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Methods of measuring the influence of factors in deterministic analysis https://econ.biem.sumdu.edu.ua/

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lecture 2.pptx

This document discusses methods and methodologies for economic analysis. It covers:
1) The essence of economic analysis methods, which study economic objects by identifying relationships between parameters.
2) Traditional methods for processing economic information, such as comparison, grouping, and graphical techniques.
3) Methods of factor analysis, including deterministic, stochastic, direct, inverse, single-stage, and dynamic approaches.
4) Modeling factor systems using additive, multiplicative, multiple, and combined models to represent functional relationships between indicators and factors.

Statr session 23 and 24

This document provides an overview of simple and multiple linear regression analysis. It discusses key concepts such as:
- Dependent and independent variables in bivariate linear regression
- Using scatter plots to explore relationships
- Estimating regression coefficients and equations for simple and multiple regression models
- Using regression models to predict outcomes based on independent variable values
- Conducting statistical tests on overall regression models and individual coefficients

Taguchi design of experiments nov 24 2013

This document provides an overview of Taguchi design of experiments. It defines Taguchi methods, which use orthogonal arrays to conduct a minimal number of experiments that can provide full information on factors that affect performance. The assumptions of Taguchi methods include additivity of main effects. The key steps in an experiment are selecting variables and their levels, choosing an orthogonal array, assigning variables to columns, conducting experiments, and analyzing data through sensitivity analysis and ANOVA.

OPTIMIZATION TECHNIQUES IN PHARMACEUTICAL FORMULATION AND PROCESSING

Optimization techniques are important in pharmaceutical formulation and processing to make products as effective as possible. There are two main types of optimization problems - unconstrained and constrained. Variables can be independent factors under the formulator's control, or dependent response variables. Classical optimization uses calculus to find the maximum or minimum of a function. Statistical experimental designs are also used, involving factors and responses in regression models. Several techniques are commonly used, including sequential hill-climbing methods, simultaneous methods using experimental designs, and combinations. Lagrangian and simplex methods are classical techniques to optimize multiple factors and responses. Statistical search methods incorporate experimental design and computer-assisted multivariate analysis to optimize complex systems with many variables.

Final notes on s1 qc

Statistical quality control refers to using statistical methods to monitor and maintain quality in products and services. It involves inspecting random samples from a process to determine if the process is functioning properly and producing items within specifications. Acceptance sampling determines whether to accept or reject an entire batch of items based on inspecting a random sample. Common control charts include X-bar and R charts for variables and P charts for attributes. Forecasting uses statistical techniques to predict future events and outcomes based on past and present data to help managers make informed decisions.

Final notes on s1 qc

Statistical quality control refers to using statistical methods to monitor and maintain quality in products and services. It involves collecting data samples from a production process and using statistical process control and acceptance sampling techniques to determine if the process is functioning properly and producing acceptable quality levels. The main objectives are to control materials, internal rejections, customer issues, supplier evaluations, and corrective actions. Control charts graphically display process data over time and can identify changes or abnormalities that require process adjustments to maintain control. Common types include X-bar and R charts for variables and P charts for attributes.

Regression analysis ppt

The document provides an overview of regression analysis. It defines regression analysis as a technique used to estimate the relationship between a dependent variable and one or more independent variables. The key purposes of regression are to estimate relationships between variables, determine the effect of each independent variable on the dependent variable, and predict the dependent variable given values of the independent variables. The document also outlines the assumptions of the linear regression model, introduces simple and multiple regression, and describes methods for model building including variable selection procedures.

604_multiplee.ppt

This document discusses multiple regression analysis techniques. It begins by stating the goals of developing a statistical model to predict dependent variables from independent variables and using multiple regression when more than one independent variable is useful for prediction. It then provides an introduction to simple and multiple regression. The rest of the document discusses key aspects of multiple regression analysis, including linear models, the method of least squares, standard error of estimate, coefficient of multiple determination, hypothesis testing, and selection of predictor variables.

lecture 2.pptx

This document discusses methods and methodologies for economic analysis. It covers:
1) The essence of economic analysis methods, which study economic objects by identifying relationships between parameters.
2) Traditional methods for processing economic information, such as comparison, grouping, and graphical techniques.
3) Methods of factor analysis, including deterministic, stochastic, direct, inverse, single-stage, and dynamic approaches.
4) Modeling factor systems using additive, multiplicative, multiple, and combined models to represent functional relationships between indicators and factors.

Statr session 23 and 24

This document provides an overview of simple and multiple linear regression analysis. It discusses key concepts such as:
- Dependent and independent variables in bivariate linear regression
- Using scatter plots to explore relationships
- Estimating regression coefficients and equations for simple and multiple regression models
- Using regression models to predict outcomes based on independent variable values
- Conducting statistical tests on overall regression models and individual coefficients

Taguchi design of experiments nov 24 2013

This document provides an overview of Taguchi design of experiments. It defines Taguchi methods, which use orthogonal arrays to conduct a minimal number of experiments that can provide full information on factors that affect performance. The assumptions of Taguchi methods include additivity of main effects. The key steps in an experiment are selecting variables and their levels, choosing an orthogonal array, assigning variables to columns, conducting experiments, and analyzing data through sensitivity analysis and ANOVA.

OPTIMIZATION TECHNIQUES IN PHARMACEUTICAL FORMULATION AND PROCESSING

Optimization techniques are important in pharmaceutical formulation and processing to make products as effective as possible. There are two main types of optimization problems - unconstrained and constrained. Variables can be independent factors under the formulator's control, or dependent response variables. Classical optimization uses calculus to find the maximum or minimum of a function. Statistical experimental designs are also used, involving factors and responses in regression models. Several techniques are commonly used, including sequential hill-climbing methods, simultaneous methods using experimental designs, and combinations. Lagrangian and simplex methods are classical techniques to optimize multiple factors and responses. Statistical search methods incorporate experimental design and computer-assisted multivariate analysis to optimize complex systems with many variables.

Final notes on s1 qc

Statistical quality control refers to using statistical methods to monitor and maintain quality in products and services. It involves inspecting random samples from a process to determine if the process is functioning properly and producing items within specifications. Acceptance sampling determines whether to accept or reject an entire batch of items based on inspecting a random sample. Common control charts include X-bar and R charts for variables and P charts for attributes. Forecasting uses statistical techniques to predict future events and outcomes based on past and present data to help managers make informed decisions.

Final notes on s1 qc

Statistical quality control refers to using statistical methods to monitor and maintain quality in products and services. It involves collecting data samples from a production process and using statistical process control and acceptance sampling techniques to determine if the process is functioning properly and producing acceptable quality levels. The main objectives are to control materials, internal rejections, customer issues, supplier evaluations, and corrective actions. Control charts graphically display process data over time and can identify changes or abnormalities that require process adjustments to maintain control. Common types include X-bar and R charts for variables and P charts for attributes.

Regression analysis ppt

The document provides an overview of regression analysis. It defines regression analysis as a technique used to estimate the relationship between a dependent variable and one or more independent variables. The key purposes of regression are to estimate relationships between variables, determine the effect of each independent variable on the dependent variable, and predict the dependent variable given values of the independent variables. The document also outlines the assumptions of the linear regression model, introduces simple and multiple regression, and describes methods for model building including variable selection procedures.

604_multiplee.ppt

This document discusses multiple regression analysis techniques. It begins by stating the goals of developing a statistical model to predict dependent variables from independent variables and using multiple regression when more than one independent variable is useful for prediction. It then provides an introduction to simple and multiple regression. The rest of the document discusses key aspects of multiple regression analysis, including linear models, the method of least squares, standard error of estimate, coefficient of multiple determination, hypothesis testing, and selection of predictor variables.

Factor analysis (fa)

Here are the steps I would take to analyze this data using exploratory factor analysis:
1. Check assumptions
- Sample size of 300 is adequate
- Most correlations are between .3 and .8
2. Extract initial factors using principal axis factoring
- Kaiser's criterion suggests 4 factors with eigenvalues > 1
3. Rotate factors orthogonally using varimax rotation
- This will make the factor structure more interpretable
4. Interpret the factors based on which items have strong loadings
- Factor 1 relates to anxiety about learning SPSS
- Factor 2 relates to anxiety about using computers
- Factors 3 and 4 may reflect other aspects of statistics anxiety
5. Compute factor scores if desired to use in further

Types of models

Classification of mathematical modeling,
Classification based on Variation of Independent Variables,
Static Model,
Dynamic Model,
Rigid or Deterministic Models,
Stochastic or Probabilistic Models,
Comparison Between Rigid and Stochastic Models

Regression Analysis.pptx

Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.

Regression Analysis Techniques.pptx

Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.

Multiple Regression.ppt

This document discusses multiple regression analysis. It begins by introducing multiple regression as an extension of simple linear regression that allows for modeling relationships between a response variable and multiple explanatory variables. It then covers topics such as examining variable distributions, building regression models, estimating model parameters, and assessing overall model fit and significance of individual predictors. An example demonstrates using multiple regression to build a model for predicting cable television subscribers based on advertising rates, station power, number of local families, and number of competing stations.

4. regression analysis1

This document provides an overview of regression analysis, including:
- Regression analysis is used to study the relationship between variables and predict one variable from another. It can be linear or non-linear.
- Simple regression involves one independent and one dependent variable, while multiple regression involves two or more independent variables.
- The method of least squares is used to determine the regression equation that best fits the data by minimizing the sum of the squared residuals.

Managerial-Accounting-Cost-Estimation.pptx

This document discusses key concepts in managerial accounting related to cost estimation and classification. It covers:
1) Direct and indirect costs and how they are classified on financial statements as expired or unexpired costs.
2) The composition of manufacturing costs including prime costs, conversion costs, and period costs.
3) Basic cost behavior patterns including variable, fixed, mixed, and step costs and how they react to changes in activity.
4) Methods for separating mixed costs into fixed and variable components such as the high-low method and scatterplot method.
5) Key terms like cost predictors, cost drivers, and overhead cost allocation.

BRM-lecture-11.ppt

This document discusses correlation analysis and regression analysis. It begins by defining correlation as a measure of how two variables vary together. A positive correlation means the variables increase or decrease together, while a negative correlation means one variable increases as the other decreases. Regression analysis investigates the relationship between a dependent variable and one or more independent variables. An example is provided to illustrate calculating a correlation coefficient and testing hypotheses about relationships between variables using a regression model. Key terms discussed include the Pearson correlation coefficient, coefficient of determination, t-statistic, and developing a conceptual model for multiple regression analysis.

Msa presentation

This document discusses measurement system analysis (MSA), which is used to evaluate statistical properties of process measurement systems. MSA determines if current measurement systems provide representative, unbiased and minimal variability measurements. The document outlines the MSA process, including preparing for a study, evaluating stability, accuracy, precision, linearity, and repeatability and reproducibility. Accuracy looks at bias while precision considers repeatability and reproducibility. MSA is required for certification and helps identify process variation sources and minimize defects.

10685 6.1 multivar_mr_srm bm i

This document discusses multivariate techniques, specifically multiple regression. It defines multivariate techniques as those that analyze more than two variables simultaneously, accounting for relationships among variables. Multiple regression is described as a dependence method that uses one dependent variable and multiple independent variables. The document provides details on additive and multiplicative regression models, the matrix form of multiple regression equations, and assumptions of the technique. It also outlines how to interpret multiple regression output, including significance of slope coefficients and the adjusted R-squared statistic.

project planning demand Estimation II.pptx

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.

Lecture - 8 MLR.pptx

Multiple linear regression allows modeling of relationships between a dependent variable and multiple independent variables. It estimates the coefficients (betas) that best fit the data to a linear equation. The ordinary least squares method is commonly used to estimate the betas by minimizing the sum of squared residuals. Diagnostics include checking overall model significance with F-tests, individual variable significance with t-tests, and detecting multicollinearity. Qualitative variables require preprocessing with dummy variables before inclusion in a regression model.

Class 15 control action and controllers

The document discusses various types of controllers used in process control systems. It describes two-position or on-off controllers that have two output states of fully on or fully off. These controllers can exhibit cycling behavior as the process variable oscillates around the setpoint. Multi-position controllers are also covered, which have more than two output levels to help reduce cycling. The document provides examples of how different controller types respond based on the error between the measured process variable and desired setpoint.

Control charts

The document discusses control charts and run charts. Control charts were first developed by Walter Shewhart in 1924 to monitor process stability and control. They distinguish between common cause and special cause variation. Run charts plot process data over time to detect trends or shifts. They have seven steps: select a measure, gather minimum 10 data points, make a graph with vertical and horizontal axes, plot the data chronologically, and add a center line. Both charts aim to only address non-random variation warranting process improvement actions.

Gradient Boosting Regression Analysis Reveals Dependent Variables and Interre...

Gradient Boosting Regression Analysis Reveals Dependent Variables and Interre...Smarten Augmented Analytics

This overview discusses the predictive analytical technique known as Gradient Boosting Regression, an analytical technique that explore the relationship between two or more variables (X, and Y). Its analytical output identifies important factors ( Xi ) impacting the dependent variable (y) and the nature of the relationship between each of these factors and the dependent variable. Gradient Boosting Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. The Gradient Boosting Regression technique is useful in many applications, e.g., targeted sales strategies by using appropriate predictors to ensure accuracy of marketing campaigns and clarify relationships among factors such as seasonality, product pricing and product promotions, or for an agriculture business attempting to ascertain the effects of temperature, rainfall and humidity on crop production. Gradient Boosting Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.Design of experiments using Moldflow Analysis.

The document discusses using Design of Experiments (DOE) in Moldflow to analyze injection molding processes. It describes different types of DOE analyses including Taguchi screening analysis, factorial analysis, and Taguchi followed by factorial analysis. Input parameters for DOE in Moldflow include mold temperature, melt temperature, injection time, and others. Reasons to perform DOE include optimizing part thickness and cycle time. The document provides steps to conduct each type of analysis and interpret their results.

What is the Paired Sample T Test and How is it Beneficial to Business Analysis?

What is the Paired Sample T Test and How is it Beneficial to Business Analysis?Smarten Augmented Analytics

The Paired Sample T Test is used to determine whether the mean of a dependent variable. For example, weight, anxiety level, salary, or reaction time is the same in two related groups. It is particularly useful in measuring results before and after a particular event, action, process change, etc.Statistical process control

This document provides information about statistical process control (SPC) from Dr. Rick Edgeman, a professor and chair of statistics. It discusses using SPC to monitor and improve processes over time through the use of control charts, which distinguish normal variation from abnormal causes. Control charts can be used to monitor variables, attributes, proportions, and patterns over sequential time periods to help processes perform consistently.

LECTURE 1. Control Systems Engineering_MEB 4101.pdf

This document provides an overview of the course "Control Systems Engineering". It discusses key topics that will be covered, including control systems terminology and definitions, modeling and performance, dynamic response, stability criteria and analysis, feedback control system analysis and design, practical aspects of control systems, and measuring systems. The course content is divided into 7 modules that will cover these essential control systems engineering concepts and applications. Students will be continuously assessed and have an end of semester exam.

Ch-4: Measurement systems and basic concepts of measurement methods

This document provides an introduction and overview of measurement systems and concepts. It discusses:
- The basic components of a generalized measurement system, including sensing, conversion, manipulation, processing, transmission and presentation stages.
- Key definitions and concepts in measurement like accuracy, error, calibration, threshold, sensitivity and hysteresis.
- Classification schemes for transducers based on factors like the physical phenomenon, power type, output type and electrical phenomenon.
- Types of transducers like active vs passive, primary vs secondary, analog vs digital, and examples within resistive, capacitive, inductive and other categories.

1.pdf

This document discusses statistical analysis and provides definitions and examples. It defines statistical analysis as the process of collecting and analyzing large volumes of data to identify trends and develop insights. It then describes different types of statistical analysis, including descriptive analysis, inferential analysis, prescriptive analysis, predictive analysis, and causal analysis. The document emphasizes the importance of statistical analysis for businesses, researchers, politicians and more. It concludes by explaining some commonly used statistical analysis methods like standard deviation, hypothesis testing, mean, regression, and sample size determination.

Шимошенко Анастасія.pptx

Шимошенко Анастасія.pptxDepartment of Economics, Entrepreneurship and Business Administration, SumDU

This document discusses types of correlation relationships between phenomena. It describes functional relationships where a change in one attribute corresponds to a change in another attribute, and stochastic relationships where this correspondence is probabilistic rather than definitive. Correlation is defined as a stochastic relationship where the average value of one attribute changes with the average value of another. Relationships can be direct or reverse, linear or curvilinear, and involve one or multiple factors. Statistical methods like correlation analysis are used to study and quantify relationships established by theoretical analysis.Factor analysis (fa)

Here are the steps I would take to analyze this data using exploratory factor analysis:
1. Check assumptions
- Sample size of 300 is adequate
- Most correlations are between .3 and .8
2. Extract initial factors using principal axis factoring
- Kaiser's criterion suggests 4 factors with eigenvalues > 1
3. Rotate factors orthogonally using varimax rotation
- This will make the factor structure more interpretable
4. Interpret the factors based on which items have strong loadings
- Factor 1 relates to anxiety about learning SPSS
- Factor 2 relates to anxiety about using computers
- Factors 3 and 4 may reflect other aspects of statistics anxiety
5. Compute factor scores if desired to use in further

Types of models

Classification of mathematical modeling,
Classification based on Variation of Independent Variables,
Static Model,
Dynamic Model,
Rigid or Deterministic Models,
Stochastic or Probabilistic Models,
Comparison Between Rigid and Stochastic Models

Regression Analysis.pptx

Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.

Regression Analysis Techniques.pptxLinear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.

Multiple Regression.ppt

This document discusses multiple regression analysis. It begins by introducing multiple regression as an extension of simple linear regression that allows for modeling relationships between a response variable and multiple explanatory variables. It then covers topics such as examining variable distributions, building regression models, estimating model parameters, and assessing overall model fit and significance of individual predictors. An example demonstrates using multiple regression to build a model for predicting cable television subscribers based on advertising rates, station power, number of local families, and number of competing stations.

4. regression analysis1

This document provides an overview of regression analysis, including:
- Regression analysis is used to study the relationship between variables and predict one variable from another. It can be linear or non-linear.
- Simple regression involves one independent and one dependent variable, while multiple regression involves two or more independent variables.
- The method of least squares is used to determine the regression equation that best fits the data by minimizing the sum of the squared residuals.

Managerial-Accounting-Cost-Estimation.pptx

This document discusses key concepts in managerial accounting related to cost estimation and classification. It covers:
1) Direct and indirect costs and how they are classified on financial statements as expired or unexpired costs.
2) The composition of manufacturing costs including prime costs, conversion costs, and period costs.
3) Basic cost behavior patterns including variable, fixed, mixed, and step costs and how they react to changes in activity.
4) Methods for separating mixed costs into fixed and variable components such as the high-low method and scatterplot method.
5) Key terms like cost predictors, cost drivers, and overhead cost allocation.

BRM-lecture-11.ppt

This document discusses correlation analysis and regression analysis. It begins by defining correlation as a measure of how two variables vary together. A positive correlation means the variables increase or decrease together, while a negative correlation means one variable increases as the other decreases. Regression analysis investigates the relationship between a dependent variable and one or more independent variables. An example is provided to illustrate calculating a correlation coefficient and testing hypotheses about relationships between variables using a regression model. Key terms discussed include the Pearson correlation coefficient, coefficient of determination, t-statistic, and developing a conceptual model for multiple regression analysis.

Msa presentation

This document discusses measurement system analysis (MSA), which is used to evaluate statistical properties of process measurement systems. MSA determines if current measurement systems provide representative, unbiased and minimal variability measurements. The document outlines the MSA process, including preparing for a study, evaluating stability, accuracy, precision, linearity, and repeatability and reproducibility. Accuracy looks at bias while precision considers repeatability and reproducibility. MSA is required for certification and helps identify process variation sources and minimize defects.

10685 6.1 multivar_mr_srm bm i

This document discusses multivariate techniques, specifically multiple regression. It defines multivariate techniques as those that analyze more than two variables simultaneously, accounting for relationships among variables. Multiple regression is described as a dependence method that uses one dependent variable and multiple independent variables. The document provides details on additive and multiplicative regression models, the matrix form of multiple regression equations, and assumptions of the technique. It also outlines how to interpret multiple regression output, including significance of slope coefficients and the adjusted R-squared statistic.

project planning demand Estimation II.pptx

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.

Lecture - 8 MLR.pptx

Multiple linear regression allows modeling of relationships between a dependent variable and multiple independent variables. It estimates the coefficients (betas) that best fit the data to a linear equation. The ordinary least squares method is commonly used to estimate the betas by minimizing the sum of squared residuals. Diagnostics include checking overall model significance with F-tests, individual variable significance with t-tests, and detecting multicollinearity. Qualitative variables require preprocessing with dummy variables before inclusion in a regression model.

Class 15 control action and controllers

The document discusses various types of controllers used in process control systems. It describes two-position or on-off controllers that have two output states of fully on or fully off. These controllers can exhibit cycling behavior as the process variable oscillates around the setpoint. Multi-position controllers are also covered, which have more than two output levels to help reduce cycling. The document provides examples of how different controller types respond based on the error between the measured process variable and desired setpoint.

Control charts

The document discusses control charts and run charts. Control charts were first developed by Walter Shewhart in 1924 to monitor process stability and control. They distinguish between common cause and special cause variation. Run charts plot process data over time to detect trends or shifts. They have seven steps: select a measure, gather minimum 10 data points, make a graph with vertical and horizontal axes, plot the data chronologically, and add a center line. Both charts aim to only address non-random variation warranting process improvement actions.

Gradient Boosting Regression Analysis Reveals Dependent Variables and Interre...

Gradient Boosting Regression Analysis Reveals Dependent Variables and Interre...Smarten Augmented Analytics

This overview discusses the predictive analytical technique known as Gradient Boosting Regression, an analytical technique that explore the relationship between two or more variables (X, and Y). Its analytical output identifies important factors ( Xi ) impacting the dependent variable (y) and the nature of the relationship between each of these factors and the dependent variable. Gradient Boosting Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. The Gradient Boosting Regression technique is useful in many applications, e.g., targeted sales strategies by using appropriate predictors to ensure accuracy of marketing campaigns and clarify relationships among factors such as seasonality, product pricing and product promotions, or for an agriculture business attempting to ascertain the effects of temperature, rainfall and humidity on crop production. Gradient Boosting Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.Design of experiments using Moldflow Analysis.

The document discusses using Design of Experiments (DOE) in Moldflow to analyze injection molding processes. It describes different types of DOE analyses including Taguchi screening analysis, factorial analysis, and Taguchi followed by factorial analysis. Input parameters for DOE in Moldflow include mold temperature, melt temperature, injection time, and others. Reasons to perform DOE include optimizing part thickness and cycle time. The document provides steps to conduct each type of analysis and interpret their results.

What is the Paired Sample T Test and How is it Beneficial to Business Analysis?

What is the Paired Sample T Test and How is it Beneficial to Business Analysis?Smarten Augmented Analytics

The Paired Sample T Test is used to determine whether the mean of a dependent variable. For example, weight, anxiety level, salary, or reaction time is the same in two related groups. It is particularly useful in measuring results before and after a particular event, action, process change, etc.Statistical process control

This document provides information about statistical process control (SPC) from Dr. Rick Edgeman, a professor and chair of statistics. It discusses using SPC to monitor and improve processes over time through the use of control charts, which distinguish normal variation from abnormal causes. Control charts can be used to monitor variables, attributes, proportions, and patterns over sequential time periods to help processes perform consistently.

LECTURE 1. Control Systems Engineering_MEB 4101.pdf

This document provides an overview of the course "Control Systems Engineering". It discusses key topics that will be covered, including control systems terminology and definitions, modeling and performance, dynamic response, stability criteria and analysis, feedback control system analysis and design, practical aspects of control systems, and measuring systems. The course content is divided into 7 modules that will cover these essential control systems engineering concepts and applications. Students will be continuously assessed and have an end of semester exam.

Ch-4: Measurement systems and basic concepts of measurement methods

This document provides an introduction and overview of measurement systems and concepts. It discusses:
- The basic components of a generalized measurement system, including sensing, conversion, manipulation, processing, transmission and presentation stages.
- Key definitions and concepts in measurement like accuracy, error, calibration, threshold, sensitivity and hysteresis.
- Classification schemes for transducers based on factors like the physical phenomenon, power type, output type and electrical phenomenon.
- Types of transducers like active vs passive, primary vs secondary, analog vs digital, and examples within resistive, capacitive, inductive and other categories.

Factor analysis (fa)

Factor analysis (fa)

Types of models

Types of models

Regression Analysis.pptx

Regression Analysis.pptx

Regression Analysis Techniques.pptx

Regression Analysis Techniques.pptx

Multiple Regression.ppt

Multiple Regression.ppt

4. regression analysis1

4. regression analysis1

Managerial-Accounting-Cost-Estimation.pptx

Managerial-Accounting-Cost-Estimation.pptx

BRM-lecture-11.ppt

BRM-lecture-11.ppt

Msa presentation

Msa presentation

10685 6.1 multivar_mr_srm bm i

10685 6.1 multivar_mr_srm bm i

project planning demand Estimation II.pptx

project planning demand Estimation II.pptx

Lecture - 8 MLR.pptx

Lecture - 8 MLR.pptx

Class 15 control action and controllers

Class 15 control action and controllers

Control charts

Control charts

Gradient Boosting Regression Analysis Reveals Dependent Variables and Interre...

Gradient Boosting Regression Analysis Reveals Dependent Variables and Interre...

Design of experiments using Moldflow Analysis.

Design of experiments using Moldflow Analysis.

What is the Paired Sample T Test and How is it Beneficial to Business Analysis?

What is the Paired Sample T Test and How is it Beneficial to Business Analysis?

Statistical process control

Statistical process control

LECTURE 1. Control Systems Engineering_MEB 4101.pdf

LECTURE 1. Control Systems Engineering_MEB 4101.pdf

Ch-4: Measurement systems and basic concepts of measurement methods

Ch-4: Measurement systems and basic concepts of measurement methods

1.pdf

This document discusses statistical analysis and provides definitions and examples. It defines statistical analysis as the process of collecting and analyzing large volumes of data to identify trends and develop insights. It then describes different types of statistical analysis, including descriptive analysis, inferential analysis, prescriptive analysis, predictive analysis, and causal analysis. The document emphasizes the importance of statistical analysis for businesses, researchers, politicians and more. It concludes by explaining some commonly used statistical analysis methods like standard deviation, hypothesis testing, mean, regression, and sample size determination.

Шимошенко Анастасія.pptx

Шимошенко Анастасія.pptxDepartment of Economics, Entrepreneurship and Business Administration, SumDU

This document discusses types of correlation relationships between phenomena. It describes functional relationships where a change in one attribute corresponds to a change in another attribute, and stochastic relationships where this correspondence is probabilistic rather than definitive. Correlation is defined as a stochastic relationship where the average value of one attribute changes with the average value of another. Relationships can be direct or reverse, linear or curvilinear, and involve one or multiple factors. Statistical methods like correlation analysis are used to study and quantify relationships established by theoretical analysis.усик марина.pdf

This document discusses the subject and method of statistics. It defines statistics as the systematic collection, processing, and dissemination of quantitative data on social phenomena. Statistics has three main categories: 1) statistical aggregates, which are large groups of connected social elements or phenomena, 2) units of the aggregate, which are individual elements that make up the aggregate, and 3) attributes, which are measurable properties of the units. Attributes can be qualitative, quantitative, or descriptive. Statistical patterns include dynamics over time, distributions of population characteristics, structural shifts in populations, and interconnections between variables. The law of large numbers states that statistical patterns are more clear and complete when more population units are observed.

Трофимович Валерія.pdf

Statistics plays a vital role in many aspects of human life by helping collect, analyze, and interpret crucial data for decision making. In economics, statistics helps measure growth, forecast trends, and evaluate policies to inform decisions around investments, pricing, and resource allocation. It also helps identify economic inequalities to develop targeted policies that reduce poverty and promote development. Understanding basic statistical concepts, making predictions from data, and critical thinking are important skills for interpreting statistics across fields like business, science, and medicine.

Темченко Євгенія.pptx

Statistical methods can help forecast market trends. Regression analysis and time series analysis are two effective statistical methods. Regression analysis examines the relationship between independent variables, like advertising costs, and dependent variables, like sales. Time series analysis studies indicators over time using methods like exponential smoothing and ARIMA models. No single method is best - their effectiveness depends on the situation and available data. Combining methods can improve forecasts.

Таценко Олеся.pdf

Statistics are used in marketing in several ways: to identify market trends and measure the success of marketing programs, provide demographic and competitor data to inform target markets, track customer satisfaction and brand loyalty over time, and analyze household parameters to enable targeted promotions and cross-selling opportunities. Statistical analysis helps marketers accurately identify target audiences and evaluate the effectiveness of their marketing strategies.

Сурело В. презентація.pdf

Сурело В. презентація.pdfDepartment of Economics, Entrepreneurship and Business Administration, SumDU

Statistics is the science of studying the size, dimensions, and quantitative aspects of mass social phenomena. It has its roots in the Latin word "status" meaning position or state. The scientific system of statistics consists of statistical theory, methodology, and various branches. Statistical theory is the general study of dimensions of social phenomena and indicators. Methodology develops methods for collecting, analyzing, and studying relationships in statistical data. The main branches are mathematical statistics, general theory of statistics, social statistics, economic statistics, and industry statistics. Each branch has a focus area such as studying quantitative social phenomena, principles of statistical science, or indicators of production processes.Сокол Людмила.pdf

This document discusses seasonal fluctuations in economic indicators and their importance for businesses. Seasonal fluctuations refer to changes that occur at different times of the year, usually due to weather, holidays, or other seasonal events. Measuring seasonal fluctuations involves statistical analysis and understanding factors that influence specific variables. Understanding seasonal trends allows businesses to optimize strategies and investors to make informed decisions. Industries like retail, travel, agriculture, and energy experience fluctuations that research can help businesses and investors anticipate.

Рудень Ліліана.pptx

Statistics originated thousands of years ago in ancient China and Rome where populations were counted. It gradually developed for government management purposes like determining military forces and taxable lands. The term "statistics" was introduced as a science in 1746 by German scientist Gottfried Achenwall who proposed replacing the name of a course taught at universities with "Statistics". Statistics has a long history of collecting census and survey data in countries like Ukraine where censuses of populations and properties have been conducted since ancient times.

Рубан Аліна.pptx

Statistics is the science of collecting, analyzing, and presenting empirical data. It aims to provide reliable information about socioeconomic phenomena and processes to administrative bodies and society through research, identification of relationships, and forecasting of trends. Statistical methods involve mass observation, compilation, calculation of generalizing indicators, and interpretation, and are used across economics, marketing, management, and other social sciences by applying tools from mathematics and probability theory.

Постоєнко Тетяна.pdf

From personalized health to personalized learning, statistical challenges include developing subgroup analysis, dynamic treatment regimes, algorithmic fairness, postselection inference, causal inference for large and networked data, and addressing emerging data types and adversarial machine learning. Statistical approaches must account for complex real-world data to ensure valid, interpretable and replicable results that can guide high-stake decisions and policy recommendations.

Паливода Єгор.pptx

The document discusses the importance of being careful with statistics. It emphasizes verifying sources, checking for biases, understanding context, and questioning assumptions when evaluating statistical claims. Being statistically savvy helps prevent the spread of misinformation and allows people to make more informed decisions based on reliable data. It requires taking the time to thoroughly analyze numbers and dig deeper beyond surface-level interpretations.

Мамаєва Карина.pdf

The document discusses absolute and relative values. Absolute values are specific numbers that can have different measurement units depending on the phenomenon, while relative values express the ratio of absolute values compared. Relative values are calculated using an ordinary fraction of the value being compared over the base of comparison. Relative values can then be expressed as coefficients, percentages, per mille, or prodecimal depending on the numerator and denominator values.

Голик Аліна.pdf

The document discusses different methods and techniques for sampling in research. It describes the statistical approach to sample formation which uses random selection from the general population to ensure objectivity. An applied approach directly selects representatives according to criteria. Common sampling methods include proportional, class, and stratified sampling, each with advantages and disadvantages depending on the study. Sample size estimation considers factors like confidence level and group size, using formulas like Cochrane and Taylor. Proper sample formation is important for reliable and objective results.

Віталіна Ніколаєва.pptx

This document discusses methods of presenting statistical data, including statistical tables, diagrams, and references. It describes the T score table and Z score table as the two most commonly used statistical tables. The T score table was discovered by William Sealy Gosset and deals with situations where the population standard deviation is unknown. The Z score table indicates how many standard deviations above or below the mean a data point is. Diagrams are also used to show how variables are related, with common types being line graphs, bar graphs, histograms, and pie charts.

Вєтрова А..pdf

This document discusses different types of data collection methods. It describes primary data collection as gathering original data through surveys, interviews, or experiments, while secondary data collection uses previously collected data. The document also distinguishes between qualitative research, which generates textual data through methods like interviews, and quantitative research, which produces numerical data through surveys and experiments. Finally, it outlines different approaches to structured versus unstructured observation in data collection.

Богомаз Карина.pptx

A statistical summary is the process of organizing and analyzing primary statistical data to identify typical features, patterns, and relationships. The main goal is to derive generalized statistical indicators that characterize phenomena and their common traits. A summary can be simple, involving only totals, or complex, dividing data into groups and calculating results for each. It can also be centralized, with all raw data processed centrally, or decentralized, where initial processing is done locally and results combined progressively up the chain. Key aspects of a compilation program include determining grouping variables, metrics to calculate, table layouts, and data coding for computer processing.

Андрухова Діана.pdf

A statistical graph visually represents socio-economic data using geometric shapes and lines. It makes large amounts of data easier to understand by creating images of phenomena and allowing viewers to immediately spot trends and relationships. Graphs serve illustrative, control, and analytical functions, being used to characterize phenomena over time and space, study connections between variables, and compare statistical values. They must accurately reflect source data, be clear and easy to understand, and have an artistic design when possible.

Аналіз виробництва та реалізації продукції.pptx

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https://econ.biem.sumdu.edu.ua/History of statistics.pdf

History of statistics.pdfDepartment of Economics, Entrepreneurship and Business Administration, SumDU

The document discusses the history and importance of statistics. It begins by explaining the etymology of the word "statistics," which is derived from Italian and Latin terms relating to the analysis of state data. It then discusses how statistics today involves the collection and distribution of data across many fields using mathematical theories of probability and statistical inference. The document concludes by highlighting some early achievements in statistics, including the development of human statistical and census methods in the 17th century and the introduction of graphical representation and the English term "statistics" in the 18th century.1.pdf

1.pdf

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Таценко Олеся.pdf

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Сурело В. презентація.pdf

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Сокол Людмила.pdf

Рудень Ліліана.pptx

Рудень Ліліана.pptx

Рубан Аліна.pptx

Рубан Аліна.pptx

Постоєнко Тетяна.pdf

Постоєнко Тетяна.pdf

Паливода Єгор.pptx

Паливода Єгор.pptx

Мамаєва Карина.pdf

Мамаєва Карина.pdf

Голик Аліна.pdf

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Віталіна Ніколаєва.pptx

Віталіна Ніколаєва.pptx

Вєтрова А..pdf

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Богомаз Карина.pptx

Богомаз Карина.pptx

Андрухова Діана.pdf

Андрухова Діана.pdf

Аналіз виробництва та реалізації продукції.pptx

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History of statistics.pdf

History of statistics.pdf

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DEMAND AND SUPPLY.docx Notes for Economics

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Seminar: Gender Board Diversity through Ownership Networks

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Independent Study - College of Wooster Research (2023-2024)

"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.

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你做代理，可以改变自己，改变他人，给他人和自己一个机会大块就啃啃得满嘴满脸猴屁股般的红艳大家一个劲地指着对方吃吃地笑瓜裂得古怪奇形怪状却丝毫不影响瓜味甜丝丝的满嘴生津遍地都是瓜横七竖八的活像掷满了一地的大石块摘走二三只爷爷是断然发现不了的即便发现爷爷也不恼反而教山娃辨认孰熟孰嫩孰甜孰淡名义上是护瓜往往在瓜棚里坐上一刻饱吃一顿后山娃就领着阿黑漫山遍野地跑阿黑是一条黑色的大猎狗挺机灵的是山娃多年的忠实伙伴平时山娃上学阿黑也摇头晃脑地跟去暑假用不着上学阿钩

Who Is Abhay Bhutada, MD of Poonawalla Fincorp

Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.

1.2 Business Ideas Business Ideas Busine

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一比一原版(GWU,GW毕业证)加利福尼亚大学|尔湾分校毕业证如何办理

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国外毕业证学位证成绩单如何办理：
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How will new technology fields affect economic trade?

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Sources of Revenue for State Government - Prof Oyedokun.pptx

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Lecture slide on Sources of Revenue for State GovernmentTdasx: Unveiling the Trillion-Dollar Potential of Bitcoin DeFi

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在线办理(GU毕业证书)美国贡萨加大学毕业证学历证书一模一样

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Well-crafted financial reports serve as vital tools for decision-making and transparency within an organization. By following the undermentioned tips, you can create standardized financial reports that effectively communicate your company's financial health and performance to stakeholders.

Eco-Innovations and Firm Heterogeneity.Evidence from Italian Family and Nonf...

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- 1. 2.5. Methods of measuring the influence of factors in deterministic analysis
- 2. • In deterministic analysis to determine the influence of factors on performance indicators, the following methods are used: chain substitutions, absolute differences, relative differences, index, proportional division and so on. • The first four methods are based on the principle of elimination.
- 3. • Eliminate - means to take away the influence of all factors on the value of the performance indicator, except one. • This is based on the conditional assumption that all factors change independently of each other: first one changes, and all others remain unchanged, then changes the second, third, etc., provided that the others remain unchanged.
- 4. • The method of chain substitutions is used to calculate the influence of factors in all types of deterministic factor models: additive, multiplicative, multiple and combined (mixed). • This method allows to determine the influence of individual factors on the change in the value of the performance indicator by gradually replacing the base value of each factor indicator in the volume of the performance indicator on the actual value in the reporting period.
- 5. Using the method of chain substitutions, you need to follow certain rules that determine the sequence of calculation: • 1) first of all quantitative factors are subject to replacement, further - structural, last of all - qualitative; • 2) if the model is presented by several quantitative, structural or qualitative indicators, the sequence of substitutions is determined by logical analysis. • 3) provided that the influence of a certain factor is not determined, take its base value, ie the one with which it is compared, and if determined, take the actual value - the one that is compared; • 4) the number of calculated conditional indicators is one less than the factors in the model.
- 6. • The mathematical description of the method of chain substitutions when using it, for example, in three-factor multiplicative models, can be as follows. • Three-factor multiplicative model ; c b a Y де Y – результативний показник; Yо – базисний рівень результативного показника; Y1 – звітний рівень результативного показника; а, b – кількісні показники; а – первинний щодо показника b; с – якісний показник.
- 7. • The first stage. To apply the method of chain substitutions, it is necessary to give the formula for calculating the performance indicator in the sequence corresponding to the order of substitutions, and to determine the basic level of the performance indicator: • The second stage. To calculate the conditional performance indicators, the base values are consistently replaced by the reported ones. ; 0 0 0 0 c b a Y 𝑌ум1 = 𝑎1 ⋅ 𝑏0 ⋅ 𝑐0; 𝑌ум2 = 𝑎1 ⋅ 𝑏1 ⋅ 𝑐0; ; 1 1 1 1 c b a Y
- 8. • The third stage. To calculate the impact of each factor, follow these steps: • 1) the influence of factor a on the change in the performance of Y: • Yа= Yум1 -Y0; • 2) the influence of factor b on the change in the performance indicator Y: • Yb= Yум2-Yум1 ; • 3) the influence of factor c on the change in the performance indicator Y: • Yс= Y1 - Yум2.
- 9. • The fourth stage. To check the correctness of the calculations you need to determine the balance of deviations: • Y1 - Y0 = Yа + Yb + Yс. • Advantages: universality of application (for all types of models); ease of use. • Disadvantages: depending on the chosen order of replacement of factors, the results of factor decomposition have different meanings.
- 10. • The method of absolute differences is also based on the method of elimination. Used in models of multiplicative and mixed type • The rule of calculation using this method is that the magnitude of the influence of factors is calculated by multiplying the absolute increase of the studied factor by the base value of the factors in the model to his right and the actual value of the factors to his left. • Consider the order of analytical calculations on the example of a three-factor multiplicative model: ) ( c b a Y ; c b a Y
- 11. • The first stage. To apply the method of absolute differences, it is necessary to submit the formula for calculating the performance indicator in the sequence that corresponds to the order of substitutions, and determine the base level of the performance indicator: • The second stage. Determine the absolute deviations for each factor: • а = а1 - а0; b = b1 – b0 ;c = с1- с0. ; 0 0 0 0 c b a Y
- 12. • The third stage. Calculate the change in the value of the performance indicator by changing each factor: • The fourth stage. To verify the correctness of the calculations calculate the balance of deviations: • Y1 - Y0 = Yа + Yb + Yс. ; 0 0 c b a Yà ; 0 1 c b a Yb . 1 1 c b a Yc (2.29)
- 13. • Therefore, when applying the method of absolute differences, the calculation is based on the sequential replacement of the basic values of the indicators by their absolute deviation, and then on the actual level of indicators.
- 14. • The method of relative differences, as well as the method of absolute, is used for multiplicative models and models of mixed type • Consider the method of calculating the influence of factors for multiplicative models of the type • The first stage. To apply the method of relative differences, it is necessary to submit the formula for calculating the performance indicator in the sequence that corresponds to the order of substitutions, and determine the base level of the performance indicator: ) ( c b a Y . c b a Y 0 0 0 0 c b a Y
- 15. • The second stage. Calculate the relative deviations of each factor: • 𝛥𝑎 % = а1−а0 а0 ⋅ 100 %; • 𝛥с % = с1−с0 с0 ⋅ 100 % • The third stage. Determine the deviation of the performance indicator by changing each factor as follows: % 100 % 0 0 1 b b b b . 100 % 0 a Y Ya
- 16. • To calculate the impact of the first factor, the base value of the performance indicator must be multiplied by the relative increase of the first factor, expressed as a percentage, and the result divided by 100. • In order to calculate the influence of the second factor, it is necessary to add (subtract) the change from the first factor to the base value of the performance indicator, and then multiply the amount obtained by the relative increase of the second factor as a percentage and divide the result by 100. . 100 % ) ( 0 b Y Y Y a b
- 17. • To determine the impact of the third factor (and all the following) perform similar procedures: to the base value of the performance indicator must add its increase (decrease) due to the first and second factors and multiply the amount by the relative increase of the third factor. • 𝛥𝑌с = (𝑌0+𝛥𝑌𝑎+𝛥𝑌𝑏)⋅𝛥𝑐 % 100 . • The fourth stage. Check the correctness of the calculations - the balance of deviations: • Y1 - Y0 = Yа + Yb + Yс.
- 18. • In the factor analysis in additive models of the combined (mixed) type the method of proportional division can be used. The algorithm for calculating the influence of factors on changes in performance indicators for the additive model type will be as follows: c b a Y a c b a Y Ya b c b a Y Yb c c b a Y Yc ; ; .
- 19. • Index method. It is used to study economic phenomena that are formed under the influence of several factors, each of which is subject to dynamic change. • A classic example of such an object of analysis is the volume of sales (sales) of goods, which is formed under the influence of a certain physical volume of goods and their prices. Under such conditions, general (group) indices of sales (sales) of goods in the form • де q0 , q1 – базисні й звітні обсяги реалізованих товарів, p0 , p1 – базисні й звітні ціни на них, що характеризують динаміку загальної виручки від реалізації, але не відповідають на запитання, як змінився обсяг продажу товарів (тому що до чисельника і знаменника даної функції входять непорівнянні величини) чи як у середньому змінилися ціни на реалізовані товари. 0 0 1 1 p q p q iз
- 20. • The general index does not allow to single out the influence of sales volume factors (quantitative factor) and prices (qualitative factor) on the final result - sales revenue. • The index method of analysis allows you to solve these problems by constructing aggregate indices. • Aggregate indices are general indices (which, as already mentioned, characterize the phenomena determined by a set of directly incomparable elements), in which in order to eliminate the influence of individual elements (factors) on the index is fixing other elements at a constant (baseline or reporting) level.
- 21. • a) aggregate index of physical sales: • b) aggregate index of prices for products of enterprises: 0 0 0 1 p q p q iф 0 1 1 1 p q p q iц
- 22. • The general theory of statistics uses the following rule of constructing aggregate indices: qualitative (intensive) elements (factors) included in the formula are fixed at the level of the base period, quantitative elements - at the reporting level. • The difference between the numerator and the denominator of the aggregate index shows the impact on the overall result had a particular factor.