1 Zinc Data EPP 245 Statistical Analysis of Laboratory Data.

Slides:



Advertisements
Similar presentations
Dummy Variables and Interactions. Dummy Variables What is the the relationship between the % of non-Swiss residents (IV) and discretionary social spending.
Advertisements

Sociology 601 Class 24: November 19, 2009 (partial) Review –regression results for spurious & intervening effects –care with sample sizes for comparing.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 1) Slideshow: exercise 1.16 Original citation: Dougherty, C. (2012) EC220 - Introduction.
From Anova to Regression: analyzing the effect on consumption of no. of persons in family Family consumption data family.dta E/Albert/Courses/cdas/appstat00/From.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 4) Slideshow: interactive explanatory variables Original citation: Dougherty, C. (2012)
Heteroskedasticity The Problem:
HETEROSCEDASTICITY-CONSISTENT STANDARD ERRORS 1 Heteroscedasticity causes OLS standard errors to be biased is finite samples. However it can be demonstrated.
1 Nonlinear Regression Functions (SW Chapter 8). 2 The TestScore – STR relation looks linear (maybe)…
Sociology 601, Class17: October 27, 2009 Linear relationships. A & F, chapter 9.1 Least squares estimation. A & F 9.2 The linear regression model (9.3)
Christopher Dougherty EC220 - Introduction to econometrics (chapter 3) Slideshow: exercise 3.5 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Adaptive expectations and partial adjustment Presented by: Monika Tarsalewska Piotrek Jeżak Justyna Koper Magdalena Prędota.
Sociology 601 Class 21: November 10, 2009 Review –formulas for b and se(b) –stata regression commands & output Violations of Model Assumptions, and their.
Multiple Regression Spring Gore Likeability Example Suppose: –Gore’s* likeability is a function of Clinton’s likeability and not directly.
SPH 247 Statistical Analysis of Laboratory Data 1April 23, 2010SPH 247 Statistical Analysis of Laboratory Data.
Sociology 601 Class 25: November 24, 2009 Homework 9 Review –dummy variable example from ASR (finish) –regression results for dummy variables Quadratic.
Sociology 601 Class 28: December 8, 2009 Homework 10 Review –polynomials –interaction effects Logistic regressions –log odds as outcome –compared to linear.
1 Multiple Regression EPP 245/298 Statistical Analysis of Laboratory Data.
Regression Example Using Pop Quiz Data. Second Pop Quiz At my former school (Irvine), I gave a “pop quiz” to my econometrics students. The quiz consisted.
Introduction to Regression Analysis Straight lines, fitted values, residual values, sums of squares, relation to the analysis of variance.
Addressing Alternative Explanations: Multiple Regression Spring 2007.
1 Review of Correlation A correlation coefficient measures the strength of a linear relation between two measurement variables. The measure is based on.
1 Michigan.do. 2. * construct new variables;. gen mi=state==26;. * michigan dummy;. gen hike=month>=33;. * treatment period dummy;. gen treatment=hike*mi;
Sociology 601 Class 23: November 17, 2009 Homework #8 Review –spurious, intervening, & interactions effects –stata regression commands & output F-tests.
A trial of incentives to attend adult literacy classes Carole Torgerson, Greg Brooks, Jeremy Miles, David Torgerson Classes randomised to incentive or.
1 Analysis of Variance (ANOVA) EPP 245 Statistical Analysis of Laboratory Data.
Interpreting Bi-variate OLS Regression
1 Regression and Calibration EPP 245 Statistical Analysis of Laboratory Data.
Sociology 601 Class 26: December 1, 2009 (partial) Review –curvilinear regression results –cubic polynomial Interaction effects –example: earnings on married.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 6) Slideshow: variable misspecification iii: consequences for diagnostics Original.
TESTING A HYPOTHESIS RELATING TO A REGRESSION COEFFICIENT This sequence describes the testing of a hypotheses relating to regression coefficients. It is.
SLOPE DUMMY VARIABLES 1 The scatter diagram shows the data for the 74 schools in Shanghai and the cost functions derived from a regression of COST on N.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 5) Slideshow: exercise 5.5 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 5) Slideshow: Chow test Original citation: Dougherty, C. (2012) EC220 - Introduction.
EDUC 200C Section 4 – Review Melissa Kemmerle October 19, 2012.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 5) Slideshow: the effects of changing the reference category Original citation: Dougherty,
1 INTERACTIVE EXPLANATORY VARIABLES The model shown above is linear in parameters and it may be fitted using straightforward OLS, provided that the regression.
1 TWO SETS OF DUMMY VARIABLES The explanatory variables in a regression model may include multiple sets of dummy variables. This sequence provides an example.
Confidence intervals were treated at length in the Review chapter and their application to regression analysis presents no problems. We will not repeat.
EXERCISE 5.5 The Stata output shows the result of a semilogarithmic regression of earnings on highest educational qualification obtained, work experience,
Returning to Consumption
Country Gini IndexCountryGini IndexCountryGini IndexCountryGini Index Albania28.2Georgia40.4Mozambique39.6Turkey38 Algeria35.3Germany28.3Nepal47.2Turkmenistan40.8.
EDUC 200C Section 3 October 12, Goals Review correlation prediction formula Calculate z y ’ = r xy z x for a new data set Use formula to predict.
Wiener Institut für Internationale Wirtschaftsvergleiche The Vienna Institute for International Economic Studies Structural change, productivity.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 1) Slideshow: exercise 1.5 Original citation: Dougherty, C. (2012) EC220 - Introduction.
. reg LGEARN S WEIGHT85 Source | SS df MS Number of obs = F( 2, 537) = Model |
Econ 314: Project 1 Answers and Questions Examining the Growth Data Trends, Cycles, and Turning Points.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 5) Slideshow: exercise 5.2 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Panel Data. Assembling the Data insheet using marriage-data.csv, c d u "background-data", clear d u "experience-data", clear u "wage-data", clear d reshape.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 4) Slideshow: exercise 4.5 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Special topics. Importance of a variable Death penalty example. sum death bd- yv Variable | Obs Mean Std. Dev. Min Max
COST 11 DUMMY VARIABLE CLASSIFICATION WITH TWO CATEGORIES 1 This sequence explains how you can include qualitative explanatory variables in your regression.
Lecture 5. Linear Models for Correlated Data: Inference.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 6) Slideshow: exercise 6.13 Original citation: Dougherty, C. (2012) EC220 - Introduction.
STAT E100 Section Week 12- Regression. Course Review - Project due Dec 17 th, your TA. - Exam 2 make-up is Dec 5 th, practice tests have been updated.
RAMSEY’S RESET TEST OF FUNCTIONAL MISSPECIFICATION 1 Ramsey’s RESET test of functional misspecification is intended to provide a simple indicator of evidence.
1 CHANGES IN THE UNITS OF MEASUREMENT Suppose that the units of measurement of Y or X are changed. How will this affect the regression results? Intuitively,
SEMILOGARITHMIC MODELS 1 This sequence introduces the semilogarithmic model and shows how it may be applied to an earnings function. The dependent variable.
GRAPHING A RELATIONSHIP IN A MULTIPLE REGRESSION MODEL The output above shows the result of regressing EARNINGS, hourly earnings in dollars, on S, years.
1 BINARY CHOICE MODELS: LINEAR PROBABILITY MODEL Economists are often interested in the factors behind the decision-making of individuals or enterprises,
1 In the Monte Carlo experiment in the previous sequence we used the rate of unemployment, U, as an instrument for w in the price inflation equation. SIMULTANEOUS.
F TESTS RELATING TO GROUPS OF EXPLANATORY VARIABLES 1 We now come to more general F tests of goodness of fit. This is a test of the joint explanatory power.
WHITE TEST FOR HETEROSCEDASTICITY 1 The White test for heteroscedasticity looks for evidence of an association between the variance of the disturbance.
1 COMPARING LINEAR AND LOGARITHMIC SPECIFICATIONS When alternative specifications of a regression model have the same dependent variable, R 2 can be used.
1 Analysis of Variance (ANOVA) EPP 245/298 Statistical Analysis of Laboratory Data.
The slope, explained variance, residuals
QM222 Your regressions and the test
Auto Accidents: What’s responsible?
QM222 Class 15 Section D1 Review for test Multicollinearity
Eva Ørnbøl + Morten Frydenberg
EPP 245 Statistical Analysis of Laboratory Data
Presentation transcript:

1 Zinc Data EPP 245 Statistical Analysis of Laboratory Data

October 25, 2007EPP 245 Statistical Analysis of Laboratory Data 2. insheet using "Zinc.raw" (2 vars, 91 obs). do "zinc.do". regress peakarea concentration Source | SS df MS Number of obs = F( 1, 89) = Model | e e+11 Prob > F = Residual | R-squared = Adj R-squared = Total | e e+09 Root MSE = peakarea | Coef. Std. Err. t P>|t| [95% Conf. Interval] concentrat~n | _cons | scatter peakarea concentration || lfit peakarea concentration. graph export zinc1.wmf, replace. rvfplot. graph export zinc2.wmf, replace. display (1850-_b[_cons])/_b[concentration]

October 25, 2007EPP 245 Statistical Analysis of Laboratory Data 3

October 25, 2007EPP 245 Statistical Analysis of Laboratory Data 4

October 25, 2007EPP 245 Statistical Analysis of Laboratory Data 5. keep if concentration == 0 (83 observations deleted). list concentration peakarea | concen~n peakarea | | | 1. | | 2. | | 3. | | 4. | | 5. | | | | 6. | 0 93 | 7. | 0 99 | 8. | | summarize peakarea Variable | Obs Mean Std. Dev. Min Max peakarea | display *