Economics 203 Syllabus APLIAEconomic Statistics II Sections AL1‚ BL1 Fall 2013 Instructor: Office: Phone: e-mail: Office hours: Lecture hours: Lecture Section: Lecture Location: Professor Joseph A. Petry 116 David Kinley Hall 333-4260 jpetry@illinois.edu Wed 10:15 – 11:15 M/W 3:00 – 3:50 (AL1); M/W 4:00 – 4:50 (BL1) AL1‚ BL1 141 Wohlers Hall Lab Time: Lab Days: Lab Location: TA Office Hours: TA Contacts: Head TA Varies by TA section Thursday / Friday 901 W. Oregon
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A. DETERMINE IF BLOOD FLOW CAN PREDICT ARTIRIAL OXYGEN. 1. Always start with scatter plot to see if the data is linear (i.e. if the relationship between y and x is linear). Next perform residual analysis and test for violation of assumptions. (Let y = arterial oxygen and x = blood flow). twoway (scatter y x) (lfit y x) regress y x rvpplot x 2. Since regression diagnostics failed‚ we transform our data. Ratio transformation was used to generate the dependent variable and reciprocal transformation
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the diet plan: The chicken food type should contribute at most 25% of the total calories intake that will result from the diet plan. The vegetable food type should provide at least 30% of the minimum daily requirements for vitamins. Provide a linear programming formulation for the above case. (No need to solve the problem.) Element | Milk | Chicken | Bread | Vegetables | Calories (X1) | 160 | 25% * 210 | 120 | 150 | Carbohydrates (X2) | 110 | 130 | 110 | 120 | Protein (X3) | 90 | 190
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Due in class Feb 6 UCI ID_____________________________ MultipleChoice Questions (Choose the best answer‚ and briefly explain your reasoning.) 1. Assume we have a simple linear regression model: . Given a random sample from the population‚ which of the following statement is true? a. OLS estimators are biased when BMI do not vary much in the sample. b. OLS estimators are biased when the sample size is small (say 20 observations)
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The simple regression model (SRM) is model for association in the population between an explanatory variable X and response Y. The SRM states that these averages align on a line with intercept β0 and slope β1: µy|x = E(Y|X = x) = β0 + β1x Deviation from the Mean The deviation of observed responses around the conditional means µy|x are called errors (ε). The error’s equation: ε = y - µy|x Errors can be positive or negative‚ depending on whether data lie above (positive) or below the conditional
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Confidence intervals and prediction intervals from simple linear regression The managers of an outdoor coffee stand in Coast City are examining the relationship between coffee sales and daily temperature. They have bivariate data detailing the stand ’s coffee sales (denoted by [pic]‚ in dollars) and the maximum temperature (denoted by [pic]‚ in degrees Fahrenheit) for each of [pic] randomly selected days during the past year. The least-squares regression equation computed from their data is [pic]
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jobs recovery‚ there simply is not going to be a housing recovery. In this report‚ I will perform a regression analysis to determine the effect of the Unemployment Rate (UR) on Total New Houses Sold (TNHS). I expect that there will be a negative relationship between the two variables. In other words‚ as the unemployment rate increases‚ the total number of new houses sold will decrease. The simple functional form of the model is TNHS=f(UR)‚ where TNHS (measured in thousands) is the dependent variable
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SIMPLE VERSUS MULTIPLE REGRESSION The difference between simple and multiple regression is similar to the difference between one way and factorial ANOVA. Like one-way ANOVA‚ simple regression analysis involves a single independent‚ or predictor variable and a single dependent‚ or outcome variable. This is the same number of variables used in a simple correlation analysis. The difference between a Pearson correlation coefficient and a simple regression analysis is that whereas the correlation does
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Project 1: Linear Correlation and Regression Analysis Gross Revenue and TV advertising: Pfizer Inc‚ along with other pharmaceutical companies‚ has begun investing more promotion dollars into television advertising. Data collected over a two year period‚ shows the amount of money Pfizer spent on television advertising and the revenue generated‚ all on a monthly bases. |Month |TV advertising |Gross Revenue | |1 |17 |4.1 | |2
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these characteristics and modeled the relationship between them and the price of real estate for a specific area. How are these characteristics used in determining the price? A model that is commonly used in real estate appraisal is the hedonic regression. This method is specific to breaking down items that are not homogenous commodities‚ to estimate value of its characteristics and ultimately determine a price based on the consumers’ willingness to pay. The approach in estimating the values is done
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