'Simple regression' presentation slideshows

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Chapter 14

Chapter 14. Regression and Forecasting Models. Introduction. Many decision-making applications depend on a forecast of some quantity . Here are some examples:

914 views • 64 slides

Regulation of immunity in bronchiectasis and ABPA

Dr Rosemary Boyton Lung Immunology Group Molecular immunology of lung disease National Heart & Lung Institute & Royal Brompton Hospital Imperial College London UK. Regulation of immunity in bronchiectasis and ABPA. Bronchiectasis.

981 views • 36 slides

Appendix 2A. Simple regression and multiple regression

By Cheng Few Lee Joseph Finnerty John Lee Alice C Lee Donald Wort. Appendix 2A. Simple regression and multiple regression. Appendix 2A. Simple regression and multiple regression. 2. A.1 INTRODUCTION 2. A.2 SIMPLE REGRESSION Variance of Multiple Regression.

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Assimilating HealthMap Data to Nowcast Epidemics

J. Ray jairay [at] sandia [dot] gov Sandia National Laboratories, Livermore, CA Acknowledgements: The work was funded by DoD /NCMI SAND2012-9575P. Assimilating HealthMap Data to Nowcast Epidemics.

413 views • 27 slides

DEMAND FORECASTING TECHNIQUES

DEMAND FORECASTING TECHNIQUES. Qualitative & Quantitative . Outline. Introduction Demand Forecasting Forecasting Techniques Qualitative Methods Quantitative Methods Components of Time Series Data Time Series Forecasting Methods Forecast Accuracy Useful Forecasting Websites

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Regression Analysis

Regression Analysis. Chapter 10. Regression and Correlation.

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Why and how people use R

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Is Global Warming for Real?

Is Global Warming for Real?. J. C. Sprott Department of Physics University of Wisconsin - Madison Presented at the Chaos and Complex Systems Seminar In Madison, Wisconsin On January 17, 2006. Some Evidence. From Recent Seminars. Greenland Ice-core Data (C. S. Clay). 782,000 years.

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Quantitative data analysis

Quantitative data analysis. Module in research methods course for tourism program Reza Mortazavi 2014 Lecture 4. Relationship between variables. Correlation When two variables are linearly related (or covary ) we say they are correlated either positively or negatively.

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Time Series Data

Time Series Data. y t = b 0 + b 1 x t1 + . . .+ b k x tk + u t 2. Further Issues. Testing for AR(1) Serial Correlation. Want to be able to test for whether the errors are serially correlated or not

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Regression Analysis: Part 2

Regression Analysis: Part 2. Inference Dummies / Interactions Multicollinearity / Heteroscedasticity Residual Analysis / Outliers. Shoe Size Example. Consider the prediction of Shoe Size discussed before

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General Linear Model

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Bivariate &/vs. Multivariate

Bivariate &/vs. Multivariate . Differences between correlations, simple regression weights & multivariate regression weights Patterns of bivariate & multivariate effects Proxy variables Multiple regression results to remember.

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Regression Analysis

Regression Analysis. Modeling Relationships. Dependent Variable. Independent Variables. Regression Analysis. Regression Analysis is a study of the relationship between a set of independent variables and the dependent variable .

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Using SPSS for Simple Regression

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Testing for Unit Roots

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Alternatives to Sales Budgeting Process

Alternatives to Sales Budgeting Process. Sales budget. Production budget. Revenue budget. Sales and administrative expense budget. Direct labor materials and overhead budgets. Cost of goods sold budget. Budgeted profit and loss statement. Sales forecasts.

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Full example

Full example. Simple Regression. Data. Howell dataset Hassles Number of minor daily hassles reported during a month (measure of stress) Psychological symptomology. First descriptives. Ouch!. Pearson r = 0.61. Big difference!.

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Predicting Future

Predicting Future. Two Approaches to Predition. Extrapolation: Use past experiences for predicting future. One looks for patterns over time. Predictive models: Observed relationship between dependent and independent factors. Goodness of fit: estimated by analysis of residuals. .

236 views • 6 slides