Qt presentation
•Download as PPTX, PDF•
1 like•402 views
This document discusses various forecasting techniques including: naive forecasting models, smoothing techniques like moving averages, decomposition techniques, exponential smoothing models like Holt's two-parameter method, and Winter's three parameter exponential smoothing model. It also discusses error measurement techniques, time series components, autocorrelation, autoregression, and index number techniques like simple index numbers, weighted and unweighted aggregate price indexes, and the Laspeyres and Paasche price indexes.
Report
Share
Report
Share
Recommended
1.4 notes
Scientists use various visual formats like tables, line graphs, bar graphs, and circle graphs to organize and present data. Tables relate variables and display values in rows and columns. Line graphs show changes over time or between variables with slope indicating relationships. Bar graphs compare measurements, while circle graphs illustrate proportions of a whole.
FinalThesis_AnasRadouani
This thesis aims to predict the price of Saipem's shares using an autoregressive lag distributed (ADL) model with crude oil prices and the Italian stock market index as explanatory variables. The author estimates an ADL model using weekly data from 2003 to 2015 and finds the price of crude oil and the stock market index to be statistically significant predictors of Saipem's share price. Out-of-sample forecasts are generated and evaluated using error metrics like MAPE, MAE, and MSE, demonstrating the model's ability to accurately predict future prices.
Hrug intro to forecasting
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.
Intro To Forecasting - Part 2 - HRUG
This document discusses forecasting techniques in R, including linear trend models, transformations of data, and dummy variables. It provides examples of how to improve forecast accuracy by taking a log transformation of air passenger data to account for growth trends. It also discusses using dummy variables to model seasonal effects like holidays, and provides an example of creating a dummy variable for the Easter holiday to forecast sales of Cadbury eggs.
Weighted index numbers
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.
Quantitative Aptitude , Section-Arithmetic , Chapter-Percentages
This document provides information on calculating percentages of numbers and dealing with percentage problems. It discusses:
1) How to calculate a percentage of a number by replacing the % with a decimal and multiplying. For example, 225% of 24 is 2.25 x 24 = 54.
2) A trick for breaking percentages into 10% increments, like calculating 60% of 42 as (50% of 42) + (10% of 42).
3) Common mistakes made when dealing with percentages that are increased or decreased by a certain amount, like confusing "increased by 200%" versus "increased to 200%".
4) How to calculate the percentage one number is more or less than another, and examples of
Index Numbers
This document discusses various types of index numbers used to measure changes in economic variables over time. It defines an index number as a quantitative measure of the growth of prices, production, or other quantities of economic interest from one period to another. The document then describes different characteristics, uses, problems, classifications, and methods for constructing index numbers, including simple aggregative methods, weighted aggregative methods like Laspeyres and Paasche, and chain index numbers. It provides examples to illustrate how to calculate different index numbers.
Chapter 20
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.
Recommended
1.4 notes
Scientists use various visual formats like tables, line graphs, bar graphs, and circle graphs to organize and present data. Tables relate variables and display values in rows and columns. Line graphs show changes over time or between variables with slope indicating relationships. Bar graphs compare measurements, while circle graphs illustrate proportions of a whole.
FinalThesis_AnasRadouani
This thesis aims to predict the price of Saipem's shares using an autoregressive lag distributed (ADL) model with crude oil prices and the Italian stock market index as explanatory variables. The author estimates an ADL model using weekly data from 2003 to 2015 and finds the price of crude oil and the stock market index to be statistically significant predictors of Saipem's share price. Out-of-sample forecasts are generated and evaluated using error metrics like MAPE, MAE, and MSE, demonstrating the model's ability to accurately predict future prices.
Hrug intro to forecasting
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.
Intro To Forecasting - Part 2 - HRUG
This document discusses forecasting techniques in R, including linear trend models, transformations of data, and dummy variables. It provides examples of how to improve forecast accuracy by taking a log transformation of air passenger data to account for growth trends. It also discusses using dummy variables to model seasonal effects like holidays, and provides an example of creating a dummy variable for the Easter holiday to forecast sales of Cadbury eggs.
Weighted index numbers
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.
Quantitative Aptitude , Section-Arithmetic , Chapter-Percentages
This document provides information on calculating percentages of numbers and dealing with percentage problems. It discusses:
1) How to calculate a percentage of a number by replacing the % with a decimal and multiplying. For example, 225% of 24 is 2.25 x 24 = 54.
2) A trick for breaking percentages into 10% increments, like calculating 60% of 42 as (50% of 42) + (10% of 42).
3) Common mistakes made when dealing with percentages that are increased or decreased by a certain amount, like confusing "increased by 200%" versus "increased to 200%".
4) How to calculate the percentage one number is more or less than another, and examples of
Index Numbers
This document discusses various types of index numbers used to measure changes in economic variables over time. It defines an index number as a quantitative measure of the growth of prices, production, or other quantities of economic interest from one period to another. The document then describes different characteristics, uses, problems, classifications, and methods for constructing index numbers, including simple aggregative methods, weighted aggregative methods like Laspeyres and Paasche, and chain index numbers. It provides examples to illustrate how to calculate different index numbers.
Chapter 20
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.
Intro to Forecasting - Part 3 - HRUG
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.
Index numbers99
Index numbers are used to measure and compare changes in prices, quantities, and values over time. There are several types of index numbers including simple price indexes, quantity indexes, value indexes, and aggregate price indexes. Aggregate price indexes include unweighted, Laspeyres, Paasche, and fixed-weight indexes. The Consumer Price Index is a widely used fixed-weight aggregate price index published monthly by the Bureau of Labor Statistics to measure inflation experienced by urban consumers.
Presenting scientific data graphing
This document discusses different types of graphs used to present scientific data:
- Line graphs are used to show changes in related variables over time or against each other, and can show direct or inverse proportions.
- Bar graphs compare counted data by displaying items on the x-axis and their quantities on the y-axis.
- Circle graphs or pie charts show how a fixed quantity is divided into parts.
The document provides examples of different graphs and how to interpret the relationships between variables displayed in each type of graph.
Time series Forecasting
The document summarizes time series analysis conducted to forecast sales for an airline company over the next 12 months. Key steps included: 1) checking for volatility, non-stationarity and seasonality in the data; 2) creating training and test datasets; 3) building ARIMA models and selecting the best based on error metrics; 4) generating forecasts and calculating errors compared to actual data. The optimal model with AR=0 and MA=3 was chosen for final forecasting based on lowest MAPE.
Index number
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.
Index Number (Business Statistics Tutorial )
Business Statistics Tutorial
Mahmudul Hasan Nakib
Founder President (Inventive Point)
BBA , MBA (Finance), AUB, SEU
CMA, ICMAB(Studying)
Index numbers
This document discusses index numbers and basic statistics. It begins by defining index numbers as devices that measure changes in variables over time or space. It notes that index numbers are specialized averages. The document then discusses different types of index numbers, methods for constructing them, advantages and disadvantages, and problems related to index numbers. It provides examples of calculating Fisher's ideal index number. In the end, it lists some required readings and resources for further learning about index numbers and basic statistics.
Chart
A pie chart is a circular graph that is divided into sectors or slices to illustrate numerical proportion. Each slice's central angle represents the quantity's percentage of the whole. To create a pie chart, you calculate the percentage of each category by dividing its value by the total value. You then multiply the decimal percentage by 360 degrees to determine each slice's central angle. A pie chart visually compares the proportional relationship of parts to a whole.
Elasticity 2
This document discusses measuring income elasticity at a point on an Engel curve. It explains that income elasticity is measured similarly to supply elasticity by calculating the percentage change in quantity demanded divided by the percentage change in income between two points on the Engel curve. The document also briefly mentions applications of elasticity coefficients to economic policy and business decisions.
Weighted index number - business statistics
Special For Jaipur national university Student || BBA || Weighted Index Number || Business Statics || Presentation
Class 11 Economics -Introduction to index numbers
INDEX NUMBERS
Economic activities have constant tendency to change. Prices of commodities which arc the total result of number of economic activities also have a tendency to fluctuate. The problem of change in prices is very important. But it is not very simple to study this problem and derive conclusions because price of different commodities change by different degrees. Hence, there is a great need for a device which can smoothen the irregularities in the prices to obtain a conclusion.
Our institute AMEND EDUCATION ACADEMY works in the direction to educate the students in the best possible way
Regression analysis
For all the quires contact me my Email id :- vijayyadav1208@gmail.com
for mor update like me, share , follow me
Index numbers
This document provides an overview of index numbers, including their meaning, features, advantages, limitations, construction methods, and examples of important index numbers. Index numbers are statistical measures used to compare economic variables, like prices or production, over time. They show percentage changes from a base period and are widely used by governments, businesses, and economists to understand inflation trends and formulate policies. The document discusses various methods for constructing index numbers, such as Laspeyre's method, Paasche's method, and Fisher's method which involve assigning weights. Important commonly used index numbers mentioned include the Consumer Price Index, Sensex, Agricultural Production Index, and Wholesale Price Index.
How to make a good graph
To make a good graph, include a title, labels for the axes, and consistent scales. The x-axis should include the independent variable like time, while the y-axis includes the dependent variable measured. Data points can be connected with a smooth curve to show patterns, and less ink allows easier readability. Key details like calculations or uncertainties should be indicated on the graph as needed.
Graphing ppt
The document discusses different types of graphs such as line graphs, bar graphs, pie charts, and pictographs. It explains that graphs show relationships between variables, with the independent variable generally on the x-axis and the dependent variable on the y-axis. Examples are provided of how to construct and interpret different graphs, along with exercises for students to practice graphing and analyzing graphs.
Different types of graphs
This document provides information about different types of graphs used to display data: circle graphs (pie charts) show portions of a whole, bar graphs allow for comparisons using vertical or horizontal bars of equal width, pictographs use pictures/symbols to represent data with a key, broken line graphs join data points with line segments to show trends over time, continuous line graphs have meaning for all points between plotted data points, and scatter plots graph sets of points. Sample questions demonstrate using a bar graph to group student marks into intervals and creating a line graph to compare the average monthly temperatures of two towns over eight months.
How to make a graph
The document provides step-by-step instructions for making a graph:
1) Collect data in a table, determine the range of values, and whether the independent variable goes on the x-axis and dependent on y-axis.
2) Label the graph with titles and units on each axis. Mark numbers on the axes at even intervals.
3) Plot the data points on the graph and draw a best-fit line through as many points as possible.
Forecasting Quantitative - Time Series.ppt
The document discusses various quantitative time series forecasting models including causal models and time series models. It describes stationary time series models including the naïve model, moving average models, and exponential smoothing. It explains that moving average models reduce random variation by averaging past data, and that exponential smoothing requires less data storage than moving averages as it applies a smoothing constant to weight the most recent period.
2 session 2a_hp case study_2010_cfvg
This document discusses aggregate demand forecasting through a case study of HP's supply chain. It explains that aggregate forecasts at higher supply chain levels are more accurate than at lower levels. The case study shows how HP forecasted demand for desktop PCs with 110V and 220V power supplies separately and in total. Forecast accuracy improved when considering total demand rather than specific configurations. Key lessons are that delayed product differentiation through aggregate forecasting and demand management helps supply chain competitiveness and cost reduction.
Index number
This document discusses index numbers, which are specialized averages used to measure changes in phenomena over time or location. It defines an index number and lists its key characteristics, uses, and problems in construction. The document then classifies index number methods into price, quantity, value, and special purpose indexes. It describes unweighted and weighted index number construction, including the Laspeyres, Paasche, Bowley, and Fisher methods for weighted indexes. Specific formulas are provided for simple aggregative, simple average of price relatives, and weighted aggregative index numbers.
Forecasting 5 6.ppt
This document discusses various quantitative forecasting techniques. It describes time series forecasting and the components of time series data including trend, seasonal, cyclical, and random variations. It then explains different forecasting methods such as the naive approach, moving averages, exponential smoothing, and least squares regression. It provides examples of how to calculate forecasts using these methods and compares their forecast errors using measures like mean absolute deviation, mean squared error, and mean absolute percent error to evaluate forecast accuracy.
Presentation1
Cost estimation involves determining the relationship between costs and activity levels using historical data. There are several types of cost behaviors such as variable, fixed, and semi-variable costs. Managers must understand these cost behaviors to predict how costs will change with activity levels. Common cost estimation methods include account classification, regression analysis, visual fitting, high-low analysis, and engineering estimates. These methods analyze past cost and activity data to develop cost behavior models. Accurate cost estimation requires high quality source data without issues like missing information, outliers, or inconsistent time periods.
More Related Content
What's hot
Intro to Forecasting - Part 3 - HRUG
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.
Index numbers99
Index numbers are used to measure and compare changes in prices, quantities, and values over time. There are several types of index numbers including simple price indexes, quantity indexes, value indexes, and aggregate price indexes. Aggregate price indexes include unweighted, Laspeyres, Paasche, and fixed-weight indexes. The Consumer Price Index is a widely used fixed-weight aggregate price index published monthly by the Bureau of Labor Statistics to measure inflation experienced by urban consumers.
Presenting scientific data graphing
This document discusses different types of graphs used to present scientific data:
- Line graphs are used to show changes in related variables over time or against each other, and can show direct or inverse proportions.
- Bar graphs compare counted data by displaying items on the x-axis and their quantities on the y-axis.
- Circle graphs or pie charts show how a fixed quantity is divided into parts.
The document provides examples of different graphs and how to interpret the relationships between variables displayed in each type of graph.
Time series Forecasting
The document summarizes time series analysis conducted to forecast sales for an airline company over the next 12 months. Key steps included: 1) checking for volatility, non-stationarity and seasonality in the data; 2) creating training and test datasets; 3) building ARIMA models and selecting the best based on error metrics; 4) generating forecasts and calculating errors compared to actual data. The optimal model with AR=0 and MA=3 was chosen for final forecasting based on lowest MAPE.
Index number
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.
Index Number (Business Statistics Tutorial )
Business Statistics Tutorial
Mahmudul Hasan Nakib
Founder President (Inventive Point)
BBA , MBA (Finance), AUB, SEU
CMA, ICMAB(Studying)
Index numbers
This document discusses index numbers and basic statistics. It begins by defining index numbers as devices that measure changes in variables over time or space. It notes that index numbers are specialized averages. The document then discusses different types of index numbers, methods for constructing them, advantages and disadvantages, and problems related to index numbers. It provides examples of calculating Fisher's ideal index number. In the end, it lists some required readings and resources for further learning about index numbers and basic statistics.
Chart
A pie chart is a circular graph that is divided into sectors or slices to illustrate numerical proportion. Each slice's central angle represents the quantity's percentage of the whole. To create a pie chart, you calculate the percentage of each category by dividing its value by the total value. You then multiply the decimal percentage by 360 degrees to determine each slice's central angle. A pie chart visually compares the proportional relationship of parts to a whole.
Elasticity 2
This document discusses measuring income elasticity at a point on an Engel curve. It explains that income elasticity is measured similarly to supply elasticity by calculating the percentage change in quantity demanded divided by the percentage change in income between two points on the Engel curve. The document also briefly mentions applications of elasticity coefficients to economic policy and business decisions.
Weighted index number - business statistics
Special For Jaipur national university Student || BBA || Weighted Index Number || Business Statics || Presentation
Class 11 Economics -Introduction to index numbers
INDEX NUMBERS
Economic activities have constant tendency to change. Prices of commodities which arc the total result of number of economic activities also have a tendency to fluctuate. The problem of change in prices is very important. But it is not very simple to study this problem and derive conclusions because price of different commodities change by different degrees. Hence, there is a great need for a device which can smoothen the irregularities in the prices to obtain a conclusion.
Our institute AMEND EDUCATION ACADEMY works in the direction to educate the students in the best possible way
Regression analysis
For all the quires contact me my Email id :- vijayyadav1208@gmail.com
for mor update like me, share , follow me
Index numbers
This document provides an overview of index numbers, including their meaning, features, advantages, limitations, construction methods, and examples of important index numbers. Index numbers are statistical measures used to compare economic variables, like prices or production, over time. They show percentage changes from a base period and are widely used by governments, businesses, and economists to understand inflation trends and formulate policies. The document discusses various methods for constructing index numbers, such as Laspeyre's method, Paasche's method, and Fisher's method which involve assigning weights. Important commonly used index numbers mentioned include the Consumer Price Index, Sensex, Agricultural Production Index, and Wholesale Price Index.
How to make a good graph
To make a good graph, include a title, labels for the axes, and consistent scales. The x-axis should include the independent variable like time, while the y-axis includes the dependent variable measured. Data points can be connected with a smooth curve to show patterns, and less ink allows easier readability. Key details like calculations or uncertainties should be indicated on the graph as needed.
Graphing ppt
The document discusses different types of graphs such as line graphs, bar graphs, pie charts, and pictographs. It explains that graphs show relationships between variables, with the independent variable generally on the x-axis and the dependent variable on the y-axis. Examples are provided of how to construct and interpret different graphs, along with exercises for students to practice graphing and analyzing graphs.
Different types of graphs
This document provides information about different types of graphs used to display data: circle graphs (pie charts) show portions of a whole, bar graphs allow for comparisons using vertical or horizontal bars of equal width, pictographs use pictures/symbols to represent data with a key, broken line graphs join data points with line segments to show trends over time, continuous line graphs have meaning for all points between plotted data points, and scatter plots graph sets of points. Sample questions demonstrate using a bar graph to group student marks into intervals and creating a line graph to compare the average monthly temperatures of two towns over eight months.
How to make a graph
The document provides step-by-step instructions for making a graph:
1) Collect data in a table, determine the range of values, and whether the independent variable goes on the x-axis and dependent on y-axis.
2) Label the graph with titles and units on each axis. Mark numbers on the axes at even intervals.
3) Plot the data points on the graph and draw a best-fit line through as many points as possible.
Forecasting Quantitative - Time Series.ppt
The document discusses various quantitative time series forecasting models including causal models and time series models. It describes stationary time series models including the naïve model, moving average models, and exponential smoothing. It explains that moving average models reduce random variation by averaging past data, and that exponential smoothing requires less data storage than moving averages as it applies a smoothing constant to weight the most recent period.
2 session 2a_hp case study_2010_cfvg
This document discusses aggregate demand forecasting through a case study of HP's supply chain. It explains that aggregate forecasts at higher supply chain levels are more accurate than at lower levels. The case study shows how HP forecasted demand for desktop PCs with 110V and 220V power supplies separately and in total. Forecast accuracy improved when considering total demand rather than specific configurations. Key lessons are that delayed product differentiation through aggregate forecasting and demand management helps supply chain competitiveness and cost reduction.
What's hot (19)
Similar to Qt presentation
Index number
This document discusses index numbers, which are specialized averages used to measure changes in phenomena over time or location. It defines an index number and lists its key characteristics, uses, and problems in construction. The document then classifies index number methods into price, quantity, value, and special purpose indexes. It describes unweighted and weighted index number construction, including the Laspeyres, Paasche, Bowley, and Fisher methods for weighted indexes. Specific formulas are provided for simple aggregative, simple average of price relatives, and weighted aggregative index numbers.
Forecasting 5 6.ppt
This document discusses various quantitative forecasting techniques. It describes time series forecasting and the components of time series data including trend, seasonal, cyclical, and random variations. It then explains different forecasting methods such as the naive approach, moving averages, exponential smoothing, and least squares regression. It provides examples of how to calculate forecasts using these methods and compares their forecast errors using measures like mean absolute deviation, mean squared error, and mean absolute percent error to evaluate forecast accuracy.
Presentation1
Cost estimation involves determining the relationship between costs and activity levels using historical data. There are several types of cost behaviors such as variable, fixed, and semi-variable costs. Managers must understand these cost behaviors to predict how costs will change with activity levels. Common cost estimation methods include account classification, regression analysis, visual fitting, high-low analysis, and engineering estimates. These methods analyze past cost and activity data to develop cost behavior models. Accurate cost estimation requires high quality source data without issues like missing information, outliers, or inconsistent time periods.
Forcast2
This document provides an overview of forecasting methods for operations management. It defines forecasting and identifies key principles. Quantitative and qualitative forecasting methods are described, including time series models, causal models, and techniques for addressing trends, seasonality, and error measurement. Guidelines for selecting the appropriate forecasting method and software are also provided.
Cost-Behavior-1-3.pptx
This document discusses different types of cost behavior and methods for analyzing mixed costs. It describes variable costs, fixed costs, and semi-variable costs. Several methods are presented for separating mixed costs into variable and fixed components, including account analysis, industrial engineering, conference methods, high-low analysis, regression analysis, and scattergraph analysis. Each method has strengths and weaknesses for estimating cost functions based on factors like objectivity, use of data points, and complexity.
Time series
concept of Business Statistics.
Meaning and method under Business Statistics,
way of answering under few methods
conceptual estimate
The document discusses cost estimation for construction projects. It provides 5 key elements of cost estimation: 1) project information, 2) historical data, 3) current data, 4) estimating methodology, and 5) the cost estimator. It also discusses adjusting historical project costs using a city cost index to account for inflation over time. Construction cost indices published by the Indonesian government are more appropriate to use for this than consumer price indices. Tables show construction cost indices for various cities and regencies in West Sumatra, Indonesia from 2010-2015. Inflation rates for Indonesia from 2006-2015 are also provided to allow for cost adjustments between years.
Analyzing and forecasting time series data ppt @ bec doms
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.
Index numbers( simple index number by fixed base method )
The document discusses index numbers which measure changes in variables over time. It defines simple and composite index numbers. The three main types of index numbers are price, quantity, and value indexes. Examples are provided to demonstrate calculating price indexes using the fixed base method. Index numbers are calculated for different years using different base periods to compare price changes.
timeseries1.ppt
This document provides an overview of time series forecasting methods. It describes what forecasting is, explains the components of a time series including trend, seasonality, cyclicality, and irregular variations. It discusses qualitative and quantitative forecasting approaches and focuses on various quantitative time series forecasting methods like moving averages, exponential smoothing, and linear and quadratic trend models. These methods are used to forecast values by analyzing historical time series data patterns.
Ch1 2 index number
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.
Chapter 16
This chapter discusses time series analysis and forecasting. The key components are:
1. A time series contains data recorded over time and can be analyzed to identify trends and patterns that may continue in the future.
2. The components of a time series are secular trends, cyclical variation, seasonal variation, and irregular variation.
3. Moving averages and weighted moving averages can be used to smooth time series data and identify trends. Linear and nonlinear trend lines can also model trends in the data.
4. Seasonal indices identify seasonal patterns that repeat each year and can be used to deseasonalize time series data. Autocorrelation tests whether residuals are independent or correlated over time.
OPM101Chapter.ppt
This document provides an overview of forecasting methods for operations management. It begins by listing the key learning objectives which are to identify principles of forecasting, explain the forecasting process, describe different forecasting methods and models, and compute forecast accuracy. The document then covers various qualitative and quantitative forecasting techniques including time series models, causal models, and methods for addressing trends, seasonality, and accuracy evaluation. Linear regression is presented as a tool for causal modeling.
Time series
Basic Concepts, Components of time series. The trend, Fitting of trend by least square method and moving average method, uses of time series in business.
Time series Analysis
This document defines time series and its components. A time series is a set of observations recorded over successive time intervals. It has four main components: trend, seasonality, cycles, and irregular variations. Trend refers to the overall increasing or decreasing tendency over time. Seasonality refers to predictable changes that occur around the same time each year. Cycles have periods longer than a year. Irregular variations are random fluctuations. The document also discusses methods for analyzing time series components including additive, multiplicative, and mixed models.
BS6_Measurement of Trend.pptx
This document discusses methods for measuring trends in time series data. It describes secular trends as long-term movements in data over time, which can be upward, downward, or stagnant. Four common methods for measuring trends are discussed: graphical/freehand method, method of semi-averages, moving averages method, and least squares method. The graphical method involves visually fitting a smooth curve to the data points, while the semi-averages method divides the data into two sets and connects the midpoints using a straight line. Strengths and weaknesses of each approach are also presented.
Air Passenger Prediction Using ARIMA Model
How has the Airline industry suffered during the pandemic? was a question that always stuck in my mind
when I saw articles on how travelling has been banned and movement of people not only from one country
to another country but also one state to another was being restricted. Hence as a statistics Student with a
curious mind I set out on a quest to find the effect of pandemic on the airline Industry. Tying statistics to
business problems that could benefit a business excites me. Hence I took up the initiative and called two
friends and decided to take their help in this task
we decided to get month wise domestic and international aviation data of the number of departures and
passengers in India during Jan 2010 to April 2022 from Airport Authorities of India website. We then took
this data cleaned, processed and transformed it to make it usable for our analysis. The analysis I suggested
to do for this objective was a familiar one which we had recently learnt in our fifth semester which was
Time series analysis under which we used the Auto Regressive Integrated Moving Average model which
creates a model that uses the past data to predict the future. As I am comfortable in coding I did the analysis
using R studio and python which has some excellent libraries to assist us in the analysis. We created the
model in such a way that the data could predict how the industry would behave if covid had not occurred.
We then compared the reality with the simulation which gave us some interesting interpretations. The results we found is that, international aviation industry on an average suffered five crores thirty three lakhs
per flight per month in losses and the domestic industry on an average suffered eighty two lakhs twenty
four thousand per flight per month in losses. But the key takeaway for the aviation industry from our
simulation vs reality analysis is that international travel is almost back on track after a major setback like
travel ban and it took 2 years and 3 months to do so whereas domestic travel is yet to recover.
I presented our findings and analysis to my statistics professor Mrs.Anwesha Roy also under whose
guidance we could come this far. She was thrilled with our work and encouraged us to get it published and
my team is currently working on it.
Cost Accounting : Determining How Cost Behaves
This document discusses how to determine cost behavior by estimating cost functions based on the relationship between costs and activity levels. It outlines the key steps in estimating cost functions, including choosing a dependent cost variable and independent cost driver, collecting data, and evaluating the fit of linear and nonlinear models. Common issues with data collection like inconsistent time periods or unreliable measurements are also addressed.
Moving average method maths ppt
The document discusses the moving average method for forecasting or smoothing time series data. It explains that a moving average is calculated by averaging successive data points over a set time period, with old data dropped as new data is added. The document outlines how to calculate moving averages for both odd and even time periods. It also discusses the merits and limitations of the moving average method, and provides an example calculation.
Bba 3274 qm week 6 part 2 forecasting
This document discusses various quantitative forecasting techniques including time series models. It provides an overview of moving averages, exponential smoothing, trend projections, and decomposition models. Examples are given to illustrate computing forecasts using a three-month simple moving average and a three-month weighted moving average. Exponential smoothing is also introduced as a type of moving average that requires less data to compute forecasts.
Similar to Qt presentation (20)
Analyzing and forecasting time series data ppt @ bec doms
Analyzing and forecasting time series data ppt @ bec doms
Index numbers( simple index number by fixed base method )
Index numbers( simple index number by fixed base method )
More from Devyani Jain
Wto dj
- On March 6, 2012, the United States requested WTO dispute consultations with India regarding India's import prohibitions on certain agricultural products from the US, purportedly due to concerns about Avian Influenza.
- A WTO panel was established in February 2013 and found several of India's measures to be inconsistent with WTO agreements in October 2014. India appealed some findings in January 2015.
- In June 2015, the Appellate Body upheld most of the panel's findings that India's avian influenza measures were inconsistent with WTO obligations. The DSB adopted the panel and Appellate Body reports.
- India was given until June 2016 to bring its measures into compliance with its WTO obligations.
Imm presentation
This document discusses different types of companies (domestic, international, multinational, global, and transnational) and defines the attributes of a transnational company. It provides examples of attributes like configuration of assets and capabilities, roles of overseas operations, and development and diffusion of knowledge. A transnational company prefers a flexible value chain and coordination mechanisms to facilitate local responsiveness while also achieving global integration. It has dispersed and interdependent assets that are also centralized. Knowledge is developed jointly and shared worldwide. Company C is identified as the transnational company based on its attributes described in the document.
Hbo final1
This document discusses six distinct leadership styles identified by research - coercive, authoritative, affiliative, democratic, pacesetting, and coaching. Each style springs from different components of emotional intelligence and has a direct impact on a company's working atmosphere. Effective leaders are able to apply the right leadership style to match the people and situation, and use multiple styles. Leaders who have mastered four or more styles, especially authoritative, democratic, affiliative and coaching, are able to optimize business performance and create the best organizational climate.
The consumer decision journey
Article by David Court, Dave Elzinga, Susan Mulder, and Ole Jørgen Vetvik
(McKinsey Quarterly)
Social media in political uprising
Social media played a key role in political uprisings in Tunisia and Egypt. In Tunisia, social media helped spread information about street protests against a repressive dictator, despite government efforts to shut down the internet. This led to the overthrow of Zine el-Abidine Ben Ali. Similarly in Egypt, a Facebook page helped spark protests against Hosni Mubarak over issues like police brutality and corruption. Large gatherings in Cairo's Tahrir Square grew with help from social media to coordinate protests, leading to Mubarak stepping down. However, social media was also used during London riots to spread information about targets, though for negative purposes rather than political change.
Ultratech
This document shows various financial metrics for Ultratech over the years 2011-2015. The profit margin and return on asset have fluctuated over the years between 0-12%. The liquidity ratios including receivable turnover, average collection period, inventory turnover, quick ratio and current ratio are shown for the years 2011-2015 with most values between 0-25. The solvency ratios of debt to equity ratio and interest cover are shown but no values are provided.
Ernst & young
Ernst & Young (EY) is one of the largest professional services networks in the world, with headquarters in London. EY has over 190,000 employees working across 709 offices in 150 countries. The company provides assurance, tax, advisory and transaction advisory services and generates annual revenue of over $27 billion. EY was formed in 1989 through the merger of Ernst & Whinney and Arthur Young & Co. and is considered one of the 'Big Four' accounting firms globally.
Walt disney
Disney generates over 40% of its total revenue from its cable and TV business. Star Wars: The Force Awakens is the seventh episode in the Skywalker family saga and a sequel to Return of the Jedi from 1983. It will be the first Star Wars film not released in May and was filmed in England and the United Arab Emirates desert.
Bca 7 cs
The document discusses the need for effective communication and outlines the 7 C's of communication: completeness, conciseness, consideration, concreteness, clarity, courtesy, and correctness. It provides guidelines for each C, such as including all necessary information for completeness, using specific facts and figures to be concrete, choosing precise words for clarity, and using respectful language to be courteous. The 7 C's provide a framework for composing effective written and oral messages by focusing on the content and style of presentation.
Sir adrian cadbury committee
The document summarizes the key recommendations of the Cadbury Committee on corporate governance established in 1991 by the Bank of England and the London Stock Exchange. The committee, chaired by Sir Adrian Cadbury, published a code of best practice in 1992 that focused on board structure and responsibilities. It recommended clearly dividing responsibilities between executive and non-executive directors, having a majority of independent non-executive directors, and establishing remuneration and audit committees.
Metro manila fast food market
Burger King faced challenges in the Philippine fast food market due to lower brand awareness and a lack of visibility compared to competitors like Jollibee and McDonald's. Market research including a survey and focus group found that while burgers and pizzas were the most popular fast food items, Jollibee had the highest brand recall. Factors like food quality, price, and service were most important to customers when choosing a restaurant. The research also revealed opportunities for Burger King to improve its menu options and add family-friendly items and services.
Global enterprises
Global enterprises, also known as multinational corporations (MNCs), are large companies that operate internationally through a network of branches across multiple countries. MNCs are characterized by their huge size, extensive product offerings, advanced technology, effective marketing strategies, and global operations. The top 200 MNCs control over a quarter of the world's economy due to their vast capital resources, latest technologies, and strong brand recognition that allow them to sell products worldwide. MNCs have a significant impact on the global economy and exercise massive influence through their international operations.
Dad
A son or daughter's perspective of their father changes dramatically over time. As a young child, they see their father as all-knowing and great. As they become teenagers, they view their father as outdated and not understanding the modern world. In adulthood, they appreciate all the hardships their father endured to raise them and bring order to the family, realizing how difficult it was for him to manage and control things as they grew older.
More from Devyani Jain (13)
Recently uploaded
How MJ Global Leads the Packaging Industry.pdf
MJ Global's success in staying ahead of the curve in the packaging industry is a testament to its dedication to innovation, sustainability, and customer-centricity. By embracing technological advancements, leading in eco-friendly solutions, collaborating with industry leaders, and adapting to evolving consumer preferences, MJ Global continues to set new standards in the packaging sector.
How to Implement a Strategy: Transform Your Strategy with BSC Designer's Comp...
The Strategy Implementation System offers a structured approach to translating stakeholder needs into actionable strategies using high-level and low-level scorecards. It involves stakeholder analysis, strategy decomposition, adoption of strategic frameworks like Balanced Scorecard or OKR, and alignment of goals, initiatives, and KPIs.
Key Components:
- Stakeholder Analysis
- Strategy Decomposition
- Adoption of Business Frameworks
- Goal Setting
- Initiatives and Action Plans
- KPIs and Performance Metrics
- Learning and Adaptation
- Alignment and Cascading of Scorecards
Benefits:
- Systematic strategy formulation and execution.
- Framework flexibility and automation.
- Enhanced alignment and strategic focus across the organization.
Industrial Tech SW: Category Renewal and Creation
Every industrial revolution has created a new set of categories and a new set of players.
Multiple new technologies have emerged, but Samsara and C3.ai are only two companies which have gone public so far.
Manufacturing startups constitute the largest pipeline share of unicorns and IPO candidates in the SF Bay Area, and software startups dominate in Germany.
HOW TO START UP A COMPANY A STEP-BY-STEP GUIDE.pdf
How to Start Up a Company: A Step-by-Step Guide Starting a company is an exciting adventure that combines creativity, strategy, and hard work. It can seem overwhelming at first, but with the right guidance, anyone can transform a great idea into a successful business. Let's dive into how to start up a company, from the initial spark of an idea to securing funding and launching your startup.
Introduction
Have you ever dreamed of turning your innovative idea into a thriving business? Starting a company involves numerous steps and decisions, but don't worry—we're here to help. Whether you're exploring how to start a startup company or wondering how to start up a small business, this guide will walk you through the process, step by step.
Structural Design Process: Step-by-Step Guide for Buildings
The structural design process is explained: Follow our step-by-step guide to understand building design intricacies and ensure structural integrity. Learn how to build wonderful buildings with the help of our detailed information. Learn how to create structures with durability and reliability and also gain insights on ways of managing structures.
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdf
In the recent edition, The 10 Most Influential Leaders Guiding Corporate Evolution, 2024, The Silicon Leaders magazine gladly features Dejan Štancer, President of the Global Chamber of Business Leaders (GCBL), along with other leaders.
Company Valuation webinar series - Tuesday, 4 June 2024
This session provided an update as to the latest valuation data in the UK and then delved into a discussion on the upcoming election and the impacts on valuation. We finished, as always with a Q&A
Unveiling the Dynamic Personalities, Key Dates, and Horoscope Insights: Gemin...
Explore the fascinating world of the Gemini Zodiac Sign. Discover the unique personality traits, key dates, and horoscope insights of Gemini individuals. Learn how their sociable, communicative nature and boundless curiosity make them the dynamic explorers of the zodiac. Dive into the duality of the Gemini sign and understand their intellectual and adventurous spirit.
Satta Matka Dpboss Matka Guessing Kalyan Chart Indian Matka Kalyan panel Chart
Satta Matka Dpboss Matka Guessing Kalyan Chart Indian Matka Kalyan panel Chart➒➌➎➏➑➐➋➑➐➐Dpboss Matka Guessing Satta Matka Kalyan Chart Indian Matka
SATTA MATKA SATTA FAST RESULT KALYAN TOP MATKA RESULT KALYAN SATTA MATKA FAST RESULT MILAN RATAN RAJDHANI MAIN BAZAR MATKA FAST TIPS RESULT MATKA CHART JODI CHART PANEL CHART FREE FIX GAME SATTAMATKA ! MATKA MOBI SATTA 143 spboss.in TOP NO1 RESULT FULL RATE MATKA ONLINE GAME PLAY BY APP SPBOSS2024-6-01-IMPACTSilver-Corp-Presentation.pdf
IMPACT Silver is a pure silver zinc producer with over $260 million in revenue since 2008 and a large 100% owned 210km Mexico land package - 2024 catalysts includes new 14% grade zinc Plomosas mine and 20,000m of fully funded exploration drilling.
Digital Transformation Frameworks: Driving Digital Excellence
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
Call 8867766396 Satta Matka Dpboss Matka Guessing Satta batta Matka 420 Satta...
CALL 8867766396 SATTA MATKA | DPBOSS | KALYAN MAIN BAZAR | FAST MATKA | DPBOSS GUESSING | TARA MATKA | KALYAN CHART | MATKA BOSS
Part 2 Deep Dive: Navigating the 2024 Slowdown
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
How to Implement a Real Estate CRM Software
To implement a CRM for real estate, set clear goals, choose a CRM with key real estate features, and customize it to your needs. Migrate your data, train your team, and use automation to save time. Monitor performance, ensure data security, and use the CRM to enhance marketing. Regularly check its effectiveness to improve your business.
Authentically Social by Corey Perlman - EO Puerto Rico
Authentically Social by Corey Perlman - EO Puerto RicoCorey Perlman, Social Media Speaker and Consultant
Social media for business Taurus Zodiac Sign: Unveiling the Traits, Dates, and Horoscope Insights of th...
Dive into the steadfast world of the Taurus Zodiac Sign. Discover the grounded, stable, and logical nature of Taurus individuals, and explore their key personality traits, important dates, and horoscope insights. Learn how the determination and patience of the Taurus sign make them the rock-steady achievers and anchors of the zodiac.
Creative Web Design Company in Singapore
At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
Best Forex Brokers Comparison in INDIA 2024
Navigating the world of forex trading can be challenging, especially for beginners. To help you make an informed decision, we have comprehensively compared the best forex brokers in India for 2024. This article, reviewed by Top Forex Brokers Review, will cover featured award winners, the best forex brokers, featured offers, the best copy trading platforms, the best forex brokers for beginners, the best MetaTrader brokers, and recently updated reviews. We will focus on FP Markets, Black Bull, EightCap, IC Markets, and Octa.
Recently uploaded (20)
How to Implement a Strategy: Transform Your Strategy with BSC Designer's Comp...
How to Implement a Strategy: Transform Your Strategy with BSC Designer's Comp...
HOW TO START UP A COMPANY A STEP-BY-STEP GUIDE.pdf
HOW TO START UP A COMPANY A STEP-BY-STEP GUIDE.pdf
Structural Design Process: Step-by-Step Guide for Buildings
Structural Design Process: Step-by-Step Guide for Buildings
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdf
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdf
Company Valuation webinar series - Tuesday, 4 June 2024
Company Valuation webinar series - Tuesday, 4 June 2024
Unveiling the Dynamic Personalities, Key Dates, and Horoscope Insights: Gemin...
Unveiling the Dynamic Personalities, Key Dates, and Horoscope Insights: Gemin...
Satta Matka Dpboss Matka Guessing Kalyan Chart Indian Matka Kalyan panel Chart
Satta Matka Dpboss Matka Guessing Kalyan Chart Indian Matka Kalyan panel Chart
Digital Transformation Frameworks: Driving Digital Excellence
Digital Transformation Frameworks: Driving Digital Excellence
Call 8867766396 Satta Matka Dpboss Matka Guessing Satta batta Matka 420 Satta...
Call 8867766396 Satta Matka Dpboss Matka Guessing Satta batta Matka 420 Satta...
Authentically Social by Corey Perlman - EO Puerto Rico
Authentically Social by Corey Perlman - EO Puerto Rico
Taurus Zodiac Sign: Unveiling the Traits, Dates, and Horoscope Insights of th...
Taurus Zodiac Sign: Unveiling the Traits, Dates, and Horoscope Insights of th...
Qt presentation
- 1. BY: Devyani Jain (09310) Madhu Vuppuluri (09314) Malvika Bahety (09315) Saloni Grover (09328) Utpala (09337) Vasudha Gupta (09340)
- 2. MeasurementofForecasting error Mean error Mean absolute deviation MAD= mean of absolute value of errors Mean square error MSE= mean of squared errors Mean percentage error Mena absolute percentage error Mean absolute deviation Mean square error Error of an individual forecast= actual value- forecast value Time series components •Trend •Cyclicality •Seasonality •Irregularity
- 4. Linear Regression Trend Analysis 𝑌𝑖 = β0 + β1 + ϵi Regression Trend Analysis Using Quadratic Models Yi = β0 + β1Xti + β2X²ti + ϵi Holt’s Two-Parameter Exponential Smoothing Method
- 5. Decomposition Technique Uses the multiplicative model Multiplicative Model=T.C.S.I Winter’s three parameter exponential smoothing model Uses a weight for seasonality Develops forecasts that include a smoothing value for observations, a trend value and a seasonal value Seasonal effects are patterns of data behaviour that occur in periods of time of less than 1 year.
- 6. Autocorelation Definition: When error terms of regression forecasting model are limited. Problem: Coefficients of regression may not have minimum variance and may be inefficient. Solutions: Adding important predictor variables that have been left out. Transforming data into variables that may help solving the problem Autoregression A multiple regression when independent variable are time lagged version of dependent variable. 𝑦 = 𝑏0 + 𝑏1 𝑌𝑡−1 + 𝑏2 𝑌𝑡−2 + 𝑏3 𝑌𝑡−2
- 7. SIMPLE INDEX NUMBERS Makes possible conversion of prices, costs, quantities etc. for different time periods into a no. scale with base year equaling 100% Ii = Xi/Xo UNWEIGHTED AGGREGATE PRICE INDEX NUMBERS This is a technique used for combining several items and determining index no. for total (aggregate) Ii = ƩPi/ƩPo WEIGHTED AGGREGATE PRICE INDEX NUMBERS Computed by multiplying quantity weights and item prices in determining the market base worth for a given year IL = ƩPIQI/Ʃ PoQo LASPEYRES PRICE INDEX Computed by using quantities of the base period of all other years. ADVANTAGE – Price index for all years can be compared IL = ƩPIQI/Ʃ PoQo PAASCHE PRICE INDEX Computed by using the quantities for the year of interest in computations for a given year. ADVANTAGE – Incorporates current quantity figures in the calculations. IL = ƩPIQI/ƩPoQi