course was then recorded in hours. The table below summarizes the data collected. From this data we can note that the two methods have a very comparable completion time as the means of both are just above 75 hours. We can also note that the variance is less for the proposed method. To test the difference between the two population means we conducted the following hypothesis test. Ho:u_1-u_2=0 Ha:u_1-u_2≠0 With this hypothesis test established‚ using the information summarized earlier
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were unequal variances amongst both male and female data sets. Results There is a normal distribution of the data with ranges in size from size 5 to size 14 amongst the participants. With these ranges‚ the mean is 9.142‚ with a standard deviation of 2.583 and a variance of 6.670. Appendix B: Male vs. Female data sets‚ show the mean‚ standard deviation and variance for both of these sets respectively. Female data sets have a mean of 7.111‚ standard deviation of 1.131‚ and a variance of 1.281 whereas
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been 10% of the number of employees in a given year. 2) The number of workplace injuries‚ X‚ typically follows a Poisson distribution with a parameter λ‚ where E(X) = λ and Variance(X) = λ. 3) We can reasonably approximate the Poisson distribution via a Normal distribution‚ with the same expected value and variance (according to Central Limit Theorem‚ for which you don’t need to know the details). 4) You will also need to use the critical value for the standard Normal distribution (i.e
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DANSHUI PLANT NO.2 Background: Danshui was a contract manufacturer assembling electronic products in southern China. There were many manufacturers like Danshui in China assembling parts for the companies wishing to save labor costs. Apple contracted with Danshui to assemble 2.4 million iPhones in Plant No.2 with the expected high demand of this product in 2010. As a profit center‚ Plant No.2 was credited for each iPhone produced and shipped. The process of assembling was complex and required almost
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portion of a population. We often use information concerning a sample to make an inference (conclusion) about the population. Parameter - describes a characteristic of the population‚ eg: the population variance Statistic- describes a characteristic of a sample‚ eg: the sample variance Frequency Distribution and Histograms Class - a collection of data which are mutually exclusive Frequency distribution - a grouping of data into classes Relative frequency distribution - calculates the
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20 Diameter‚ xk 14.5 20.3 26.2 30.5 23.4 21.6 17.8 27.2 23.6 20.8 Age‚ yk 61 80 114 147 122 106 82 88 97 99 ¯ (a) Treating the trees as a simple random sample‚ estimate the mean age of trees in the stand Y ‚ with a variance estimate‚ 95% confidence interval‚ and RSE. Comment on the quality of the estimate. (b) Draw a scatterplot of these data (make sure the x and y axes both start at zero). Fit a regression line y = α +βx+ε to the data‚ and draw it on to the plot
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expected return for each unit of risk. The calculation is based on forecasts of each asset’s long-term return and volatility and correlations among the various assets. Method showed how the variances of individual stock returns and the correlations of those returns can be combined to calculate a value for the variance of a portfolio made up of those stocks. Based on the received data of possible asset allocation we were able to draw an efficient frontier. The efficient frontier is the curve that shows
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and b E. neither a nor b 2.Assume that stock market returns do follow a single-index structure. An investment fund analyzes 500 stocks in order to construct a mean-variance efficient portfolio constrained by 500 investments. They will need to calculate ________ estimates of firm-specific variances and ________ estimates for the variance of the macroeconomic factor. A. 500; 1 B. 500; 500 C. 124‚750; 1 D. 124‚750; 500 E. 250‚000; 500 3.Suppose you held a well-diversified portfolio with a very
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University of Southern California Department of Economics ECON 317 Introduction to Statistics for Economists Prof. Safarzadeh Assignment # 2 Student Name: ________________ Answer all the questions on the spaces provided. Underline your answers and show your calculations and work on the tables. Item |Speed |Mileage | | |X - X |(X- X)2 |(Y-Y) |(Y-Y)2 |(X-X)(Y-Y) | | | |1 | 30 | 25 | | | | | | | | | | |2 | 50 | 20 | | | | | | | | | | |3 | 35 | 23 | | | | | | | | | | |4 | 45 | 21 | |
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hypotheses tested concerning the value of βj or its estimated values? Question 3: Techniques Consider the moving average process: Yt = εt + θ1 εt−1 + θ12 εt−12 with {εt }T a mean zero white noise process with variance σ 2 > 0. t=0 a. Calculate the mean of Yt . b. Calculate the variance of Yt . c. Calculate the autocovariance function of {Yt }T . t=a T =120 d. Assume that {yt }t=1 represents the monthly tons of ice cream sold in the UK between Oct. 2001 and Oct. 2012. What type of
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