...model parameters. Answer: TRUE Diff: 1 Page Ref: 489 Main Heading: Types of Probability Key words: deterministic techniques 2) Probabilistic techniques assume that no uncertainty exists in model parameters. Answer: FALSE Diff: 1 Page Ref: 489 Main Heading: Types of Probability Key words: probabilistic techniques 3) Objective probabilities that can be stated prior to the occurrence of an event are classical or a priori. Answer: TRUE Diff: 2 Page Ref: 489 Main Heading: Types of Probability Key words: objective probabilities, classical probabilities 4) Objective probabilities that are stated after the outcomes of an event have been observed are relative frequencies. Answer: TRUE Diff: 2 Page Ref: 489 Main Heading: Types of Probability Key words: relative frequencies 5) Relative frequency is the more widely used definition of objective probability. Answer: TRUE Diff: 1 Page Ref: 490 Main Heading: Types of Probability Key words: relative frequencies 6) Subjective probability is an estimate based on personal belief, experience, or knowledge of a situation. Answer: TRUE Diff: 2 Page Ref: 490 Main Heading: Types of Probability Key words: subjective probability 7) An experiment is an activity that results in one of several possible outcomes. Answer: TRUE Diff: 1 Page Ref: 491 Main Heading: Fundamentals of Probability Key words: experiment 8) The events in an experiment are mutually exclusive if only one can occur at a...
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...Random access memory • Sequential circuits all depend upon the presence of memory. – A flip-flop can store one bit of information. – A register can store a single “word,” typically 32-64 bits. • Random access memory, or RAM, allows us to store even larger amounts of data. Today we’ll see: – The basic interface to memory. – How you can implement static RAM chips hierarchically. This is the last piece we need to put together a computer! • Random Access Memory 1 Introduction to RAM • Random-access memory, or RAM, provides large quantities of temporary • storage in a computer system. Remember the basic capabilities of a memory: – It should be able to store a value. – You should be able to read the value that was saved. – You should be able to change the stored value. A RAM is similar, except that it can store many values. – An address will specify which memory value we’re interested in. – Each value can be a multiple-bit word (e.g., 32 bits). We’ll refine the memory properties as follows: A RAM should be able to: - Store many words, one per address - Read the word that was saved at a particular address - Change the word that’s saved at a particular address • • Random Access Memory 2 Picture of memory • You can think of computer memory as being one big array of data. – The address serves as an array index. – Each address refers to one word of data. You can read or modify the data at any given memory address, just like you can read...
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...flipping a coin and getting a tail b. Throwing a die and getting a 6, then throwing a die and getting a 5 c. Selecting a red marble from a bag, returning the marble to the bag, then selecting a blue marble d. Drawing a spade from a set of poker cards, setting the card aside, then selecting a diamond from the set of poker cards e. All of the above are independent events _____3. Exam scores from a previous STATS 200 course are normally distributed with a mean of 74 and standard deviation of 2.65. Approximately 95% of its area is within: a. One standard deviation of the mean b. Two standard deviations of the mean c. Three standard deviations of the mean d. Depends on the number of outliers e. Must determine the z-scores first to determine the area _____4. You had no chance to study for the final exam and had to guess for each question. The instructor gave you three choices for the final exam: I: 10 questions, each question has 5 choices, must answer at least 4 correct to pass II: 5 questions, each question has 4 choices, must answer at least 3 correct to pass III: 4 questions, each question has 6 choices, must answer at least 2 correct to pass Which final exam format offers the highest probability to pass? a. Final exam I b. Final exam II c. Final exam III d....
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...flipping a coin and getting a tail b. Throwing a die and getting a 6, then throwing a die and getting a 5 c. Selecting a red marble from a bag, returning the marble to the bag, then selecting a blue marble d. Drawing a spade from a set of poker cards, setting the card aside, then selecting a diamond from the set of poker cards e. All of the above are independent events _____3. Exam scores from a previous STATS 200 course are normally distributed with a mean of 74 and standard deviation of 2.65. Approximately 95% of its area is within: a. One standard deviation of the mean b. Two standard deviations of the mean c. Three standard deviations of the mean d. Depends on the number of outliers e. Must determine the z-scores first to determine the area _____4. You had no chance to study for the final exam and had to guess for each question. The instructor gave you three choices for the final exam: I: 10 questions, each question has 5 choices, must answer at least 4 correct to pass II: 5 questions, each question has 4 choices, must answer at least 3 correct to pass III: 4 questions, each question has 6 choices, must answer at least 2 correct to pass Which final exam format offers the highest probability to pass? a. Final exam I b. Final exam II c. Final exam III d....
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...STAT1029 - Lecture 7 Supplementary Notes Dr. Erwin George, School of Computing and Mathematical Sciences, University of Greenwich September 19, 2012 Dr. Erwin George, Dept. of Mathematics STAT1029 TIPS Invest time learning the language of Mathematics/Statistics/Computing (with all of its special cases and exceptions and conventions). Dr. Erwin George, Dept. of Mathematics STAT1029 TIPS Invest time learning the language of Mathematics/Statistics/Computing (with all of its special cases and exceptions and conventions). Review constantly. Dr. Erwin George, Dept. of Mathematics STAT1029 TIPS Invest time learning the language of Mathematics/Statistics/Computing (with all of its special cases and exceptions and conventions). Review constantly. Do assignments, tutorials, etc. Practise, practise, practise. Dr. Erwin George, Dept. of Mathematics STAT1029 TIPS Invest time learning the language of Mathematics/Statistics/Computing (with all of its special cases and exceptions and conventions). Review constantly. Do assignments, tutorials, etc. Practise, practise, practise. Read lecture notes and try to learn from lectures. Ideally read the lecture material on a topic before the relevant lecture. Dr. Erwin George, Dept. of Mathematics STAT1029 TIPS Invest time learning the language of Mathematics/Statistics/Computing (with all of its special cases and exceptions and conventions). Review constantly. Do assignments, tutorials, etc. Practise...
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...CHAPTER 6 RANDOM VARIABLES PART 1 – Discrete and Continuous Random Variables OBJECTIVE(S): • Students will learn how to use a probability distribution to answer questions about possible values of a random variable. • Students will learn how to calculate the mean and standard deviation of a discrete random variable. • Students will learn how to interpret the mean and standard deviation of a random variable. Random Variable – Probability Distribution - Discrete Random Variable - The probabilities of a probability distribution must satisfy two requirements: a. b. Mean (expected value) of a discrete random variable [pic]= E(X) = = 1. In 2010, there were 1319 games played in the National Hockey League’s regular season. Imagine selecting one of these games at random and then randomly selecting one of the two teams that played in the game. Define the random variable X = number of goals scored by a randomly selected team in a randomly selected game. The table below gives the probability distribution of X: Goals: 0 1 2 3 4 5 6 7 8 9 Probability: 0.061 0.154 0.228 0.229 0.173 0.094 0.041 0.015 0.004 0.001 a. Show that the probability distribution for X is legitimate. b. Make a histogram of the probability distribution. Describe what you see. 0.25 0.20 0.15 0.10 ...
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...the principles of _____ to focus the group in an exchange of ideas, feelings, and experiences on a specific topic. Select one: a. psychology b. sociology c. anthropology d. group dynamics e. communications Feedback The correct answer is: group dynamics Question 2 Complete Marked out of 1 Flag question Question text Which term below is used to describe the combining of several qualitative methods or combining qualitative with quantitative methods? Select one: a. Triangulation b. Dyadic support c. Inter-rater reliability d. Projection e. Component sorts Feedback The correct answer is: Triangulation Question 3 Complete Marked out of 1 Flag question Question text When individual depth interviews are aided by the use of computer-generated visual and auditory aids, the method is known as _____. Select one: a. computer-assisted telephone interviewing (CATI) b. computer-assisted personal interviewing (CAPI) c. online interviewing d. group interviewing e. computer-aided design (CAD) Feedback The correct answer is: computer-assisted personal interviewing (CAPI) Question 4 Complete Marked out of 1 Flag question Question text Qualitative methods that encourage participants to reveal hidden or suppressed attitudes, ideas, emotions, and motives are called _____ techniques. Select one: a. deceptive b. unstructured c. projective d. focus group e. semistructured Feedback The correct answer is: projective Question 5 Complete Marked out of 1 Flag question Question text American Airlines is conducting...
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...Business Statistics SCMA 1000 Winter 2015 Section 2 Assignment 1 Due Tuesday February 25, 2015 Sampling exercise The purpose of this exercise is to convey some basic concepts in regards to sampling while at the same time deriving sampling distributions empirically. Deriving sampling distributions empirically works best when there are a large number of samples. The idea here is that each student in the class will create 20 samples for two populations, using two different sampling procedures, for a total of 80 samples. These samples will be combined into common datasets which will be used in class (and made available to all students in the class. The four sampling contexts will be: 1. Discrete numerical population, sampling without replacement (DNWITHOUT) 2. Discrete numerical population, sampling with replacement (DNWITH) 3. Dichotomous population, sampling without replacement (DWITHOUT) 4. Dichotomous population, sampling with replacement (DWITH) You will generate 20 samples of 5 observations for each of the four sampling contexts above for a total of 80 samples. The deliverable for this exercise will be an Excel spreadsheet. The samples from all four sampling contexts are to be recorded and submitted on the same spreadsheet. The exact details of which will be described below. The spreadsheet will be used to record your samples. The spreadsheet will have 80 rows, one row for each sample. The first two columns (Columns A and B) will be your last...
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...Diagram of Confusion Matrix with Basic Definitions In the confusion matrix, you have two classifications, “Positive” or “Negative,” with the underlying, true conditions being either “+” or “-”. Examples of Binary Conditions: Radar - Enemy Bomber/No Enemy Bomber Diagnostics - Cancer/No Cancer Credit Scoring - Borrower defaults/does not default Outcome Y Table 1: Confusion Matrix. Classification/Test,X “Test Positive” “Test Negative” c d “+” a e f “-” b g h Where, (a,b) is the Marginal Probability Distribution of Condition p(Y ). Note that the rarer of the two is traditionally assigned to “+” and the probability p(a) is called the “incidence” of a. (c,d) is the Marginal Probability Distribution of the Classification p(X) (e,f,g,h) is the Joint Probability Distribution of the Condition and the Classification, p(X, Y ). Another way of representing the confusion matrix is: 1 Outcome 1.2 Table 2: Confusion Matrix, another representation. Classification/Test,X “Test Positive” “Test Negative” c d ”+” a ”True Positive” (TP) ”False Negative” (FN) Y ”-” b ”False Positive” (FP) ”True Negative” (TN) Important Measures of the Confusion Matrix True Positive (TP) Rate: The conditional probability that someone with the condition has a positive test. e = p(T estP ositive|+) a False Negative (FN) Rate: The conditional probability that someone with the condition has a Negative test. f = p(T estN egative|+) a Note that T P rate + F N rate...
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...assessed Lab 5 Built-in functions and methods ASSESSMENT INFORMATION This worksheet is one of the seven assessed lab sheets. It can be assessed within the next 5 weeks. Let me know in advance when you’d like to be assessed. Deadline: Thursday, 17th of July. Do not forget to have it ‘signed off’ after you have been assessed. 1 introduction This laboratory worksheet covers the use of built in functions, methods and classes within the Java programming environment. This laboratory involves the creation of a number of Java programs. Make sure that you save any code you write. Also make sure you save any results or notes that you observe about your work. Note that you are unlikely to complete this worksheet in just one laboratory session. 2 Preliminaries Create a project in Eclipse called CS1002_Lab5 and create a corresponding class (say CS1002_Lab5). Try and organise your work (from the following exercises) into separate methods as we did in the previous worksheet. 3 Strings Copy the following code into your project and run the program. public static void main(String args[]) { double number = 1.0/3.0; System.out.println(number); DecimalFormat number_format = new DecimalFormat("#.##"); String formatted_string = number_format.format(number); System.out.println(formatted_string); } Is it showing you an error? Can you fix it? The DecimalFormat class enables us to format numbers (and other classes) in a variety of ways. In the above example we are formatting the number to...
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...assembling a product. We can define a random variable as x equals to the time in minutes to assemble the product b) The possible outcomes for this experiment is the worker may assemble the product from the first second to whatever how long it takes him or her to assemble the product. Therefore, the random variable x may assume any number greater than zero in minutes, meaning any positive number. It can be noted as x > 0. c) In the experiment x is assuming to be all the value greater than zero variable, so the experimental outcomes are based on a measurement of scale. Thus, the random variable x is a continuous random variable. Answer 2 a) The number of questions answered correctly are the possible outcomes. The experiment is based on a 20-question examination, so all the possible values the random variable can assume are 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20. All the possible outcomes are range from 0 to 20, that means the random variable x can take a finite number of value, therefore, x is a discrete random variable b) The random variable x representing the number of cars arriving a tollbooth may assume all the following values 0, 1, 2, 3,…, n cars in one hour. The values of the random variable x is infinite as x may assume the value of n cars in one hour, it is a discrete random variable because it is bound to stop at the number n cars. c) 0, 1, 2, 3, 4, 5,……, 50 are all the values the random variable x may assume given that...
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...Hashing hash functions collision resolution applications References: Algorithms in Java, Chapter 14 http://www.cs.princeton.edu/introalgsds/42hash 1 Summary of symbol-table implementations implementation unordered array ordered array unordered list ordered list BST randomized BST red-black tree guarantee search N lg N N N N 7 lg N 3 lg N insert N N N N N 7 lg N 3 lg N delete N N N N N 7 lg N 3 lg N search N/2 lg N N/2 N/2 1.39 lg N 1.39 lg N lg N average case insert N/2 N/2 N N/2 1.39 lg N 1.39 lg N lg N delete N/2 N/2 N/2 N/2 ? 1.39 lg N lg N ordered iteration? no yes no yes yes yes yes Can we do better? 2 Optimize Judiciously More computing sins are committed in the name of efficiency (without necessarily achieving it) than for any other single reason including blind stupidity. - William A. Wulf We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. - Donald E. Knuth We follow two rules in the matter of optimization: Rule 1: Don't do it. Rule 2 (for experts only). Don't do it yet - that is, not until you have a perfectly clear and unoptimized solution. - M. A. Jackson Reference: Effective Java by Joshua Bloch. 3 Hashing: basic plan Save items in a key-indexed table (index is a function of the key). Hash function. Method for computing table index from key. hash(“it”) = 3 ?? hash(“times”) = 3 0 1 2 3 4 5 “it” Issues. 1. Computing the hash function 2. Collision resolution:...
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...multiplication task. There will be three experimental groups the groups exposed to the treatment, each will receive a different amount of the “blast”, and the data is collected and then compared with the control group. d) A placebo effect: experimental results caused by expectations alone; any effect on behaviour caused by the administration of an inert substance or condition, which the recipient assumes is an active agent. Yes, the placebo is a concern in the study, the control group will be given an identical drink. Both the experimental group and the control group will have expectations towards the multiplication task after drinking the energy drink. e) One control procedure is giving the three experimental groups and the control group the same exact information in the same exact way for example giving them the same multiplication task. f) Yes, random assignment and random selection are needed because not all variables are known, to get the most accurate results, the experimenter/researcher must assign the groups in random manner in other words randomize, random selection is most related to the external validity of the results and allows the experimenter to make unbiased inferences about the population based on his sample, and random assignment better represents the larger group, and most related to internal...
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...P(A) Part II Although the rules of probability are just basic facts about percents or proportions, we need to be able to use the language of events and their probabilities. Choose an American adult at random. Define two events: A = the person chosen is obese B = the person chosen is overweight, but not obese According to the National Center for Health Statistics, P(A) = 0.32 and P(B) = 0.34. (a) Explain why events A and B are disjoint. Event B rules out any subject that is obese, so there is no type of overlap with event A (b) Say in plain language what the event “A or B” is. What is P(A or B)? “A or B” is the event “The person chosen is obese” P(A or B) = P(A) + P(B) = 0.32 + 0.34 = 0.66 (c) If C is the event that the person chosen has normal weight or less, what is P(C)? P(C) = 1.00 – 0.66 = 0.34 Part III Choose a person aged 19 to 25 years at random and ask, “In the past seven days, how many times did you go to an exercise or fitness center or work out?” Call the response X for short. Based on a large sample survey, here is a probability model for the answer you will get:4 (a) Verify that this is a legitimate discrete probability model. All eight probabilities above when added together are equal to 1 (b) Describe the event X < 7 in words. What is P(X < 7)? “X < 7” means the individual did not work out every day in the...
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...Tutorial 1 Hilda Liyana (BA1401) Introduction to Statistics Prepare answers for the following questions to be discussed in class: Distinguish Between a Population and a Sample 1. Write the word or phrase that best completes each statement or answers the question. Identify the population and the sample. a. A survey of 1378 American households found that 27% of the households own a computer. Population – responses of all American households. Sample – responses of 1378 American Households in the survey. The sample is a subset of the responses of all American Households. b. When 1094 American households were surveyed, it was found that 67% of them owned two cars. Population – responses of all American households. Sample – responses of 1094 American Households in the survey. The sample is a subset of the responses of all American Households. c. A survey of 2625 elementary school children found that 28% of the children could be classified as obese. Population – responses of all elementary school children. Sample – consists of responses of 2625 elementary school children in the survey. The sample is a subset of the responses of all elementary school children. Distinguish Between a Parameter and a Statistic 2. Determine whether the numerical value is a parameter or a statistic. Explain your reasoning. a. A recent survey by the alumni of a major university indicated that the average salary of 10,500 of its 175,000 graduates was $95,000. Sample statistic...
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