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    QMS102 Cribsheet final

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    - Statistics is the science of collecting organizing‚ presenting‚ analyzing and interpreting data. It turns Data into information through transformation. Population ! - Everyone in group ! ! - µ is the mean ! - σ is the standard deviation Sample! - Some/Portion of group ! -x is the mean!! -S is the standard deviation Measurement Scales Types of Data Categorical/Qualitative Categorical/Qualitative Nominal! -Data has category but no order Data that can only be in categories ! ! -Example

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    Bootstrap codes

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    11:18)] k200rt<-data[‚c(1‚19)] unhedgert<-data[‚c(1‚20)] head(unhedgert) #Hedge portfolio ---QUESTION: THE HEDGE SHOULD BE DONE WITH THE PARAMETERS OF THE RETURNS OF EACH ASSETS OR FROM THE HEDGE PORT------ Expr<- sapply(asret‚mean) #Calculating the expected return (Just the mean of the each of the assets) sdh<- sapply(asret‚sd) #Calculating the Standard deviation. varh<-sapply(asret‚var) covmat<-cov(asret) cormat<-cor(asret) ncol(asret) library(PerformanceAnalytics) library(tseries) library(quadprog)

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    Weight Reducers International‚ advertises that those who join will lose‚ on the average‚ 10 pounds the first two weeks with a standard deviation of 2.8 pounds. A random sample of 50 people who joined the new weight reduction program revealed the mean loss to be 9 pounds. At the .05 level of significance‚ can we conclude that those joining Weight Reducers on average will lose less than 10 pounds? Determine the p-value. Answer: H0: = 10 pounds H1: < 10 pounds Reject the null hypothesis

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    Gsing

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    Pakistan Journal of Meteorology Vol. 9‚ Issue 17: Jul 2012 Rainfall Trends in Different Climate Zones of Pakistan Salma‚ S.1‚ S. Rehman1‚ M. A. Shah2 Abstract In this paper‚ the study was conducted across the country to assess the rainfall trend in different climate zones of Pakistan over the past three decades. For this purpose dataset comprising 30 years for the period 1976 to 2005 were acquired from 30 meteorological observatories from different parts of the country. The whole data was

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    Kentucky Case

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    theorists‚ the mean winning bid price in the “rigged” market will exceed the mean winning bid price in the competitive market for each year in which collusion occurs. In addition‚ the difference between the competitive average and the “rigged” average tends to grow over time when collusionary tactics are employed over several consecutive years. Comparison of difference in mean winning bid price between Tri-county and surrounding area per year per product YEAR | Difference in mean winning bid

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    Wohw

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    ------------------------------------------------- ETX2011/ETX9520 QUANTITATIVE METHODS FOR RISK ANALYSIS ------------------------------------------------- Semester 1‚ 2013 ------------------------------------------------- ASSIGNMENT 1 - solutions Total: 69 marks ------------------------------------------------- I recently asked many students about the number of Problems from AWZ that they have done so far. The answer was mostly “0”. If this is your answer‚ then I invite you to look at your

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    Statistics

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    1. A radio station that plays classical music has a “By Request” program each Saturday night. The percentage of requests for composers on a particular night are listed below: Composers Percentage of Requests Bach 5 Beethoven 26 Brahms 9 Dvorak 2 Mendelssohn 3 Mozart 21 Schubert 12 Schumann 7 Tchaikovsky 14 Wagner 1 a. Does the data listed above comprise a valid probability distribution? Explain.

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    sample. 2) Data Analysis From X-bar chart of Figure 2‚ there are only five points (12.5833‚ 18.6667‚ 12.9167‚ 17.9167‚ and 12.9167) are close to the UCL or LCL‚ others are fluctuating around the average of sample mean. Given that X-bar chart monitors the changes in the mean of a process‚ we can conclude that the tendencies of the average grind time are centralized. More importantly‚ we have to pay attention on the R-chart of the grind time. We can see that there are up to 16/37 points close

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    Eating Paint

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    2. Construct side-by-side dot and box plots. What do the graphs seem to indicate? 1 3. Find the simple summary statistics for each group. Find a standard error for each mean‚ and a confidence interval for the mean of each group. Interpret the se’s and the confidence intervals. 4. Estimate the difference in the means and obtain an estimated se for the difference and a confidence interval for the difference. Interpret the se and the confidence interval. 5. Define the parameters of this experiment‚

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    Assignment 2 Worksheet

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    the gas mileage. What does it mean? 4- Compute the correlation coefficient between the speed and the gas mileage. What does it mean? 5- Graph a histogram of speed. Comment on the distribution of the speed. 6- Graph a histogram of gas mileage. Comment on the distribution of the mileage. II. Descriptive Statistics 1. For each variable (speed and mileage) in the table above‚ find mean‚ median‚ and mode. Mean Speed = Median X = Mode X = Mean Mileage = Median Y = Mode Y

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