1. A shoe manufacturer’s data show that the distribution of shoe size is normal with a mean of 11inches and SD 1.5 inches. * A manager of the company claims that 70% of the times shoe size will be between 8 and 12 inches. What do you think? In your deliverables, write a few lines about what you think of the manager’s claim and what would you advise him to do. (1 point) The probability of a size being below size 8 is .02275 and the probability of a show size being size 12 or below is .747507
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5) = 1 - .0668 = .9332 d. P(-2.5 ≤ z) = 1 - P(z < -2.5) = 1 - .0062 = .9938 e. P(-3 < z ≤ 0) = P(z ≤ 0) - P(z ≤ -3) = .5000 - .0013 = .4987 These probability values are read directly from the table of areas for the cumulative standard normal probability distribution. See Table B in my notes or the relevant table in your book. 17. Let x = debt amount μ = 15,015, σ = 3540 a. z= 18,000 −15,015 = .84 3540 P(x > 18,000) = 1- P(z ≤ .84) = 1 - .7995 = .2005 b. z= 10,000 − 15
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that the mean price was $1.52 per gallon and that the standard deviation was $0.05 per gallon. At the .05 significance level, can we conclude that the price of gasoline is higher in the Milwaukee area? Calculate the p-value and interpret. PROBLEM 2: (Weight 20 points). Suppose Babsie generated the following probability distribution: X p(x) 5 7 10 12 15 .25 .30 .25 .05 .15 a. b. c. d. Is this probability distribution discrete or continuous? Explain your reasoning. Calculate
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The graduate selection committee wants to select the top 10% of applicants. On a standardized test with a mean of 500 and a standard deviation of 100. What would be the cutoff score for selecting the 10% of applicants, assuming that the standardized test is normally distributed? Mean = 500; deviation = 100; x = 10% (100-10)/100=.90 (looking at the normal distribution curve, the closest I could get was .8997 which rounds up to 0.90) and the Z score is 1.28. 1.28*100 = 128 and the mean is 500
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X^2 distribution test: X^2 distribution: not symmetric, its skewed to the right as D.F gets larger the curve looks increasingly like a normal curve. Same for t-distribution D.F = n-1 Ho: σ2= σo2 Ha: σ 2 ≠ σ02 Ha: σ 2 > σ02 Ha: σ 2 < σ02 Χ2 = [ ( n - 1 ) * s2 ] / σ2 Confidence interval for σ2 : Confidence interval for σ2 : D.F. = n-1 D.F. = n-1 Note: sometimes L = & u = Note: s= standard deviation X^2
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11/8/2014 Bilkent University Department of Electrical and Electronics Engineering EEE418 Telecommunications I Project#1 Report Student Name: Mehmet Yasin Tuzmen Student ID: 21001179 Professor’s Name: Tolga Mete Duman Table of Contents I. Introduction .......................................................................................................................................... 2 II. Outline........................................................................
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21 Real Estate Company. They have recorded the properties’ market values at Dec 31, 2010 & Dec 31, 2011. 1. Draw a histogram with properties’ building footages, and their market value in 2011. Discuss the characteristics of those 2 distributions. 2. Test whether the average property market value in 2011 is significantly different from $4,750,000 at 1% level of significance. 3. Suppose the 1,500 properties are separated into 2 districts: T.S.T. (Group 1) & Jordan (Group2).
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The problem with problems ------------------------------------------------- Instructions ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- The exam is closed book and closed notes. ------------------------------------------------- ------------------------------------------------- Please answer the multiple choice & true/false questions by clearly marking only one option on the
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Hypothes9 9.1 Fundamentals of Hypothesis Testing: One-Sample Tests A Connection Between Confidence Interval Estimation and Hypothesis Testing Can You Ever Know the Population Standard Deviation? USING STATISTICS @ Oxford Cereals, Part II Fundamentals of Hypothesis-Testing Methodology The Null and Alternative Hypotheses The Critical Value of the Test Statistic Regions of Rejection and Nonrejection Risks in Decision Making Using Hypothesis Testing Hypothesis Testing Using the Critical Value
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GARCH Model Fitting For IBM Introduction: Nowadays, many econometric studies have documented that financial return time series tend to be highly heteroskedastic, with varying variability or volatility and thick-tailed marginal distribution. Volatility is the conditional variance, which is unobservable and tends to be clustered together. Clustering is referred to as the phenomenon that large price changes of either signs tend to be followed by large changes, and similarly small changes follow
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