Observation = estimated relationship + residual: yi =+ ei => yi = b1 + b2 x + ei Assumptions underlying model: 1. Linear Model ui = yi - 1- 2xi 2. Error terms have mean = 0 E(ui|x)=0 => E(y|x) = 1 + 2xi 3. Error terms have constant variance (independent of x) Var(ui|x) = 2=Var(yi|x) (homoscedastic errors) 4. Cov(ui‚ uj )= Cov(yi‚ yj )= 0. (no autocorrelation) 5. X is not a constant and is fixed in repeated samples. Additional assumption: 6. ui~N(0‚ 2) => yi~N(1- 2xi‚ 2)
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Part 1 of 1 - | Question 1 of 10 | 1.0 Points | Consider the following scenario in answering questions 1 through 4. Results from previous studies showed 79% of all high school seniors from a certain city plan to attend college after graduation. A random sample of 200 high school seniors from this city reveals that 162 plan to attend college. Does this indicate that the percentage has increased from that of previous studies? Test at the 5% level of significance. State the null and alternative
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of paper. Please highlight your answers so we can find them easily. 1. Compute and report the mean returns‚ variances‚ and standard deviations for the two stocks. In addition‚ compute the covariance and the correlation between the two stock returns. Report all numbers as annualized. (Hint: annualized variance is equal to 12*monthly variance. Also‚ please do not report variances and covariances in %‚ which would not make sense.) 2. Plot the mean-standard deviation graph for a portfolio
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been reviewing random variables. RVs have certain properties such as mean that measures the center‚ and variance that measures the dispersion. We would like to make claims about these properties and test them using statistical methods. Over the past years‚ Wall Street has been very interested in the volatility of the stocks. In this case‚ we would want to make sound claims about variances. We start with a null hypothesis Ho‚ which is the claim that we will test. It looks as such: In this
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Econometrics Assignment 2 Group Members : Eliza Tan 01120120073 Praisya Lordrietta 01120120061 Wirhan Pandutama 0112012 UNIVERSITAS PELITA HARAPAN LIPPO KARAWACI-TANGERANG 2014 Gauss-Markov Theorem The Gauss-Markov Theorem is given in the following regression model and assumptions: The regression model (1) Assumptions (A) or Assumptions (B): Assumptions (A) Assumptions (B) E( If we use
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a number of other analysis methods as special cases. We will assume that you are familiar with the basic logic of statistical reasoning as described in Elementary Concepts. Moreover‚ we will also assume that you are familiar with the concepts of variance‚ covariance‚ and correlation; if not‚ we advise that you read the Basic Statistics section at this point. Although it is not absolutely necessary‚ it is highly desirable that you have some background in factor analysis before attempting to use structural
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Normal Distribution Normal distribution is a statistics‚ which have been widely applied of all mathematical concepts‚ among large number of statisticians. Abraham de Moivre‚ an 18th century statistician and consultant to gamblers‚ noticed that as the number of events (N) increased‚ the distribution approached‚ forming a very smooth curve. He insisted that a new discovery of a mathematical expression for this curve could lead to an easier way to find solutions to
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Global Asset Allocation Finance 656 (Please return to Fang Song’s locker #552) Michelle Bien Yushao Karen Chiu Srinivas Mudireddy Fang (Derek) Song‚ 12/08/2013 A Study on stock returns and volatility Abstract This paper applies two models to examine the intertemporal relationship between expected returns and market risk. By using ARIMA models‚ two findings can be found: 1) A positive correlation exists between the expected market risk premium and the predictable volatility. 2)
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MEI STRUCTURED MATHEMATICS EXAMINATION FORMULAE AND TABLES 1 Arithmetic series General (kth) term‚ last (nth) term‚ l = Sum to n terms‚ Geometric series General (kth) term‚ Sum to n terms‚ Sum to infinity Infinite series f(x) uk = a + (k – 1)d un = a + (n – l)d – – Sn = 1 n(a + l) = 1 n[2a + (n – 1)d] 2 2 x2 xr = f(0) + xf’(0) + –– f"(0) + ... + –– f (r)(0) + ... 2! r! f(x) f(a + x) uk = a r k–1 a(1 – r n) a(r n – 1) Sn = –––––––– = –––––––– 1–r r–1
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includes a cost variance‚ in which you complete the following: Determine specific strategies to manage budgets within forecasts. Zero based (analyzes every expense within an organization and justifies the need and cost of each)‚ activity based (is the gathering of the operating cost data‚ which is assigned to specifc activies such as engineering) performance based (performance dashboard uses the metrics of performance and analyzes the root cause of financial problems)‚ cost variances (looks at the
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