'Standardized residuals' presentation slideshows

Outliers and influential data points

Outliers and influential data points. The distinction. An outlier is a data point whose response y does not follow the general trend of the rest of the data.

869 views • 68 slides

Outliers and influential data points

Outliers and influential data points. The distinction. An outlier is a data point whose response y does not follow the general trend of the rest of the data.

602 views • 46 slides

Outliers and influential data points

Outliers and influential data points. No outliers?. An outlier? Influential?. An outlier? Influential?. An outlier? Influential?. An outlier? Influential?. An outlier? Influential?. An outlier? Influential?. Impact on regression analyses.

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STAT 497 LECTURE NOTE 9

STAT 497 LECTURE NOTE 9. DIAGNOSTIC CHECKS. DIAGNOSTIC CHECKS. After identifying and estimating a time series model, the goodness-of-fit of the model and validity of the assumptions should be checked. If we have a perfect model fit, then we can construct the ARIMA forecasts.

517 views • 40 slides

Chi-square Test of Independence

Chi-square Test of Independence. Reviewing the Concept of Independence Steps in Testing Chi-square Test of Independence Hypotheses Chi-square Test of Independence in SPSS. Chi-square Test of Independence.

537 views • 40 slides

Academy of Economic Studies Doctoral School of Finance - DOFIN Exchange Rate Risk: Heads or Tails? MSc Student: ANA-MARI

Academy of Economic Studies Doctoral School of Finance - DOFIN Exchange Rate Risk: Heads or Tails? MSc Student: ANA-MARIA GAVRIL Supervisor: MOISA ALTAR, PhD Bucharest 2009. CONTENTS: Motivations for approaching and assessing exchange rate risk Objectives of the paper State of the art

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Non-continuous Relationships

Non-continuous Relationships. If the relationship between the dependent variable and an independent variable is non-continuous a slope dummy variable can be used to estimate two sets of coefficients and intercepts for the independent variable.

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BIOL 582

BIOL 582. Lecture Set 7 Tests for multiple groups: Part 3 One-Way ANOVA Assumptions, One-Way ANOVA Evaluation, Multiple Comparisons Tests, Data Transformations ,Contingency Tables. BIOL 582. Assumptions in ANOVA. A One-Way (Single Factor) ANOVA is appropriate for

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Measuring market risk: a copula and extreme value approach

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Multiple Causes of Food Insecurity: Multiple Regression Analysis

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Multiple Causes of Food Insecurity: Multiple Regression Analysis

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Forecasting CPI

Forecasting CPI. Xiang Huang , Wenjie Huang, Teng Wang , Hong Wang Benjamin Wright , Naiwen Chang, Jake Stamper. Definition. The consumer price index (CPI) measures the cost of a standard basket of goods and services commonly purchased by households.

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Conditions of applications

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Review of Lecture 13 Standard Regression Assumptions:

Lecture 14. Review of Lecture 13 Standard Regression Assumptions: a). about the form of the model b). about the measurement errors c). about the predictor variables d). about the observations

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Special Topics in Multiple Regression Analysis

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Residual Analysis for Data Considerations and LINE Assumptions

Residual Analysis for Data Considerations and LINE Assumptions. BUSA5325. Multiple Regression. Purposes of multiple regression Applications Model and OLS criterion Inferences Model building - variable selection Data considerations LINE assumptions. Summary Slide. Data Considerations

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Nonlinear Relationships

Nonlinear Relationships. Nonlinear relationships can be modeled by including a variable that is a nonlinear function of an independent variable. For example it is usually assumed that health care expenditures increase at an increasing rate as people age. Nonlinear Relationships.

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Regression Diagnostics - I

Regression Diagnostics - I. The three conditions required for the validity of the regression analysis are: the error variable is normally distributed. the error variance is constant for all values of x. The errors are independent of each other.

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Checking the Required Conditions

Checking the Required Conditions. Check these 3 boxes. Enlarge Residual Plot and visually check: 1) Are errors (residuals) centered around 0 -- checking if E( ?)= 0). 2) Is there any pattern to the magnitude of the errors -- checking if ? ? varies for different values of x.

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Regression Diagnostics

Regression Diagnostics. Fall 2013 Dec 12/13. Why Regression Diagnostics?.

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