1. Affirmative Action destroys the idea of meritocracy and students should be chosen based on their intelligence instead of their race or gender. “At the University of Wisconsin‚ the median composite SAT score for blacks who were admitted was 150 points lower than for whites and Asians and the Latino median SAT score was 100 points lower”. This quote shows how Affirmative Action destroys the idea of meritocracy and applicants are mainly chosen on someone’s race not intelligence. (http://brandongaille
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lagged drivers – Use correlation to pick a lagged driver – Build a linear forecast model using regression‚ perform DW test on residuals – Repeat if residuals do not pass DW test • Forecast revenues and generate 95% prediction intervals for 2009 and 2010 6 Revitalizing Dell: Bright forecast 7 Revitalizing Dell: Harsh reality 8 Revitalizing Dell: What did our model miss? • HP sales data not “clean” (starts to include Compaq since 2002) • Ratings lack variation (could have used Dell’s
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SET INDUCTION ENHANCE STUDENTS INTEREST LEARNING IN SCIENCE Abstract The purpose of this study is to examine the effects of applying different type of set induction on students interest‚ attention and motivation level. Participants will be Year 5 students from who will receive 1 hour weekly different type of set induction along with their regular science lesson. The set induction that i was used in this study was story telling and games like activity. During the lesson teacher
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and policy makers alike have realized that housing has significant influences on the business cycle. This paper tries to figure out the determinants of the selling price of houses in Oregon. The data set used in this paper has been retrieved from the case study titled “Housing Price” (Case #27 - Practical Data Analysis: Case Studies in Business Statistics- Marlene A. Smith & Peter G. Bryant) The most important factor in determining the selling prices ofhouses is to know the features that drive the
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presentation outlines essentially 3 approaches to fighting cancer: a) reduction of cancer risks‚ b) correction of cancer genes‚ and c) destruction of cancerous tissue. 3. In the Individual Assigntment 3: 10 Discoveries in the War on Cancer document is a set of 10 scientists’ discoveries. Scan the discoveries briefly. Then open the assignment submission link in Module/Week 4. In the text box‚ number from 1 to 10 for the 10 discoveries listed below. 4. Now reflect carefully on the first discovery (#1)
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physically training – single or multiple sets. We all have friends who spend at least three hours in the gym and others‚ who only spend about thirty minutes to an hour. Is it safe to argue the person in the gym for three hours is doing multiple sets‚ while the person in the gym for an hour is only doing a single set? Probably not because no one knows how many exercises the athlete is actually doing. The real discussion comes down to single or multiple sets‚ which is the better of the two for optimal
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Regression Modeling for Brand Xmarcom Strategy Analytical approach using Tracking Research data Approach: The analysis of brand Sofy has been done with a two stages of statistics and model building approach. MATRIX IDENTIFICATION At the very first stage the data for Sofy was plotted in scatter graphs for pattern identification. The various combinations of variables for independent and dependent variables were taken to shortlist the variables for further scientific tests. TEST AND ANALYTICS
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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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Regression with Discrete Dependent Variable CE 601 Term Project By Classification Type of Discrete Dependent Variable Example Problems Type of Regression Model Binary 1. Consumer economics 2. Decision to vote Logistic Regression Probit Regression Ordinal 1. Opinion survey 2. Rating systems Ordered Logistic Regression Ordered Probit Regression Nominal 1. Occupation choice 2. Blood type Multinomial Logistic Regression Count 1. Consumer demand 2
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Glued to the Set: The Sixties Television Shows and Events that made us who we are today During the nineteen-sixties there was a lot of change going on. There was a social revolution witch brought change to the rights to people like woman‚ African Americans‚ and other minorities. There was the “hippy movement” witch brought change to how the average adolescent thought and acted. And there was a major cultural revolution‚ including different clothing styles and a new kind of music. But there’s
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