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Econometrics Final Project

Aleksey Narko
II year Management
Econometrics Final Project
I took the data set about the wealth of nations and in particular the dependence between the population and total wealth of the country (nation).
Source: http://data.worldbank.org/data-catalog/wealth-of-nations
2011 WSB-NLU
Professor: Jacek Leskow

Econometrics Project

The study examines the effect of inflation, investment, life expectancy and literacy rate on per capita GDP across 20 countries using ordinary least squares regression. Initially, the regression results show inflation, investment and literacy rate have a negative effect, while life expectancy has a positive effect on per capita GDP. Sri Lanka, USA and Japan are identified as potential outliers based on their high residuals. Running the regression after removing these outliers improves the model fit and explanatory power of the variables. Diagnostic tests find no evidence of misspecification or heteroskedasticity, validating the OLS estimates.

econometrics project PG1 2015-16

This document provides a summary of a time series analysis of real GDP and the share of agriculture and allied sectors in India. It includes an acknowledgment, abstract, introduction on time series analysis and econometric theory. It also discusses the importance of stationary stochastic processes, difference stationary versus trend stationary processes, and the unit root test for determining stationarity. The overall summary is that the document examines the relationship between total Indian GDP and agriculture GDP using time series analysis and unit root tests on annual data from 1954-2013.

ECONOMETRICS PROJECT PG2 2015

This document presents a simultaneous equation system analyzing the labor market. It acknowledges that some economic variables are jointly determined rather than having a strictly unidirectional relationship. The system includes two equations: a labor supply equation relating hours to average wage and other factors, and a labor demand equation relating quantity demanded to average wage and factor costs. These equations represent the behavior of workers and employers in aggregate and are solved in equilibrium when quantity supplied equals quantity demanded. Estimating either equation via OLS would be inconsistent since the wage is correlated with the error term. The system can be solved into reduced form equations showing that outcomes depend on exogenous variables and structural errors. Separate explanatory factors are needed in each equation to allow unique identification of parameters.

EC4417 Econometrics Project

This document describes a project analyzing the relationship between market share, R&D expenditure, and advertising in the technology industry. The project uses data from 30 technology companies to estimate a regression model relating market share to R&D and advertising expenditures. The results show that R&D expenditure has a statistically significant positive relationship with market share, while advertising expenditure is not statistically significant. Specifically, a 20% increase in R&D is estimated to increase market share by 0.613%, while the same increase in advertising only increases market share by 0.0796%, which is not statistically robust. The model fits the data well, with an R-squared value of 0.9096.

Heteroscedasticity

This document discusses heteroscedasticity, which occurs when the error variance is not constant. It provides examples of when the variance of errors may change, such as with income level or outliers. Graphical methods are presented for detecting heteroscedasticity by examining patterns in residual plots. Formal tests are also described, including the Park test which regresses the log of the squared residuals on explanatory variables, and the Glejser test which regresses the absolute value of residuals on variables related to the error variance. Detection of heteroscedasticity is important as it violates assumptions of the classical linear regression model.

Granger Causality

This document discusses Granger causality and how to test for it. It provides the following key points:
1) Granger causality measures whether variable A occurs before variable B and helps predict B, but does not guarantee true causality. If A does not Granger cause B, one can be more confident A does not cause B.
2) To test for Granger causality, autoregressive models are developed with and without the variable being tested, and an F-test or t-test is used to see if adding the variable significantly lowers the residuals.
3) The document applies this to test if changes in loans Granger cause changes in deposits using quarterly U.S. financial

Heteroscedasticity

Heteroscedasticity occurs when the variance of the error terms in a regression model are not constant, but instead vary depending on the values of the independent variables. While ordinary least squares estimators remain unbiased, their standard errors may be incorrect under heteroscedasticity. This means that confidence intervals and hypothesis tests based on the usual standard errors are unreliable and can lead to misleading conclusions.

Econometrics Final Project

Aleksey Narko
II year Management
Econometrics Final Project
I took the data set about the wealth of nations and in particular the dependence between the population and total wealth of the country (nation).
Source: http://data.worldbank.org/data-catalog/wealth-of-nations
2011 WSB-NLU
Professor: Jacek Leskow

Econometrics Project

The study examines the effect of inflation, investment, life expectancy and literacy rate on per capita GDP across 20 countries using ordinary least squares regression. Initially, the regression results show inflation, investment and literacy rate have a negative effect, while life expectancy has a positive effect on per capita GDP. Sri Lanka, USA and Japan are identified as potential outliers based on their high residuals. Running the regression after removing these outliers improves the model fit and explanatory power of the variables. Diagnostic tests find no evidence of misspecification or heteroskedasticity, validating the OLS estimates.

econometrics project PG1 2015-16

This document provides a summary of a time series analysis of real GDP and the share of agriculture and allied sectors in India. It includes an acknowledgment, abstract, introduction on time series analysis and econometric theory. It also discusses the importance of stationary stochastic processes, difference stationary versus trend stationary processes, and the unit root test for determining stationarity. The overall summary is that the document examines the relationship between total Indian GDP and agriculture GDP using time series analysis and unit root tests on annual data from 1954-2013.

ECONOMETRICS PROJECT PG2 2015

This document presents a simultaneous equation system analyzing the labor market. It acknowledges that some economic variables are jointly determined rather than having a strictly unidirectional relationship. The system includes two equations: a labor supply equation relating hours to average wage and other factors, and a labor demand equation relating quantity demanded to average wage and factor costs. These equations represent the behavior of workers and employers in aggregate and are solved in equilibrium when quantity supplied equals quantity demanded. Estimating either equation via OLS would be inconsistent since the wage is correlated with the error term. The system can be solved into reduced form equations showing that outcomes depend on exogenous variables and structural errors. Separate explanatory factors are needed in each equation to allow unique identification of parameters.

EC4417 Econometrics Project

This document describes a project analyzing the relationship between market share, R&D expenditure, and advertising in the technology industry. The project uses data from 30 technology companies to estimate a regression model relating market share to R&D and advertising expenditures. The results show that R&D expenditure has a statistically significant positive relationship with market share, while advertising expenditure is not statistically significant. Specifically, a 20% increase in R&D is estimated to increase market share by 0.613%, while the same increase in advertising only increases market share by 0.0796%, which is not statistically robust. The model fits the data well, with an R-squared value of 0.9096.

Heteroscedasticity

This document discusses heteroscedasticity, which occurs when the error variance is not constant. It provides examples of when the variance of errors may change, such as with income level or outliers. Graphical methods are presented for detecting heteroscedasticity by examining patterns in residual plots. Formal tests are also described, including the Park test which regresses the log of the squared residuals on explanatory variables, and the Glejser test which regresses the absolute value of residuals on variables related to the error variance. Detection of heteroscedasticity is important as it violates assumptions of the classical linear regression model.

Granger Causality

This document discusses Granger causality and how to test for it. It provides the following key points:
1) Granger causality measures whether variable A occurs before variable B and helps predict B, but does not guarantee true causality. If A does not Granger cause B, one can be more confident A does not cause B.
2) To test for Granger causality, autoregressive models are developed with and without the variable being tested, and an F-test or t-test is used to see if adding the variable significantly lowers the residuals.
3) The document applies this to test if changes in loans Granger cause changes in deposits using quarterly U.S. financial

Heteroscedasticity

Heteroscedasticity occurs when the variance of the error terms in a regression model are not constant, but instead vary depending on the values of the independent variables. While ordinary least squares estimators remain unbiased, their standard errors may be incorrect under heteroscedasticity. This means that confidence intervals and hypothesis tests based on the usual standard errors are unreliable and can lead to misleading conclusions.

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.

Auto Correlation Presentation

This document discusses autocorrelation in time series data and its effects on regression analysis. It defines autocorrelation as errors in one time period carrying over into future periods. Autocorrelation can be caused by factors like inertia in economic cycles, specification bias, lags, and nonstationarity. While OLS estimators remain unbiased with autocorrelation, they become inefficient and hypothesis tests are invalid. Autocorrelation can be detected using graphical analysis or formal tests like the Durbin-Watson test and Breusch-Godfrey test. The Cochrane-Orcutt procedure is also described as a way to transform data and remove autocorrelation.

Econometrics project final edited

This document analyzes factors that affect the prices of red wine. It establishes a regression model with the log price of red wine as the dependent variable, and average winter rainfall, seasonal temperature, and harvest rainfall as independent variables. The model explains 74% of variation in price. Hypothesis testing shows that all three weather factors significantly impact price individually and overall. Specifically, higher winter rainfall and temperature increase price, while more harvest rain decreases price. The analysis concludes weather is a major determinant of red wine prices and quality between vintages.

Autocorrelation (1)

Autocorrelation measures the correlation of a time series with its past and lagged values. It exists when observations in a time series are correlated with each other. The Durbin-Watson test can detect the presence of autocorrelation by examining the residuals of a regression model. If autocorrelation is present, it violates the assumption that errors are independent and leads to inaccurate test statistics and predictions. Common structures for autocorrelation include autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes.

Autocorrelation- Concept, Causes and Consequences

Autocorrelation occurs when errors in a time series model are correlated across time periods, violating the assumption of independent errors. This can be caused by inertia in the data, omitted variables, incorrect functional form, or data manipulation. Consequences of ignoring autocorrelation in OLS models include inefficient estimators, biased error variances, and unreliable t-tests, F-tests, and R-squared values. Autocorrelation can be identified through patterns in the error terms and estimated using measures like sample autocorrelation.

Econometrics project

The document analyzes the relationship between stock market performance and economic growth in the U.S. from 1980-2011. It finds a strong positive correlation between changes in the Dow Jones Industrial Average and nominal GDP. Regression analysis shows stock market fluctuations explained about 87% of the variation in GDP. The results suggest stock prices can influence economic activity by affecting business confidence, financing, and household wealth. Therefore, large declines in stock prices may precede and prolong economic downturns.

Correlation and Regression Analysis using SPSS and Microsoft Excel

This document discusses correlation and linear regression analysis. It covers correlation coefficients, linear relationships between variables, assumptions of linear regression, and using SPSS and Excel to conduct correlation and regression analyses. Pearson and Spearman correlation coefficients are introduced as measures of the linear association between two continuous variables. Simple and multiple linear regression models are explained as tools to predict an outcome variable from one or more predictor variables.

Heteroscedasticity

The document discusses heteroscedasticity, which occurs when the variance of the error term is not constant. It defines heteroscedasticity and provides potential causes, such as errors increasing with an independent variable or model misspecification. Consequences are that OLS estimates are no longer BLUE and standard errors are biased. Several tests for detecting heteroscedasticity are outlined, including Park, Glejser, Spearman rank correlation, and Goldfeld-Quandt tests. The Goldfeld-Quandt test involves dividing data into groups and comparing regression sum of squares to test if error variance differs between groups.

R square vs adjusted r square

R-squared measures how well a linear regression model fits the data, but it will always increase or stay the same as more variables are added, even if they don't improve the model. Adjusted R-squared accounts for the number of predictors and is designed to penalize extra variables. It will only increase if a new variable significantly improves the model fit. The key differences are that adjusted R-squared deals better with additional variables and prevents overfitting, while R-squared is biased towards higher values as more predictors are included.

Multicolinearity

The document discusses multicollinearity in regression analysis. It defines multicollinearity as a statistical phenomenon where two or more predictor variables are highly correlated. The presence of multicollinearity can cause problems with estimating coefficients and interpreting results. The document outlines symptoms of multicollinearity, causes, consequences, detection methods, and remedial measures to address multicollinearity issues.

Specification Errors | Eonomics

Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx

Factor analysis in Spss

Factor analysis is a statistical technique used to reduce the dimensionality of a set of correlated variables by identifying underlying factors. It seeks to explain the variance between observed variables in terms of a smaller number of latent factors. The document describes how factor analysis works, including that it begins with a correlation matrix and aims to group highly correlated variables together into factors while variables with low correlations are separated into different factors. Factor analysis can help provide a clearer understanding of the relationships in a dataset and enable subsequent analyses using the identified factors.

Autocorrelation

This document discusses autocorrelation, which occurs when there is a correlation between members of a series of observed data ordered over time or space. This violates an assumption of classical linear regression that error terms are uncorrelated. Causes of autocorrelation include inertia in macroeconomic data, specification bias from excluded or incorrectly specified variables, lags, data manipulation, and non-stationarity of time series data. Autocorrelation can be detected graphically or using the Durbin-Watson and Breusch-Godfrey tests. Remedial measures include first-difference transformation, generalized transformation, and using Newey-West standard errors.

Econometrics Final Project

The document summarizes an econometrics project analyzing the effect of advanced degrees on income for white male economics graduates in the United States. Regression analyses found:
1) On average, men with an advanced degree earn $48,256 more per year than those with just a bachelor's degree.
2) When controlling for age, marriage, children and work hours, men with an advanced degree earn $11,124-$24,000 more annually.
3) Both earnings and the advanced degree earnings premium increase with age but at a diminishing rate, with peak earnings around age 44 for those with an advanced degree and 43 for bachelor's degree holders.

Multicollinearity PPT

This document discusses multicollinearity in regression analysis. It defines multicollinearity as an exact or near-exact linear relationship between explanatory variables. In cases of perfect multicollinearity, individual regression coefficients cannot be estimated. Near or imperfect multicollinearity is more common in real data and can lead to less precise coefficient estimates with wider confidence intervals. The document discusses various methods for detecting multicollinearity, such as auxiliary regressions and variance inflation factors, and potential remedies like dropping or transforming variables. However, multicollinearity diagnosis depends on the specific data sample and goals of the analysis.

Multicollinearity

This document discusses multicollinearity in econometrics. Multicollinearity occurs when there is a near-perfect linear relationship among independent variables. It can lead to unstable parameter estimates and high standard errors. Symptoms include high standard errors, unexpected parameter signs or magnitudes, and jointly significant but individually insignificant variables. Diagnosis involves examining variable correlations and testing joint significance. The variance inflation factor (VIF) measures the impact of multicollinearity, with values above 2 indicating a potential problem. Remedies include acquiring more data, dropping problematic variables, or reformulating the model, though these can introduce new issues. Multicollinearity alone does not invalidate estimates.

Measurement of seasonal variations

Learning material on Measurement of Seasonal variations prepared in accordance to VTU I Sem MBA syllabus for the subject Business Statistics & Analytics

Autocorrelation

1) Autocorrelation refers to correlation between members of a time series or cross-sectional data set ordered by time or space.
2) In a time series, successive errors are often correlated, violating the assumption of independent errors in a linear regression model.
3) Autocorrelation occurs when there is correlation between a variable and its own past or lagged values, while serial correlation refers to correlation between two different time series.

Introduction to Econometrics

Econometrics combines economic theory, mathematics, statistics, and economic data to empirically test economic relationships and quantify economic models. It involves stating an economic theory, specifying the mathematical and econometric models, obtaining data, estimating model parameters, testing hypotheses, forecasting, and using models for policy purposes. The econometrician adds a stochastic error term to account for uncertainty from omitted variables, data limitations, intrinsic randomness, and incorrect model specification. Econometrics aims to numerically measure relationships posited by economic theories.

Chapter 08

1. The document discusses sampling methods and the central limit theorem. It describes various probability sampling methods like simple random sampling, systematic random sampling, and stratified random sampling.
2. It defines the sampling distribution of the sample mean and explains that according to the central limit theorem, the sampling distribution will follow a normal distribution as long as the sample size is large.
3. The mean of the sampling distribution is equal to the population mean, and its variance is equal to the population variance divided by the sample size. This allows probabilities to be determined about a sample mean falling within a certain range.

Applications of regression analysis - Measurement of validity of relationship

This document provides a summary of regression analysis in 9 steps: 1) Specify dependent and independent variables, 2) Check for linearity with scatter plots, 3) Transform variables if nonlinear, 4) Estimate the regression model, 5) Test the model fit with R2, 6) Perform a joint hypothesis test of the coefficients, 7) Test individual coefficients, 8) Check for violations of assumptions like autocorrelation and heteroscedasticity, 9) Interpret the intercept and slope coefficients. Regression analysis is used to determine relationships between variables and estimate how changes in independents impact dependents.

X18136931 statistics ca2_updated

Multiple regression was used to analyze the relationship between marriage age variables and a marriage index variable. The regression showed the marriage age variables together can predict 93% of the variability in the marriage index. Specifically, it found that marriage ages 35-39 and 50-54 have a significant negative and positive effect, respectively, on the marriage index. Binary logistic regression was then used to predict life expectancy based on gender and tobacco consumption. The model showed tobacco consumption and gender can significantly predict life expectancy, with the model fitting the data well.

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.

Auto Correlation Presentation

This document discusses autocorrelation in time series data and its effects on regression analysis. It defines autocorrelation as errors in one time period carrying over into future periods. Autocorrelation can be caused by factors like inertia in economic cycles, specification bias, lags, and nonstationarity. While OLS estimators remain unbiased with autocorrelation, they become inefficient and hypothesis tests are invalid. Autocorrelation can be detected using graphical analysis or formal tests like the Durbin-Watson test and Breusch-Godfrey test. The Cochrane-Orcutt procedure is also described as a way to transform data and remove autocorrelation.

Econometrics project final edited

This document analyzes factors that affect the prices of red wine. It establishes a regression model with the log price of red wine as the dependent variable, and average winter rainfall, seasonal temperature, and harvest rainfall as independent variables. The model explains 74% of variation in price. Hypothesis testing shows that all three weather factors significantly impact price individually and overall. Specifically, higher winter rainfall and temperature increase price, while more harvest rain decreases price. The analysis concludes weather is a major determinant of red wine prices and quality between vintages.

Autocorrelation (1)

Autocorrelation measures the correlation of a time series with its past and lagged values. It exists when observations in a time series are correlated with each other. The Durbin-Watson test can detect the presence of autocorrelation by examining the residuals of a regression model. If autocorrelation is present, it violates the assumption that errors are independent and leads to inaccurate test statistics and predictions. Common structures for autocorrelation include autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes.

Autocorrelation- Concept, Causes and Consequences

Autocorrelation occurs when errors in a time series model are correlated across time periods, violating the assumption of independent errors. This can be caused by inertia in the data, omitted variables, incorrect functional form, or data manipulation. Consequences of ignoring autocorrelation in OLS models include inefficient estimators, biased error variances, and unreliable t-tests, F-tests, and R-squared values. Autocorrelation can be identified through patterns in the error terms and estimated using measures like sample autocorrelation.

Econometrics project

The document analyzes the relationship between stock market performance and economic growth in the U.S. from 1980-2011. It finds a strong positive correlation between changes in the Dow Jones Industrial Average and nominal GDP. Regression analysis shows stock market fluctuations explained about 87% of the variation in GDP. The results suggest stock prices can influence economic activity by affecting business confidence, financing, and household wealth. Therefore, large declines in stock prices may precede and prolong economic downturns.

Correlation and Regression Analysis using SPSS and Microsoft Excel

This document discusses correlation and linear regression analysis. It covers correlation coefficients, linear relationships between variables, assumptions of linear regression, and using SPSS and Excel to conduct correlation and regression analyses. Pearson and Spearman correlation coefficients are introduced as measures of the linear association between two continuous variables. Simple and multiple linear regression models are explained as tools to predict an outcome variable from one or more predictor variables.

Heteroscedasticity

The document discusses heteroscedasticity, which occurs when the variance of the error term is not constant. It defines heteroscedasticity and provides potential causes, such as errors increasing with an independent variable or model misspecification. Consequences are that OLS estimates are no longer BLUE and standard errors are biased. Several tests for detecting heteroscedasticity are outlined, including Park, Glejser, Spearman rank correlation, and Goldfeld-Quandt tests. The Goldfeld-Quandt test involves dividing data into groups and comparing regression sum of squares to test if error variance differs between groups.

R square vs adjusted r square

R-squared measures how well a linear regression model fits the data, but it will always increase or stay the same as more variables are added, even if they don't improve the model. Adjusted R-squared accounts for the number of predictors and is designed to penalize extra variables. It will only increase if a new variable significantly improves the model fit. The key differences are that adjusted R-squared deals better with additional variables and prevents overfitting, while R-squared is biased towards higher values as more predictors are included.

Multicolinearity

The document discusses multicollinearity in regression analysis. It defines multicollinearity as a statistical phenomenon where two or more predictor variables are highly correlated. The presence of multicollinearity can cause problems with estimating coefficients and interpreting results. The document outlines symptoms of multicollinearity, causes, consequences, detection methods, and remedial measures to address multicollinearity issues.

Specification Errors | Eonomics

Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx

Factor analysis in Spss

Factor analysis is a statistical technique used to reduce the dimensionality of a set of correlated variables by identifying underlying factors. It seeks to explain the variance between observed variables in terms of a smaller number of latent factors. The document describes how factor analysis works, including that it begins with a correlation matrix and aims to group highly correlated variables together into factors while variables with low correlations are separated into different factors. Factor analysis can help provide a clearer understanding of the relationships in a dataset and enable subsequent analyses using the identified factors.

Autocorrelation

This document discusses autocorrelation, which occurs when there is a correlation between members of a series of observed data ordered over time or space. This violates an assumption of classical linear regression that error terms are uncorrelated. Causes of autocorrelation include inertia in macroeconomic data, specification bias from excluded or incorrectly specified variables, lags, data manipulation, and non-stationarity of time series data. Autocorrelation can be detected graphically or using the Durbin-Watson and Breusch-Godfrey tests. Remedial measures include first-difference transformation, generalized transformation, and using Newey-West standard errors.

Econometrics Final Project

The document summarizes an econometrics project analyzing the effect of advanced degrees on income for white male economics graduates in the United States. Regression analyses found:
1) On average, men with an advanced degree earn $48,256 more per year than those with just a bachelor's degree.
2) When controlling for age, marriage, children and work hours, men with an advanced degree earn $11,124-$24,000 more annually.
3) Both earnings and the advanced degree earnings premium increase with age but at a diminishing rate, with peak earnings around age 44 for those with an advanced degree and 43 for bachelor's degree holders.

Multicollinearity PPT

This document discusses multicollinearity in regression analysis. It defines multicollinearity as an exact or near-exact linear relationship between explanatory variables. In cases of perfect multicollinearity, individual regression coefficients cannot be estimated. Near or imperfect multicollinearity is more common in real data and can lead to less precise coefficient estimates with wider confidence intervals. The document discusses various methods for detecting multicollinearity, such as auxiliary regressions and variance inflation factors, and potential remedies like dropping or transforming variables. However, multicollinearity diagnosis depends on the specific data sample and goals of the analysis.

Multicollinearity

This document discusses multicollinearity in econometrics. Multicollinearity occurs when there is a near-perfect linear relationship among independent variables. It can lead to unstable parameter estimates and high standard errors. Symptoms include high standard errors, unexpected parameter signs or magnitudes, and jointly significant but individually insignificant variables. Diagnosis involves examining variable correlations and testing joint significance. The variance inflation factor (VIF) measures the impact of multicollinearity, with values above 2 indicating a potential problem. Remedies include acquiring more data, dropping problematic variables, or reformulating the model, though these can introduce new issues. Multicollinearity alone does not invalidate estimates.

Measurement of seasonal variations

Learning material on Measurement of Seasonal variations prepared in accordance to VTU I Sem MBA syllabus for the subject Business Statistics & Analytics

Autocorrelation

1) Autocorrelation refers to correlation between members of a time series or cross-sectional data set ordered by time or space.
2) In a time series, successive errors are often correlated, violating the assumption of independent errors in a linear regression model.
3) Autocorrelation occurs when there is correlation between a variable and its own past or lagged values, while serial correlation refers to correlation between two different time series.

Introduction to Econometrics

Econometrics combines economic theory, mathematics, statistics, and economic data to empirically test economic relationships and quantify economic models. It involves stating an economic theory, specifying the mathematical and econometric models, obtaining data, estimating model parameters, testing hypotheses, forecasting, and using models for policy purposes. The econometrician adds a stochastic error term to account for uncertainty from omitted variables, data limitations, intrinsic randomness, and incorrect model specification. Econometrics aims to numerically measure relationships posited by economic theories.

Chapter 08

1. The document discusses sampling methods and the central limit theorem. It describes various probability sampling methods like simple random sampling, systematic random sampling, and stratified random sampling.
2. It defines the sampling distribution of the sample mean and explains that according to the central limit theorem, the sampling distribution will follow a normal distribution as long as the sample size is large.
3. The mean of the sampling distribution is equal to the population mean, and its variance is equal to the population variance divided by the sample size. This allows probabilities to be determined about a sample mean falling within a certain range.

Chapter 16

Chapter 16

Auto Correlation Presentation

Auto Correlation Presentation

Econometrics project final edited

Econometrics project final edited

Autocorrelation (1)

Autocorrelation (1)

Autocorrelation- Concept, Causes and Consequences

Autocorrelation- Concept, Causes and Consequences

Econometrics project

Econometrics project

Correlation and Regression Analysis using SPSS and Microsoft Excel

Correlation and Regression Analysis using SPSS and Microsoft Excel

Heteroscedasticity

Heteroscedasticity

R square vs adjusted r square

R square vs adjusted r square

Multicolinearity

Multicolinearity

Specification Errors | Eonomics

Specification Errors | Eonomics

Factor analysis in Spss

Factor analysis in Spss

Autocorrelation

Autocorrelation

Econometrics Final Project

Econometrics Final Project

Multicollinearity PPT

Multicollinearity PPT

Multicollinearity

Multicollinearity

Measurement of seasonal variations

Measurement of seasonal variations

Autocorrelation

Autocorrelation

Introduction to Econometrics

Introduction to Econometrics

Chapter 08

Chapter 08

Applications of regression analysis - Measurement of validity of relationship

This document provides a summary of regression analysis in 9 steps: 1) Specify dependent and independent variables, 2) Check for linearity with scatter plots, 3) Transform variables if nonlinear, 4) Estimate the regression model, 5) Test the model fit with R2, 6) Perform a joint hypothesis test of the coefficients, 7) Test individual coefficients, 8) Check for violations of assumptions like autocorrelation and heteroscedasticity, 9) Interpret the intercept and slope coefficients. Regression analysis is used to determine relationships between variables and estimate how changes in independents impact dependents.

X18136931 statistics ca2_updated

Multiple regression was used to analyze the relationship between marriage age variables and a marriage index variable. The regression showed the marriage age variables together can predict 93% of the variability in the marriage index. Specifically, it found that marriage ages 35-39 and 50-54 have a significant negative and positive effect, respectively, on the marriage index. Binary logistic regression was then used to predict life expectancy based on gender and tobacco consumption. The model showed tobacco consumption and gender can significantly predict life expectancy, with the model fitting the data well.

Group5

This document is an empirical assignment report submitted by a group of students analyzing the relationship between urbanization, transportation, GDP, and carbon dioxide emissions across 209 countries. The report finds that:
1) Carbon dioxide emission levels in a country can be significantly explained by its levels of urbanization and vehicle density, with higher levels of both associated with higher CO2 emissions.
2) The model used satisfies assumptions of classical linear regression, and urbanization and vehicle density jointly explain over 50% of the variation in CO2 emissions levels.
3) GDP per capita is also likely to influence CO2 emissions but is excluded from the main model due to multicollinearity with urbanization and vehicle density.

Regression and Classification Analysis

The document is a project submission sheet for a student named Yash Balaji Iyengar. It includes details of the student's program of study, the module and lecturer, as well as information about the project such as the title "Statistics Continuous Assessment 2", word count of 1718, and due date of April 7, 2019. The student certifies that all work is their own or properly cited. Instructions are provided for project submission.

Multiple Linear Regression Applications Automobile Pricing

This document describes using multiple linear regression to predict automobile prices. The response variable is price from Kelley Blue Book for 470 cars. Potential explanatory variables are mileage, make, type, liter size, cruise control, upgraded speakers, and leather seats. Preliminary analysis finds mileage and liter have significant correlations with price. The final regression model finds price is best predicted by an equation involving liter size and mileage as the most important factors. The model explains over 80% of price variation and provides a way for buyers and sellers to estimate reasonable car prices.

Stats ca report_18180485

The document describes applying multiple linear regression and logistic regression analyses to predict life expectancy using various predictor variables. For multiple linear regression, the model explained 68.9% of variance in life expectancy. Only pollution (pm25) and universal health coverage (uhc) were statistically significant. For logistic regression, the model correctly predicted life expectancy binary outcome for 79.7% of cases, with only uhc and pm25 as significant predictors. Model diagnostics and evaluations indicated both models satisfied assumptions and were good fits for the data.

Marketing Engineering Notes

8
The document provides an overview of marketing engineering and response models. It discusses linear regression models, which assume a linear relationship between dependent and independent variables. Key points include:
1) Linear regression finds coefficients that minimize error between actual and predicted dependent variable values.
2) Diagnostics include R-squared, standard error, and ANOVA tables comparing explained, residual, and total variation.
3) Models can forecast sales and profits given marketing mix changes.
4) Logit models are used when dependent variables are binary or limited ranges, predicting choice probabilities rather than continuous preferences.

X18145922 statistics ca2 final

This document summarizes two statistical analyses: multiple regression and binary logistic regression. For multiple regression, the author analyzed traffic data from New Zealand to predict average daily traffic using other traffic factors. Peak traffic rate and percentages of heavy vehicles significantly contributed to the model. For binary logistic regression, the author analyzed economic data from UN to predict if a country's growth rate increased or decreased based on employment in different sectors. The procedures and assumptions for both models are discussed.

Covariance and correlation

The document discusses covariance and correlation, which describe the relationship between two variables. Covariance indicates whether variables are positively or inversely related, while correlation also measures the degree of their relationship. A positive covariance/correlation means variables move in the same direction, while a negative covariance/correlation means they move in opposite directions. Correlation coefficients range from 1 to -1, with 1 indicating a perfect positive correlation and -1 a perfect inverse correlation. The document provides formulas for calculating covariance and correlation and examples to demonstrate their use.

Correlation 2

PPT uploaded tells us about the correlation and regression and also shown us about the different types, etc..

Quantity Demand Analysis

This document discusses regression analysis techniques for estimating relationships between variables. It provides examples of using single and multiple regression to model how dependent variables, like income, are impacted by independent variables, such as education levels and population density. Key outputs from regression analyses like the model summary, ANOVA table, and coefficients are also presented to interpret the results and significance of relationships.

Logistic regression and analysis using statistical information

1. Logistic regression allows prediction of a nominal dependent variable with two categories, extending traditional regression which is limited to continuous dependent variables.
2. The model fits by maximizing the likelihood of predicting category membership rather than minimizing errors like linear regression.
3. The analysis of a dataset with variables like family size and mortgage payment predicted participation in a solar panel program with 90% accuracy, showing logistic regression can successfully predict categorical outcomes.

Ch14 multiple regression

The multiple regression equation to predict January heating costs based on mean outside temperature, inches of insulation, and furnace age is:
Estimated Heating Cost = 82.57 + 1.23(Temperature) - 1.39(Insulation) + 1.11(Furnace Age)
Positive regression coefficients indicate higher values of those variables increase heating costs, while negative coefficients decrease costs. The intercept of 82.57 is the estimated cost when all independent variables equal zero. Plugging values into the equation estimates a heating cost of $107.52 for a home with a furnace 10 years old, 5 inches of insulation, and mean temperature of 30 degrees.

Consumer Spending causing unemployment analysis

The tested causal hypothesis is whether change in Consumer Spending causes Unemployment [rate] or vice versa...
This presentation details the steps to demonstrate causality using Granger Causality, Path Analysis, and narrative tests.

REG.pptx

This document discusses correlation, regression, and the least squares method. It defines correlation as the degree of relationship between two variables and identifies positive, negative, and zero correlations. Regression analysis determines the best fit line to predict the relationship between a dependent and independent variable. Linear regression predicts the relationship between quantitative variables, while logistic regression predicts probabilities for discrete outcomes. The least squares method determines the best fit line by minimizing the distance between the data points and the line. Real-life examples are provided to illustrate applications in business, agriculture, medicine, and investments.

Multivariate data analysis regression, cluster and factor analysis on spss

Using multiple techniques to analyse data on SPSS. A basic software that can easily help run the numbers. Multivariate Data Analysis runs regressions models, factor analyses, and clustering models apart from many more

elasticityThe ratio of the percentagechange in a depende.docx

elasticity
The ratio of the percentage
change in a dependent
variable to a percentage
change in an independent
variable.
C H A P T E R 5
Elasticity: A Measure of
Response
START UP: RAISE FARES? LOWER FARES? WHAT’S A
PUBLIC TRANSIT MANAGER TO DO?
Imagine that you are the manager of the public transportation system for a large metropolitan area. Operating
costs for the system have soared in the last few years, and you are under pressure to boost revenues. What do you
do?
An obvious choice would be to raise fares. That will make your customers angry, but at least it will generate the
extra revenue you need—or will it? The law of demand says that raising fares will reduce the number of passengers
riding on your system. If the number of passengers falls only a little, then the higher fares that your remaining pas-
sengers are paying might produce the higher revenues you need. But what if the number of passengers falls by so
much that your higher fares actually reduce your revenues? If that happens, you will have made your customers
mad and your financial problem worse!
Maybe you should recommend lower fares. After all, the law of demand also says that lower fares will increase
the number of passengers. Having more people use the public transportation system could more than offset a
lower fare you collect from each person. But it might not. What will you do?
Your job and the fiscal health of the public transit system are riding on your making the correct decision. To do
so, you need to know just how responsive the quantity demanded is to a price change. You need a measure of
responsiveness.
Economists use a measure of responsiveness called elasticity. Elasticity is the ratio of the percentage change
in a dependent variable to a percentage change in an independent variable. If the dependent variable is y, and the
independent variable is x, then the elasticity of y with respect to a change in x is given by:
ey, x =
% change in y
% change in x
A variable such as y is said to be more elastic (responsive) if the percentage change in y is large relative to the per-
centage change in x. It is less elastic if the reverse is true.
As manager of the public transit system, for example, you will want to know how responsive the number of
passengers on your system (the dependent variable) will be to a change in fares (the independent variable). The
concept of elasticity will help you solve your public transit pricing problem and a great many other issues in eco-
nomics. We will examine several elasticities in this chapter—all will tell us how responsive one variable is to a
change in another.
© 2018 Boston Academic Publishing, Inc., d.b.a. FlatWorld. All rights reserved.
Created exclusively for Marvin McKenzie <[email protected]>
price elasticity of demand
The percentage change in
quantity demanded of a
particular good or service
divided by the percentage
change in the price of that
good or service, all other
things unchanged.
1 ...

Multinomial Logistic Regression.pdf

- Multinomial logistic regression predicts categorical membership in a dependent variable based on multiple independent variables. It is an extension of binary logistic regression that allows for more than two categories.
- Careful data analysis including checking for outliers and multicollinearity is important. A minimum sample size of 10 cases per independent variable is recommended.
- Multinomial logistic regression does not assume normality, linearity or homoscedasticity like discriminant function analysis does, making it more flexible and commonly used. It does assume independence between dependent variable categories.

X18125514 ca2-statisticsfor dataanalytics

4. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS

Demand Estimation

1. The document outlines the process of estimating demand functions using statistical techniques, including identifying variables, collecting data, specifying models, and estimating parameters.
2. Linear and nonlinear models are discussed for relating dependent and independent variables, with the linear model being most common. Estimating techniques include ordinary least squares regression.
3. Regression results can be used to interpret relationships between variables and make predictions, though correlation does not necessarily imply causation. Testing procedures evaluate the model fit and significance of relationships.

Applications of regression analysis - Measurement of validity of relationship

Applications of regression analysis - Measurement of validity of relationship

X18136931 statistics ca2_updated

X18136931 statistics ca2_updated

Group5

Group5

Regression and Classification Analysis

Regression and Classification Analysis

Multiple Linear Regression Applications Automobile Pricing

Multiple Linear Regression Applications Automobile Pricing

Stats ca report_18180485

Stats ca report_18180485

Marketing Engineering Notes

Marketing Engineering Notes

X18145922 statistics ca2 final

X18145922 statistics ca2 final

Covariance and correlation

Covariance and correlation

Correlation 2

Correlation 2

Quantity Demand Analysis

Quantity Demand Analysis

Logistic regression and analysis using statistical information

Logistic regression and analysis using statistical information

Ch14 multiple regression

Ch14 multiple regression

Consumer Spending causing unemployment analysis

Consumer Spending causing unemployment analysis

REG.pptx

REG.pptx

Multivariate data analysis regression, cluster and factor analysis on spss

Multivariate data analysis regression, cluster and factor analysis on spss

elasticityThe ratio of the percentagechange in a depende.docx

elasticityThe ratio of the percentagechange in a depende.docx

Multinomial Logistic Regression.pdf

Multinomial Logistic Regression.pdf

X18125514 ca2-statisticsfor dataanalytics

X18125514 ca2-statisticsfor dataanalytics

Demand Estimation

Demand Estimation

原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理

原版制作【微信:41543339】【利兹贝克特大学毕业证(LeedsBeckett毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理

毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Experts live - Improving user adoption with AI

Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.

Open Source Contributions to Postgres: The Basics POSETTE 2024

Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.

End-to-end pipeline agility - Berlin Buzzwords 2024

We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.

一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理

毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM

06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink

Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf

Guia de Aprendizagem Globlal

Population Growth in Bataan: The effects of population growth around rural pl...

A population analysis specific to Bataan.

A presentation that explain the Power BI Licensing

Power BI Licensing

The Building Blocks of QuestDB, a Time Series Database

Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.

ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake

Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake

STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...

"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."

一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理

毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx

A note on Networking

DSSML24_tspann_CodelessGenerativeAIPipelines

Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge

Global Situational Awareness of A.I. and where its headed

You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.

原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样

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如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
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◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

Learn SQL from basic queries to Advance queries

Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics

原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理

原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理

一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理

一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理

Experts live - Improving user adoption with AI

Experts live - Improving user adoption with AI

Open Source Contributions to Postgres: The Basics POSETTE 2024

Open Source Contributions to Postgres: The Basics POSETTE 2024

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End-to-end pipeline agility - Berlin Buzzwords 2024

一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理

一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理

Palo Alto Cortex XDR presentation .......

Palo Alto Cortex XDR presentation .......

06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM

06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM

Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf

Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf

Population Growth in Bataan: The effects of population growth around rural pl...

Population Growth in Bataan: The effects of population growth around rural pl...

A presentation that explain the Power BI Licensing

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The Building Blocks of QuestDB, a Time Series Database

The Building Blocks of QuestDB, a Time Series Database

ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake

ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake

STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...

STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...

一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理

一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理

DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx

DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx

DSSML24_tspann_CodelessGenerativeAIPipelines

DSSML24_tspann_CodelessGenerativeAIPipelines

Global Situational Awareness of A.I. and where its headed

Global Situational Awareness of A.I. and where its headed

原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样

原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样

Learn SQL from basic queries to Advance queries

Learn SQL from basic queries to Advance queries

- 1. U.S. GASOLINE PRICE MARKET 1953-2004 ECONOMETRICS PROJECT PRESENTED BY:- 1) SAKSHI ARORA 2) SIMRAN TANWAR 3) SHUBHAM JOON 4) GAURISH KANT SHUKLA
- 2. INTRODUCTION The objective of the project is to study the relationship between Gasoline Expenditure, Gasoline Price Index, Per capita disposable income, Price Index for new cars, Price index for old cars and price index for public transports in the US. There may be other factors which may affect the Gasoline Expenditure but the factors taken here represent the expenditure of gasoline reasonably.
- 3. METHODOLOGY The model taken here is multivariate i.e. it has more than two variables. We have used Multiple linear regression technique. Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). The variables we are using to predict the value of the dependent variable are called the independent variables (or sometimes, the predictor, explanatory or repressor variables). Multiple regression analysis is applied here to study the relationship between the dependent variable and all the factors involved. The data taken into consideration is a time series data (1953- 2004).
- 4. DATA SOURCING The data taken into consideration (U.S. Gasoline market) is a time series data (1953-2004). We collected it from a very reliable source. It was compiled by Prof. Chris Bell, Department of Economics, University of North Carolina, Asheville. www.bea.gov and www.bls.gov
- 5. VARIABLES . The variables we considered for the gasoline market are defined below: GasExp- Total gasoline expenditure in U.S. in billions of dollars (dependent variable) Gasp-Price Index for gasoline. Income- per capita Disposable Income PNC- Price index for new cars Independent variables PUC- Price index for used cars PPT- Price Index for public transportation.
- 6. REGRESSION STATISTICS We fit the regression model to our data and the following results are witnessed: These are the “Goodness of Fit” measures. They tell you how well the calculated linear regression equation fits your data. The coefficient of determination of the model comes out to be 0.996 i.e. 99.6% of the variations in the gasoline expenditure are explained by the factors taken into consideration.
- 7. ANOVA TABLE The linear regression's F-test has the null hypothesis that there is no linear relationship between the variables Ho: Bo=B1=B2=B3=B4=B5=0 H1: at least one of the Bi’s are not 0.
- 8. Here, The significance value for F-Test is 0.000 which is less than 0.05 therefore we reject the null hypothesis which states that there is no linear relationship between the variables. Thus we can assume that there is a linear relationship between the variables in our model. Which also indicates that, overall, the regression model statistically, significantly predicts the outcome variable (i.e., it is a good fit for the data).
- 9. COEFFICIENTS The coefficients for every variable is statistically significant because their p- values are smaller than 0.05. So, the model becomes: IN MULTIPLE REGRESSION,EACH COEFFECIENT IS INTERPRETED AS THE ESTIMATED CHANGE IN Y CORRESPONDING TO A UNIT CHANGE IN A VARIABLE,WHEN ALL OTHER VARIALBES ARE HELD CONSTANT.
- 10. As per our a priori expectations we see that the coefficients of the regression are same as expected: We have the coefficient of gasoline price index positive as it is an price index it can have a positive relation with consumption and thus expenditure on gasoline. As the per capita income increases, so should the expenditure on gasoline. The coefficient obtained here depicts that with increasing income people tend to spend more on gasoline directly or indirectly. As the price index of new cars increases we witness negative relation between the gasoline expenditure and the price index which is as expected. The technology improvement in the new cars does not imply that the consumption will be low and indirectly the expenditure, even if it is true, the negative effect of price increase of new car on gasoline expenditure and the positive effect exist at the same time, but the former is much larger than the latter. Coefficient of price index of used cars is also negative with the meaning that the increasing price of used cars decrease the demand of used cars and thus decreasing the expenditure on gasoline. As the price index of public transportation increases, total gasoline expenditure should also increase, because cost of travelling in public transportation has become costlier. This is also depicted by the results.
- 11. AUTOCORRELATION A key assumption in regression is that the error terms are independent of each other. In this section, we present a simple test to determine whether there is autocorrelation i.e. whether there is a (linear) correlation between the error term for one observation and the next. Now , we will detect the presence of autocorrelation using Durbin Watson ‘s D test. The Durbin-Watson test uses the following statistic: Since most regression problems involving time series data show a positive autocorrelation, we usually test the null hypothesis H0: No autocorrelation ,versus the alternative hypothesis ,H1: ρ >0.
- 12. Using SPSS we calculated the Durbin Watson value and it came out to be: We checked the Durbin Watson table for 52 observations and k=6 and got dL=1.35124 and dU=1.76942 and the durbin-watson value from the above table is 0.733 which lies between 0-dL.
- 13. So from the graph above, we conclude that some positive autocorrelation is present. We can also remove this positive auto-correlation by using COCHRAN ORCUTT iterative method.
- 14. HOMOSCEDASTICITY It is the violation of the assumption of homoscedasticity ( equally spread variance) i.e. It is a problem of unequal variance of the error term. On plotting the standardized residuals(y-axis) against the standardized predicted values, we find that the error terms are evenly spread out over all values implying homogeneity in the data.
- 15. But to confirm this, we use Spearman Rank Correlation test to detect if there is heteroscedasticity in data or not. For that we have to calculate Unstandardized Predicted value and Unstandardized residual and then check the correlation between them. We usually test the null hypothesis H0: No heteroscedasticity versus the alternative hypothesis H1: heteroscedasticity is present. Here, the significant value is 0.388 which is greater than 0.05 therefore we accept the null hypothesis i.e. there is no heteroscedasticity present in the data. That means we can say that there is homoscedasticity in the data.
- 16. MULTICOLLINEARITY In statistics, Multicollinearity (also collinearity) is a phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy. In this situation the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data. We are checking collinearity statistics i.e. variance inflation factor (VIF) and tolerance level (ToL) to detect the presence of multicollinearity in our data.
- 17. The value of VIF > 2.5 indicates that Multicollinearity is present in the data. Here, every independent variable has VIF value greater than 2.5 that means there is STRONG MULTICOLLINEARITY present in the data. Also, the value of ToL < 0.4 indicates that Multicollinearity is present in the data. And here every variable has ToL value less than 0.4 indicating the presence of strong multicollinearity in the data.
- 18. REMOVAL OF MULTICOLLINEARITY We can remove multicollinearity from the data by following methods: Collecting additional data If there is multicollinearity present in the data, Add some more data(observations) to reduce/remove multicollinearity. Removing redundant variables Remove some redundant variables to remove/reduce multicollinearity. Combining variables Define another variable which is simply the combination of two variables which are causing multicollinearity.
- 19. CONCLUSION As discussed earlier The objective of the project is to study the relationship between Gasoline Expenditure, Gasoline Price Index, Income, Price Indices for new cars, old cars and public transport in the US. We calculated the measures of goodness of fit for the model, i.e. R = 0.998 , R2 = 0.996 , adjusted R square = 0.996 and standard error = 3.7758 for N = 52. From the ANOVA table, the statistical significance of the regression model i.e. our p value 0.000 ( which is less than 0.05), indicates that the overall regression model is statistically significant and predicts the dependent variable (i.e., it is a good fit for the data).
- 20. Finally, after running regression we tested for auto-correlation, heteroscedasticity and multi-collinearity in the model. We found that there is strong multicollinearity and positive auto-collinearity is present in the data. As multicollinearity is not a problem we may ignore it and consider all the variables to be important as they have a significant effect on the gasoline expenditure Heteroscedasticity is not present in the data so it does not affect our model in any case. Thus we conclude that our best (fitted) regression model is:
- 21. THANK YOU