Types of Sampling In applications: Probability Sampling: Simple Random Sampling‚ Stratified Random Sampling‚ Multi-Stage Sampling * What is each and how is it done? * How do we decide which to use? * How do we analyze the results differently depending on the type of sampling? Non-probability Sampling: Why don’t we use non-probability sampling schemes? Two reasons: * We can’t use the mathematics of probability to analyze the results. * In general‚ we can’t count on a non-probability
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Chapter 9 Na¨ve Bayes ı David J. Hand Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Power Despite Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extensions of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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An Analysis on Factors that Affect Unemployment Rate: The Philippine Case An empirical paper presented to Dr. Cesar Rufino School of Economics De La Salle University In partial fulfillment of the course requirements in ECONMET Submitted by: Ramon Mikael S. Cabazor April 12‚ 2014 Table of Contents Introduction Background of the Study Statement of the Problem and Objective Significance of the Study Scope and Limitation 3 3 4 4 5 Review of Related Literature Unemployment
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Analysis of the System and Estimator Variables in the Cotton Case FPSY-2001-1 Walden University 5/06/2014 There are two variables associated with the misidentification of one by an eyewitness‚ estimator variables or “factors that relate to the circumstances under which witnessing takes place”‚ and system variables in which the variables “include overt or subtle suggestive techniques cops use to influence a witness to pick a particular suspect” ("Mistaken Identification in California
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when we have panel data 3) E(X^4) < infinity or outliers are rare‚ OLS can be sensitive with outliers beta 1 = Sxy/Sx^2 is the object of interest (the causal effect of X on Y) sampling dist of beta 1 is normal when we have n large and thus estimators -> pop parameter in the limits that is it is consistent The larger the var of X‚ the smaller the var of beta hat 1 Regressor: Hypothesis testing and CI (CHAPTER 5) sampling dist of beta 1 when n is large Objective: Test various hypotheses
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between two means (variances unknown): t= 1 n1 + + (¯ x1 − x ¯2 ) ± tn1 +n2 −2 1 n2 Pooled variance estimator: Sp2 = σ12 n1 t= sd x ¯d ± tn−1 √ n ¯ d − µd X √ Sd / n Z= (P1 − P2 ) − (π1 − π2 ) P (1 − P ) R1 + R 2 n1 + n2 (p1 − p2 ) ± z χ2 test of association: i=1 j=1 1 n1 + 1 n2 Confidence interval endpoints for the difference between two proportions: Pooled proportion estimator: c 1 1 + n1 n2 Z-test for the difference between two proportions: Confidence interval endpoints for
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Project Management Investigation of the Planning of a Low-rise Domestic or Commercial Building and Production of Planning Documentation Portfolio Task 1 (For P1): Pre-Construction Phase Client brief: The client brief is where the client and the architect sit down with each other and discuss what the client wants built. The architect usually asks a range of questions to be able to do a suitable design as accurate as possible to the client’s needs. The budget for the project is also confirmed
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JetBlue Airways IPO Valuation Background Started in 1999 with the promise to “bring humanity to air travel‚” JetBlue entered the “Discount Fare Airlines” to join the likes of Southwest‚ ATA‚ Frontier and others (Bruner 2002). With a strong and experienced management team‚ having Continental Airlines’ former vice-president as president and COO‚ and Southwest Airlines’ former executive vice-president and treasurer as CFO‚ David Neeleman believed that his new airline would thrive. Neeleman built
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β(1)resplast+ β(2)avggift+ β(3)propresp+ β(4)mailyear+u+e(0). Given that there are no correlation with the explanatory variables‚ OLS estimators βs will remain constant and unbiased. This is an extension of MLR 3(zero conditional mean) which suggests that the error term u has no correlation to the βs and hence can infer that likewise ‘e’ has zero conditional mean making the OLS estimators consistent and unbiased. If we consider that e(0) does not satisfy zero conditional mean then only β0 will be biased. In
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scores than those that did not receive such inputs. The analysis uses schoollevel overall composite test scores (comprising all subject areas tested) and test scores in three separate subject areas: English‚ math‚ and science. Their preferred estimator‚ difference-in-difference with propensity score matching‚ shows that the average treatment effect of participation in school-based management was higher by 1.5 percentage points for overall composite scores‚ 1.2 percentage points for math scores
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