1. A quality control engineer knows that 10% of the microprocessor chips produced by a machine are defective. Out of a large shipment‚ five chips are chosen at random. What is the probability that none of them is defective? What is the probability that at least one is defective? 2. An automated manufacturing process produces a component with an average width of 7.55 centimeters‚ with a standard deviation of 0.02 centimeter. All components deviating by more than 0.05 centimeter from the mean must
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be able to ONEDefine probability. TWO Describe the classical‚ empirical‚ and subjective approaches to probability. THREEUnderstand the terms experiment‚ event‚ outcome‚ permutation‚ and combination. FOURDefine the terms conditional probability and joint probability. FIVE Calculate probabilities applying the rules of addition and multiplication. SIXUse a tree diagram to organize and compute probabilities. SEVEN Calculate a probability using Bayes theorem. What is probability There is really no answer
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BBA (Fall - 2014) Business Statistics Theory of Probability Ahmad Jalil Ansari Business Head Enterprise Solution Division Random Process In a random process we know that what outcomes or events could happen; but we do not know which particular outcome or event will happen. For example tossing of coin‚ rolling of dice‚ roulette wheel‚ changes in valuation in shares‚ demand of particular product etc. Probability It is the numeric value representing the chance‚ likelihood‚ or possibility
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random. What is the probability that at least one pair of shoes is obtained? 2. At a camera factory‚ an inspector checks 20 cameras and finds that three of them need adjustment before they can be shipped. Another employee carelessly mixes the cameras up so that no one knows which is which. Thus‚ the inspector must recheck the cameras one at a time until he locates all the bad ones. (a) What is the probability that no more than 17 cameras need to be rechecked? (b) What is the probability that exactly 17
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2010 Words of Probability ISHIGURO‚ Makio(The Institute of Statistical Mathematics) Words of Probability ISHIGURO‚ Makio(The Institute of Statistical Mathematics) Key Words: subjective probability‚ confidence‚ belief‚ frequency‚ verbal expression Abstract There are everyday expressions such that ’probably’; ’might be’;’could be’ etc.‚ to describe the strengths of one’s confidence in the occurrence of events in the future. On the other hand there are probability theory expressions
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Concept and basics of probability sampling methods One of the most important issues in researches is selecting an appropriate sample. Among sampling methods‚ probability sample are of much importance since most statistical tests fit on to this type of sampling method. Representativeness and generalize-ability will be achieved well with probable samples from a population‚ although the matter of low feasibility of a probable sampling method or high cost‚ don’t allow us to use it and shift us to the
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Use this probability to calculate the approximate number of packets containing no defective‚ one defective and two defective pens‚ respectively in a consignment of 20‚000 packets [ e^(--0.02) =0.9802 ] Ans. : 19604‚ 392‚ 3.92=4 respectively 2. A manufacturer who produces medicine bottles finds that 0.1% of the bottles are defective. The bottles are packed in the boxes of 500 bottles. A drug manufacturer buys 100 boxes from the producer of bottles . Using suitable probability distribution
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Tab 1----All graphs‚ including the histogram should have an appropriate title and the x and y axis should be labeled. Bin and frequency does not give any information as to what is being represented by the numerical data in the histogram (hint: Electricity cost (in $) and one-bedroom apartments). As Professor Ellis stated in the lectures‚ graphs should be able to stand alone. “A Graph should sing its song!” Bin ranges are correct. However‚ the largest percentage does not lie between 139‚ 179. Both
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APPLIED PROBABILITY AND STATISTICS APPLIED PROBABILITY AND STATISTICS DEPARTMENT OF COMPUTER SCIENCE DEPARTMENT OF COMPUTER SCIENCE STATISTICAL DISTRIBUTION STATISTICAL DISTRIBUTION SUBMITTED BY – PREETISH MISHRA (11BCE0386) NUPUR KHANNA (11BCE0254) SUBMITTED BY – PREETISH MISHRA (11BCE0386) NUPUR KHANNA (11BCE0254) SUBMITTED TO – PROFESSOR SUJATHA V. SUBMITTED TO – PROFESSOR SUJATHA V
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The linear probability model‚ ctd. When Y is binary‚ the linear regression model Yi = β0 + β1Xi + ui is called the linear probability model. • The predicted value is a probability: • E(Y|X=x) = Pr(Y=1|X=x) = prob. that Y = 1 given x • Yˆ = the predicted probability that Yi = 1‚ given X • β1 = change in probability that Y = 1 for a given ∆x: Pr(Y = 1 | X = x + ∆x ) − Pr(Y = 1 | X = x ) β1 = ∆x 5 Example: linear probability model‚ HMDA data Mortgage denial v. ratio of debt payments to income (P/I
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