Statistical Analysis BU 510 601 2 Credit Hours Fall 2013 Instructor: Shrikant Panwalkar Office phone: (410) 234 9456 Office Hours: By appointment panwalkar@jhu.edu Required Text and Learning Materials Business Statistics in Practice; 6th Edition‚ McGraw-Hill Higher Education‚ ISBN-13 978-0-07-340183-6 (There are other ISBN numbers) Authors: Bowerman‚ Bruce; O’Connell‚ Richard. (the cover shows a third author – Murphree) Please note: 7th edition is available‚ however
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areas totaling 42% The Second will be the size chart. This will measure tendency‚ variation‚ mean‚ median and mode. Descriptive Statistics: Size Mean 3.42 Standard Error 0.24593014 Median 3 Mode 2 Standard Deviation 1.73898868 Sample Variance 3.02408163 Kurtosis -0.7228086 Skewness 0.52789598 Range 6 Minimum 1 Maximum 7 Sum 171 Count 50 Frequency Distribution: Size Frequency 1 5 2 15 3 8 4 9 5 5 6 5 7 3 The mean household size of the customers is given as 3.42
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BBA / BHTM 300 – STATIATICS II COURSE DESCRIPTION-STATISTICS-II Business statistics is a rich interactive learning experience designed to give students the basic intellectual tools and aptitudes they need to meet today’s business challenges The course is also aimed at equipping the students with the skills of real socio-economic processes‚ economic interpretation of analysed data‚ its applied statistic analysis‚ construction‚ identification and verification of statistical models of
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Definition of Value at Risk (VaR) Value at risk is a statistical technique which measures the level of financial risk in a portfolio over a specific time frame. For example‚ if a firm states that it has a 1% one week value at risk of $5 million; this would mean that for any given week‚ the firm would have a 1% chance of losing $5 million. In order words‚ 1 out of every 100 weeks‚ the firm would expect to have a loss of $5 million. This can be viewed as the standard deviation of portfolio value
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TUTORIAL 1 -95253936900 Each of the following processes involves sampling from a population. Define the population‚ and state whether it is tangible or conceptual. A shipment of bolts is received from a vendor. To check whether the shipment is acceptable with regard to shear strength‚ an engineer reaches into the container and selects 10 bolts‚ one by one to test. The resistance of a certain resistor is measured 5 times with the same ohmmeter 8 welds are made with the same process‚ and the
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Group 7 MOTION PICTURES INDUSTRY- CASE STUDY The data collected for a sample of 100 motion pictures produced in 2005 is given below. A survey is carried out to analyze how different variables of the Motion Picture Industry contribute to the success of its motion pictures. The study focuses on four major variables‚ Opening Gross Sales‚ Total Gross Sales‚ Number of Theatres and weeks in top 60. Motion Picture Opening Gross Sales( $millions) Total Gross Sales ($ millions) Number of Theaters
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Extra Exercise: Estimation and Hypothesis Testing 1. The length (in millimetres) of a batch of 9 screws was selected at random from a large consignment and found to have the following information. 8.02 8.00 8.01 8.01 7.99 8.00 7.99 8.03 8.01 Construct a 95% confidence interval to estimate the true average length of the screws for the whole consignment. From a second large consignment‚ another 16 screws are selected at random and their mean and standard deviation found to be
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FALSE For a random variable X‚ let µ = E (X). The variance of X can be expressed as: V ar(X) = E X 2 − µ2 7. TRUE For random variables Y and X‚ the variance of Y conditional on X = x is given by: V ar(Y |X = x) = E Y 2 |x − [E (Y |x)]2 8. TRUE An estimator‚ W ‚ of θ is an unbiased estimator if E (W ) = θ for all possible values of θ. 9. FALSE The central limit theorem states that the average from a random sample for any population (with finite variance) when it is standardized‚ by subtracting the mean
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exponential probability density function. If then the cumulative distribution function of the exponential distribution is The following is the plot of the exponential cumulative distribution function. Mean and variance of an exponential function The mean and variance of an exponential function are respectively Example 28 If jobs arrive every 15 seconds on average‚ λ= 4 per minute‚ what is the probability of waiting less than or equal to 30 seconds‚ i.e .5 min
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| |COMPANY A | | | | | |BANK OF AMERICAN CO | |Date |Open |Close |Dividend |Return | |12/1/2005 |46 |46.15 |0.5 |0.014130435 | |11/1/2005 |43.75 |45.89 |0.5 |0.060342857 | |10/3/2005 |42.47 |43.74 |0.5 |0.041676478
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