as percentages and measure the effect of changes over periods of time.
This chapter discusses nonparametric statistical tests that make fewer assumptions about the underlying data distribution, including the sign test, Wilcoxon signed-rank test, and Mann-Whitney U-test. It provides examples of how to conduct each test and interpret the results. The sign test can test for differences in paired samples or a single population median. The Wilcoxon signed-rank test incorporates information about the magnitude of differences between paired samples. The Mann-Whitney U-test compares two independent samples and tests whether their population medians are the same.
This chapter discusses techniques for time-series forecasting and index numbers. It begins by explaining the importance of forecasting for governments, businesses and other organizations. It then outlines common qualitative and quantitative forecasting approaches, with a focus on time-series methods that use historical data patterns to predict future values. The chapter describes how to decompose a time series into trend, seasonal, cyclical and irregular components. It also explains techniques for smoothing time-series data, including moving averages and exponential smoothing. Finally, it covers methods for time-series forecasting based on trend lines, including linear, quadratic, exponential and other models.
This document discusses various types of index numbers used to measure changes in economic variables over time. It defines index numbers and their key characteristics. It then describes different methods for constructing index numbers, including simple aggregative methods, weighted aggregative methods like Laspeyres, Paasche, Fisher's ideal, and chain index numbers. Examples are provided to demonstrate how to calculate index numbers using these various methods based on price and production data for different items.
This document discusses index numbers, which are statistical values that measure changes in variables like price or quantity over time. It covers simple index numbers calculated for a single item, composite index numbers for multiple items, and weighted index numbers that account for quantities. The most commonly used index in Australia is the Consumer Price Index (CPI), which measures price changes in a fixed basket of consumer goods and services to indicate inflation and cost of living changes. The document provides formulas and explanations for simple, composite, weighted (Laspeyres, Paasche), and Fisher's ideal indexes.
This document discusses various measures of central tendency including arithmetic mean, median, mode, and quartiles. It provides definitions and formulas for calculating each measure, and describes how to calculate the mean and median for different types of data distributions including raw data, continuous series, and less than/more than/inclusive series. It also covers weighted mean, combined mean, and properties and limitations of the arithmetic mean.
There are different types of index numbers that can be used to measure how a variable changes over time. Weighted index numbers are more accurate than simple index numbers because they assign greater importance to items that see larger changes in price or quantity. Common types of weighted index numbers include Laspeyres, Paasche, and Fisher indexes. Calculating accurate index numbers can be challenging due to issues like incompatible data, inappropriate weighting factors, and improperly selected base years.
This document provides an introduction to statistical quality control and process improvement. It discusses the importance of quality and reducing variation in processes. There are two main sources of variation - common causes that are inherent in any process, and assignable causes that can be identified and addressed. Control charts are used to monitor processes over time and determine whether data points indicate common or assignable causes of variation. The chapter goals are to describe quality control, sources of variation, control charts including X-charts for the mean and S-charts for standard deviation, and measures of process capability.
This chapter discusses quality control and statistical process monitoring using control charts. The goals are to reduce process variation and ensure the process is stable with only common cause variation. Control charts like X-charts and s-charts are used to monitor process metrics like the mean and standard deviation over time. Data is collected from subgroups and control limits are set at 3 standard deviations from the center line. Points outside the limits may indicate an assignable cause of variation that needs correction to keep the process in statistical control.
This document discusses index numbers and various methods for constructing them. It defines an index number as a measure of relative change from one period to another. There are different types of index numbers including simple and weighted indexes. Weighted indexes assign weights to items based on factors like quantities. Various methods for constructing weighted indexes are described, such as Laspeyre's method, Paasche's method, and Fisher's ideal method. The document also discusses tests like the time reversal test and factor reversal test to evaluate appropriate index number formulas.
Index numbers measure relative changes in price, quantity, or other economic variables over time. They allow comparisons between different time periods. There are several methods for constructing index numbers, including simple aggregative methods, weighted index methods like Laspeyres and Paasche, and chain index numbers. Index numbers have many uses, such as measuring inflation, setting wages, analyzing industries and economic conditions, and making international comparisons. Care must be taken in choosing the appropriate base period, commodities, and method of calculation for the specific application.
Forecasting Academic Performance using Multiple Linear Regressionijtsrd
This document discusses using multiple linear regression to forecast academic performance based on intelligence quotient (IQ) and study hours. The authors collected test score, IQ, and study hour data for 10 students and used the Statistical Package for Social Sciences (SPSS) to analyze the data. They found that IQ and study hours significantly predicted test scores, with IQ and study hours explaining 91% of the variance in test scores. For every one unit increase in IQ, test scores increased by 0.509 units on average, and for every one unit increase in study hours, test scores increased by 0.467 units on average. The authors conclude that regression is a useful statistical method for educational research and that this analysis can help students and teachers improve academic performance
Applied Statistics Chapter 3 Index numbers (1).pptVidhiAgarwal89
This document provides an overview of index numbers and includes the following key points:
- Price relatives and aggregate price indexes are used to measure changes in prices over time by comparing prices in different periods to a base period. Weighted aggregate price indexes use quantities to weight the prices.
- Important price indexes include the Consumer Price Index (CPI) and Producer Price Index (PPI) which measure inflation and production costs.
- Time series data expressed in dollar values can be adjusted for inflation by deflating the values using a price index to obtain real or constant dollar values.
- Selection of items and base periods for price indexes as well as accounting for quality changes are important considerations. Quantity indexes also exist to measure changes
The document discusses estimating demand using regression analysis. It involves 4 steps:
1. Developing a theoretical demand model specifying the dependent and independent variables.
2. Collecting data on the variables.
3. Choosing a functional form, typically linear or logarithmic, to estimate the regression equation.
4. Estimating the coefficients using least squares regression, interpreting the results, and testing if the independent variables are statistically significant predictors of demand.
The document provides examples of calculating different types of weighted index numbers to measure changes in prices over time. It discusses weighted aggregative price index numbers, which calculate total expenditures for a current and base year using item weights. It also describes weighted average of relative price index numbers. Specific methods covered include Laspeyre's index, Paasche's index, Fisher's ideal index, and Marshall-Edgeworth price index. Formulas and examples are given for calculating each type of index number from sample price and quantity data for multiple time periods.
This document discusses index numbers and how they are used to measure inflation or deflation. It provides the formula for a simple price index and examples of common indices including the Retail Price Index. Weighted aggregate indices weight items by importance and can use either base year quantities (Laspeyres index) or current year quantities (Paasche index). Deflating a time series allows adjustment for inflation by dividing current values by an index to express them in base year prices. Graphs and comparisons of indices are used to examine changes over time.
This document discusses time series analysis and forecasting methods. It covers descriptive analysis techniques like index numbers and exponential smoothing to characterize patterns in time series data. It also covers inferential/forecasting methods like exponential smoothing, Holt's method, and regression models to predict future values in a time series. The learning objectives are to analyze time series data generated over time, present descriptive characterization methods, and present forecasting methods. Key concepts discussed include index numbers, exponential smoothing, time series components, measuring forecast accuracy, and autocorrelation.
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This document provides an overview of index numbers. It defines index numbers as quantitative measures of changes in variables like prices, production, or inventory over time. The document outlines different types of index numbers like simple aggregative, simple average of relatives, weighted index numbers using methods like Laspeyres, Paasche, Fisher's ideal. It also discusses value index numbers, chain index numbers, and provides examples of calculating different types of index numbers.
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This document provides an overview of index numbers and includes the following key points:
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- Important price indexes include the Consumer Price Index (CPI) and Producer Price Index (PPI) which measure inflation and production costs.
- Time series data expressed in dollar values can be adjusted for inflation by deflating the values using a price index to obtain real or constant dollar values.
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4. Estimating the coefficients using least squares regression, interpreting the results, and testing if the independent variables are statistically significant predictors of demand.
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👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.