First Exam for MTH 23
|
|
|
- Emmeline Miller
- 5 years ago
- Views:
Transcription
1 First Exam for MTH 23 October 5, 2017 Nikos Apostolakis Name: Instructions: This exam contains 6 pages (including this cover page) and 5 questions. Each question is worth 20 points, and so the perfect score in this exam is100 points. Check to see if any pages are missing. Enter your name on the top of this page, and put your initials on the top of every page, in case the pages become separated. You may use only the provided formulae sheet. You may not use your book or notes. You are allowed to use a calculator. You are required to show your work on each problem on this exam. The following rules apply: You have to enter the answer of each question in the provided box or blank line. You have to circle your answer in the multiple choice questions. Mysterious or unsupported answers will not receive full credit. A correct answer, unsupported by calculations, explanation, or other work will receive no credit; an incorrect answer supported by substantially correct calculations and explanations might still receive partial credit. If you need more space, use the back of the last page; clearly indicate when you have done this.
2 MTH23 First Exam - Page 2 of 6 Professor Apostolakis 1. Consider the following set of data: (a) Find the median. (b) Find the mode. Answer: The median is Answer: The mode is (c) Find the sample mean. Answer: x = (d) Find the sample standard deviation. Answer: s = 2. A random sample of the percentage of people in60 different counties, that voted for a certain party in the elections is given below Given that the mean is x = and the standard deviation is s = 8.63: (a) Find a75% Chebyshev interval about the mean for the data set above. Answer: The 75% Chebyshev interval is (b) How many data values does Chebyshev s theorem predict will be within two standard deviations of the mean? Answer: values. (c) How many of the data values are within two standard deviations of the mean? Answer: values. How does this compare to your result in Part (b)?
3 MTH23 First Exam - Page 3 of 6 Professor Apostolakis 3. Let the random variable x represents percentage change in neighborhood population in the last few years, and the random variabley the crime rate in the same neighborhood (measured in crimes per 1,000 people). A random sample of six neighborhoods in a city gave the following table of paired data: x y Given that x = 72, y = 589, x 2 = 1340, y 2 = 72,277, and xy = 9,499: (a) Compute the coefficient of correlationr. Answer: r = (b) Find the equation of the least square line. Answer: The equation is (c) Use the equation from Part (b) to estimate the crime rate for a neighborhood with12% change in population. Answer: The crime rate is estimated to be
4 MTH23 First Exam - Page 4 of 6 Professor Apostolakis 4. Consider the experiment of rolling two dice. The following table lists all possible outcomes Find the probability that the sum of the outcomes of the two dice is less or equal to 5.
5 MTH23 First Exam - Page 5 of 6 Professor Apostolakis 5. In a sales effectiveness seminar, a group of sales representatives tried two approaches to selling a customer a new car. The results are summarized in the table below: Sale No Sale TOTAL Aggressive Passive TOTAL Compute the following probabilities: (a) Find the probability that a customer selected at random bought a new car. (b) Find the probability that a customer selected at random will buy a new car given that an aggressive sales approach is used. (c) Find the probability that a customer selected at random will buy a car and that an aggressive sales approach was used.
6 MTH23 First Exam - Page 6 of 6 Professor Apostolakis Useful Formulae Mean: x = x x n, µ = N x 2 1 Standard Deviation: s = n ( x) 2 x 2 1, σ = N ( x) 2 n 1 N Correlation coefficient: r = SS xy SSxx SS yy, where: SS xx = x 2 1 n ( x ) 2 SS xy = xy 1 n ( x ) ( y ) SS yy = y 2 1 n ( y ) 2 Least Squares Regression Line: ŷ = b x + a, where: b = SS xy SS xx, a = ȳ b x
STAT 1220 FALL 2010 Common Final Exam December 10, 2010
STAT 1220 FALL 2010 Common Final Exam December 10, 2010 PLEASE PRINT THE FOLLOWING INFORMATION: Name: Instructor: Student ID #: Section/Time: THIS EXAM HAS TWO PARTS. PART I. Part I consists of 30 multiple
Stat3011: Solution of Midterm Exam One
1 Stat3011: Solution of Midterm Exam One Fall/2003, Tiefeng Jiang Name: Problem 1 (30 points). Choose one appropriate answer in each of the following questions. 1. (B ) The mean age of five people in a
( ) P = = =
1. On a lunch counter, there are 5 oranges and 6 apples. If 3 pieces of fruit are selected, find the probability that 1 orange and apples are selected. Order does not matter Combinations: 5C1 (1 ) 6C P
Problem max points points scored Total 120. Do all 6 problems.
Solutions to (modified) practice exam 4 Statistics 224 Practice exam 4 FINAL Your Name Friday 12/21/07 Professor Michael Iltis (Lecture 2) Discussion section (circle yours) : section: 321 (3:30 pm M) 322
Hedging and Regression. Hedging and Regression
Returns The discrete return on a stock is the percentage change: S i S i 1 S i 1. The index i can represent days, weeks, hours etc. What happens if we compute returns at infinitesimally short intervals
STATS DOESN T SUCK! ~ CHAPTER 4
CHAPTER 4 QUESTION 1 The Geometric Mean Suppose you make a 2-year investment of $5,000 and it grows by 100% to $10,000 during the first year. During the second year, however, the investment suffers a 50%
Refer to Ex 3-18 on page Record the info for Brand A in a column. Allow 3 adjacent other columns to be added. Do the same for Brand B.
Refer to Ex 3-18 on page 123-124 Record the info for Brand A in a column. Allow 3 adjacent other columns to be added. Do the same for Brand B. Test on Chapter 3 Friday Sept 27 th. You are expected to provide
Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.
Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x
Lecture 37 Sections 11.1, 11.2, Mon, Mar 31, Hampden-Sydney College. Independent Samples: Comparing Means. Robb T. Koether.
: : Lecture 37 Sections 11.1, 11.2, 11.4 Hampden-Sydney College Mon, Mar 31, 2008 Outline : 1 2 3 4 5 : When two samples are taken from two different populations, they may be taken independently or not
FACULTY OF SCIENCE DEPARTMENT OF STATISTICS
FACULTY OF SCIENCE DEPARTMENT OF STATISTICS MODULE ATE1A10 / ATE01A1 ANALYTICAL TECHNIQUES A CAMPUS APK, DFC & SWC SUPPLEMENTARY SUMMATIVE ASSESSMENT DATE 15 JULY 2014 SESSION 15:00 17:00 ASSESSOR MODERATOR
Lecture 9. Probability Distributions. Outline. Outline
Outline Lecture 9 Probability Distributions 6-1 Introduction 6- Probability Distributions 6-3 Mean, Variance, and Expectation 6-4 The Binomial Distribution Outline 7- Properties of the Normal Distribution
Final Exam - section 1. Thursday, December hours, 30 minutes
Econometrics, ECON312 San Francisco State University Michael Bar Fall 2013 Final Exam - section 1 Thursday, December 19 1 hours, 30 minutes Name: Instructions 1. This is closed book, closed notes exam.
MA131 Lecture 8.2. The normal distribution curve can be considered as a probability distribution curve for normally distributed variables.
Normal distribution curve as probability distribution curve The normal distribution curve can be considered as a probability distribution curve for normally distributed variables. The area under the normal
Lecture 9. Probability Distributions
Lecture 9 Probability Distributions Outline 6-1 Introduction 6-2 Probability Distributions 6-3 Mean, Variance, and Expectation 6-4 The Binomial Distribution Outline 7-2 Properties of the Normal Distribution
The Normal Probability Distribution
1 The Normal Probability Distribution Key Definitions Probability Density Function: An equation used to compute probabilities for continuous random variables where the output value is greater than zero
Econometrics and Economic Data
Econometrics and Economic Data Chapter 1 What is a regression? By using the regression model, we can evaluate the magnitude of change in one variable due to a certain change in another variable. For example,
Lecture 18 Section Mon, Feb 16, 2009
The s the Lecture 18 Section 5.3.4 Hampden-Sydney College Mon, Feb 16, 2009 Outline The s the 1 2 3 The 4 s 5 the 6 The s the Exercise 5.12, page 333. The five-number summary for the distribution of income
Lecture 18 Section Mon, Sep 29, 2008
The s the Lecture 18 Section 5.3.4 Hampden-Sydney College Mon, Sep 29, 2008 Outline The s the 1 2 3 The 4 s 5 the 6 The s the Exercise 5.12, page 333. The five-number summary for the distribution of income
Diploma in Financial Management with Public Finance
Diploma in Financial Management with Public Finance Cohort: DFM/09/FT Jan Intake Examinations for 2009 Semester II MODULE: STATISTICS FOR FINANCE MODULE CODE: QUAN 1103 Duration: 2 Hours Reading time:
variance risk Alice & Bob are gambling (again). X = Alice s gain per flip: E[X] = Time passes... Alice (yawning) says let s raise the stakes
![variance risk Alice & Bob are gambling (again). X = Alice s gain per flip: E[X] = Time passes... Alice (yawning) says let s raise the stakes](/thumbs/92/110156041.jpg "variance risk Alice & Bob are gambling (again). X = Alice s gain per flip: E[X] = Time passes... Alice (yawning) says let s raise the stakes")
Alice & Bob are gambling (again). X = Alice s gain per flip: risk E[X] = 0... Time passes... Alice (yawning) says let s raise the stakes E[Y] = 0, as before. Are you (Bob) equally happy to play the new
Midterm Test 1 (Sample) Student Name (PRINT):... Student Signature:... Use pencil, so that you can erase and rewrite if necessary.
MA 180/418 Midterm Test 1 (Sample) Student Name (PRINT):............................................. Student Signature:................................................... Use pencil, so that you can erase
4.2 Probability Distributions
4.2 Probability Distributions Definition. A random variable is a variable whose value is a numerical outcome of a random phenomenon. The probability distribution of a random variable tells us what the
Stat 101 Exam 1 - Embers Important Formulas and Concepts 1
1 Chapter 1 1.1 Definitions Stat 101 Exam 1 - Embers Important Formulas and Concepts 1 1. Data Any collection of numbers, characters, images, or other items that provide information about something. 2.
Review of the Topics for Midterm I
Review of the Topics for Midterm I STA 100 Lecture 9 I. Introduction The objective of statistics is to make inferences about a population based on information contained in a sample. A population is the
Applications of Data Dispersions
1 Applications of Data Dispersions Key Definitions Standard Deviation: The standard deviation shows how far away each value is from the mean on average. Z-Scores: The distance between the mean and a given
Normal Probability Distributions
Normal Probability Distributions Properties of Normal Distributions The most important probability distribution in statistics is the normal distribution. Normal curve A normal distribution is a continuous
Statistics vs. statistics
Statistics vs. statistics Question: What is Statistics (with a capital S)? Definition: Statistics is the science of collecting, organizing, summarizing and interpreting data. Note: There are 2 main ways
15.063: Communicating with Data Summer Recitation 4 Probability III
15.063: Communicating with Data Summer 2003 Recitation 4 Probability III Today s Content Normal RV Central Limit Theorem (CLT) Statistical Sampling 15.063, Summer '03 2 Normal Distribution Any normal RV
York University MATH 1131 (FALL 2005): Introduction to Statistics Mid Term Test Friday, Oct 28, 2005
York University MATH 1131 (FALL 2005): Introduction to Statistics Mid Term Test Friday, Oct 28, 2005 Last Name: Given Names: Student Number: Signature : DO NOT WRITE IN THIS AREA Read the following instructions
In a binomial experiment of n trials, where p = probability of success and q = probability of failure. mean variance standard deviation
Name In a binomial experiment of n trials, where p = probability of success and q = probability of failure mean variance standard deviation µ = n p σ = n p q σ = n p q Notation X ~ B(n, p) The probability
The Normal Distribution
5.1 Introduction to Normal Distributions and the Standard Normal Distribution Section Learning objectives: 1. How to interpret graphs of normal probability distributions 2. How to find areas under the
Chapter 14. Descriptive Methods in Regression and Correlation. Copyright 2016, 2012, 2008 Pearson Education, Inc. Chapter 14, Slide 1
Chapter 14 Descriptive Methods in Regression and Correlation Copyright 2016, 2012, 2008 Pearson Education, Inc. Chapter 14, Slide 1 Section 14.1 Linear Equations with One Independent Variable Copyright
Discrete Random Variables
Discrete Random Variables MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2018 Objectives During this lesson we will learn to: distinguish between discrete and continuous
Chapter Seven. The Normal Distribution
Chapter Seven The Normal Distribution 7-1 Introduction Many continuous variables have distributions that are bellshaped and are called approximately normally distributed variables, such as the heights
Name: 1332 Review for Final. 1. Use the given definitions to answer the following questions. 1,2,3,4,5,6,7,8,9,10
1 Name: 1332 Review for Final 1. Use the given definitions to answer the following questions. U E A B C 1,2,3,4,5,6,7,8,9,10 x x is even 1,2,4,7,8 1,3, 4,5,8 2,4,8 D x x is a power of 2 and 2 x 10 a. Is
Discrete Random Variables
Discrete Random Variables MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2017 Objectives During this lesson we will learn to: distinguish between discrete and continuous
Revised: Spring2009 Huseyin Yuce and Urmi Ghosh-Dastidar Revised: Urmi Ghosh-Dastidar and Grazyna Niezgoda (Spring 2013)
MAT 1272 REVIEW FOR THE FINAL Profs. Han, Bruno, Chen, Singh Revised: Spring2009 Huseyin Yuce and Urmi Ghosh-Dastidar Revised: Urmi Ghosh-Dastidar and Grazyna Niezgoda (Spring 2013) 1) The following are
Diploma Part 2. Quantitative Methods. Examiner s Suggested Answers
Diploma Part 2 Quantitative Methods Examiner s Suggested Answers Question 1 (a) The binomial distribution may be used in an experiment in which there are only two defined outcomes in any particular trial
7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4
7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 - Would the correlation between x and y in the table above be positive or negative? The correlation is negative. -
Chapter 6: The Normal Distribution
Chapter 6: The Normal Distribution Diana Pell Section 6.1: Normal Distributions Note: Recall that a continuous variable can assume all values between any two given values of the variables. Many continuous
Stat 201: Business Statistics I Additional Exercises on Chapter Chapter 3
Stat 201: Business Statistics I Additional Exercises on Chapter Chapter 3 Student Name: Solve the problem. 1) A sociologist recently conducted a survey of senior citizens who have net worths too high to
1. If four dice are rolled, what is the probability of obtaining two identical odd numbers and two identical even numbers?
1 451/551 - Final Review Problems 1 Probability by Sample Points 1. If four dice are rolled, what is the probability of obtaining two identical odd numbers and two identical even numbers? 2. A box contains
Chapter 6: The Normal Distribution
Chapter 6: The Normal Distribution Diana Pell Section 6.1: Normal Distributions Note: Recall that a continuous variable can assume all values between any two given values of the variables. Many continuous
Chapter 2: Descriptive Statistics. Mean (Arithmetic Mean): Found by adding the data values and dividing the total by the number of data.
-3: Measure of Central Tendency Chapter : Descriptive Statistics The value at the center or middle of a data set. It is a tool for analyzing data. Part 1: Basic concepts of Measures of Center Ex. Data
Mathematical Methods: Practice Problem Solving Task - Probability
Mathematical Methods: Practice Problem Solving Task - Probability Question 1 refers to the following graph The following graph shows the probabilities of the 5 outcomes (1 to 5) from a spinner, with one
Diploma in Business Administration Part 2. Quantitative Methods. Examiner s Suggested Answers
Cumulative frequency Diploma in Business Administration Part Quantitative Methods Examiner s Suggested Answers Question 1 Cumulative Frequency Curve 1 9 8 7 6 5 4 3 1 5 1 15 5 3 35 4 45 Weeks 1 (b) x f
Section 6.5. The Central Limit Theorem
Section 6.5 The Central Limit Theorem Idea Will allow us to combine the theory from 6.4 (sampling distribution idea) with our central limit theorem and that will allow us the do hypothesis testing in the
IOP 201-Q (Industrial Psychological Research) Tutorial 5
IOP 201-Q (Industrial Psychological Research) Tutorial 5 TRUE/FALSE [1 point each] Indicate whether the sentence or statement is true or false. 1. To establish a cause-and-effect relation between two variables,
Chapter 8 Homework Solutions Compiled by Joe Kahlig. speed(x) freq 25 x < x < x < x < x < x < 55 5
H homework problems, C-copyright Joe Kahlig Chapter Solutions, Page Chapter Homework Solutions Compiled by Joe Kahlig. (a) finite discrete (b) infinite discrete (c) continuous (d) finite discrete (e) continuous.
Econ 6900: Statistical Problems. Instructor: Yogesh Uppal
Econ 6900: Statistical Problems Instructor: Yogesh Uppal Email: yuppal@ysu.edu Lecture Slides 4 Random Variables Probability Distributions Discrete Distributions Discrete Uniform Probability Distribution
Quantitative Methods for Economics, Finance and Management (A86050 F86050)
Quantitative Methods for Economics, Finance and Management (A86050 F86050) Matteo Manera matteo.manera@unimib.it Marzio Galeotti marzio.galeotti@unimi.it 1 This material is taken and adapted from Guy Judge
Random Variables and Applications OPRE 6301
Random Variables and Applications OPRE 6301 Random Variables... As noted earlier, variability is omnipresent in the business world. To model variability probabilistically, we need the concept of a random
Economics 483. Midterm Exam. 1. Consider the following monthly data for Microsoft stock over the period December 1995 through December 1996:
University of Washington Summer Department of Economics Eric Zivot Economics 3 Midterm Exam This is a closed book and closed note exam. However, you are allowed one page of handwritten notes. Answer all
8.1 Estimation of the Mean and Proportion
8.1 Estimation of the Mean and Proportion Statistical inference enables us to make judgments about a population on the basis of sample information. The mean, standard deviation, and proportions of a population
CHAPTER 2 Describing Data: Numerical
CHAPTER Multiple-Choice Questions 1. A scatter plot can illustrate all of the following except: A) the median of each of the two variables B) the range of each of the two variables C) an indication of
Statistics Class 15 3/21/2012
Statistics Class 15 3/21/2012 Quiz 1. Cans of regular Pepsi are labeled to indicate that they contain 12 oz. Data Set 17 in Appendix B lists measured amounts for a sample of Pepsi cans. The same statistics
CHAPTER 6 Random Variables
CHAPTER 6 Random Variables 6.3 Binomial and Geometric Random Variables The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers Binomial and Geometric Random
* Point estimate for P is: x n
Estimation and Confidence Interval Estimation and Confidence Interval: Single Mean: To find the confidence intervals for a single mean: 1- X ± ( Z 1 σ n σ known S - X ± (t 1,n 1 n σ unknown Estimation
Normal Curves & Sampling Distributions
Chapter 7 Name Normal Curves & Sampling Distributions Section 7.1 Graphs of Normal Probability Distributions Objective: In this lesson you learned how to graph a normal curve and apply the empirical rule
Chapter 3-Describing Data: Numerical Measures
Chapter 3-Describing Data: Numerical Measures Jie Zhang Account and Information Systems Department College of Business Administration The University of Texas at El Paso jzhang6@utep.edu Jie Zhang, QMB
Page Points Score Total: 100
Math 1130 Spring 2019 Sample Midterm 3a 4/11/19 Name (Print): Username.#: Lecturer: Rec. Instructor: Rec. Time: This exam contains 9 pages (including this cover page) and 9 problems. Check to see if any
Simple Descriptive Statistics
Simple Descriptive Statistics These are ways to summarize a data set quickly and accurately The most common way of describing a variable distribution is in terms of two of its properties: Central tendency
Factoring Simple Trinomials February 24, What's Going On? What's the Pattern? Working Backwards. Finding Factors
What's Going On? What's the Pattern? Working Backwards Finding Factors Learning Goal I will be able to factor standard form equations when a = 1. What's the Pattern? (x + 2)(x + 3) = x 2 + 5x + 6 (x +
Numerical Descriptions of Data
Numerical Descriptions of Data Measures of Center Mean x = x i n Excel: = average ( ) Weighted mean x = (x i w i ) w i x = data values x i = i th data value w i = weight of the i th data value Median =
Tutorial 6. Sampling Distribution. ENGG2450A Tutors. 27 February The Chinese University of Hong Kong 1/6
Tutorial 6 Sampling Distribution ENGG2450A Tutors The Chinese University of Hong Kong 27 February 2017 1/6 Random Sample and Sampling Distribution 2/6 Random sample Consider a random variable X with distribution
Chapter 5 Discrete Probability Distributions. Random Variables Discrete Probability Distributions Expected Value and Variance
Chapter 5 Discrete Probability Distributions Random Variables Discrete Probability Distributions Expected Value and Variance.40.30.20.10 0 1 2 3 4 Random Variables A random variable is a numerical description
1. State whether the following groups are populations or samples. You are encouraged to justify your answers.
MATH 2210 Exam 1 Review Solution Note: This review is NOT comprehensive, so do not limit your study to it. 1. State whether the following groups are populations or samples. You are encouraged to justify
1 Describing Distributions with numbers
1 Describing Distributions with numbers Only for quantitative variables!! 1.1 Describing the center of a data set The mean of a set of numerical observation is the familiar arithmetic average. To write
Section Introduction to Normal Distributions
Section 6.1-6.2 Introduction to Normal Distributions 2012 Pearson Education, Inc. All rights reserved. 1 of 105 Section 6.1-6.2 Objectives Interpret graphs of normal probability distributions Find areas
5.7 Probability Distributions and Variance
160 CHAPTER 5. PROBABILITY 5.7 Probability Distributions and Variance 5.7.1 Distributions of random variables We have given meaning to the phrase expected value. For example, if we flip a coin 100 times,
Quantitative Methods
THE ASSOCIATION OF BUSINESS EXECUTIVES DIPLOMA PART 2 QM Quantitative Methods afternoon 27 November 2002 1 Time allowed: 3 hours. 2 Answer any FOUR questions. 3 All questions carry 25 marks. Marks for
MTP_FOUNDATION_Syllabus 2012_Dec2016 SET - I. Paper 4-Fundamentals of Business Mathematics and Statistics
SET - I Paper 4-Fundamentals of Business Mathematics and Statistics Full Marks: 00 Time allowed: 3 Hours Section A (Fundamentals of Business Mathematics) I. Answer any two questions. Each question carries
Midterm Exam III Review
Midterm Exam III Review Dr. Joseph Brennan Math 148, BU Dr. Joseph Brennan (Math 148, BU) Midterm Exam III Review 1 / 25 Permutations and Combinations ORDER In order to count the number of possible ways
Chapter 8: The Binomial and Geometric Distributions
Chapter 8: The Binomial and Geometric Distributions 8.1 Binomial Distributions 8.2 Geometric Distributions 1 Let me begin with an example My best friends from Kent School had three daughters. What is the
INSTITUTE OF ACTUARIES OF INDIA EXAMINATIONS. 20 th May Subject CT3 Probability & Mathematical Statistics
INSTITUTE OF ACTUARIES OF INDIA EXAMINATIONS 20 th May 2013 Subject CT3 Probability & Mathematical Statistics Time allowed: Three Hours (10.00 13.00) Total Marks: 100 INSTRUCTIONS TO THE CANDIDATES 1.
σ e, which will be large when prediction errors are Linear regression model
Linear regression model we assume that two quantitative variables, x and y, are linearly related; that is, the population of (x, y) pairs are related by an ideal population regression line y = α + βx +
This homework assignment uses the material on pages ( A moving average ).
Module 2: Time series concepts HW Homework assignment: equally weighted moving average This homework assignment uses the material on pages 14-15 ( A moving average ). 2 Let Y t = 1/5 ( t + t-1 + t-2 +
Business Statistics: A First Course
Business Statistics: A First Course Fifth Edition Chapter 12 Correlation and Simple Linear Regression Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc. Chap 12-1 Learning Objectives In this
A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x)
Section 6-2 I. Continuous Probability Distributions A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x) to represent a probability density
Chapter 3 - Lecture 3 Expected Values of Discrete Random Va
Chapter 3 - Lecture 3 Expected Values of Discrete Random Variables October 5th, 2009 Properties of expected value Standard deviation Shortcut formula Properties of the variance Properties of expected value
How can we factor polynomials?
How can we factor polynomials? Factoring refers to writing something as a product. Factoring completely means that all of the factors are relatively prime (they have a GCF of 1). Methods of factoring:
MATH CALCULUS & STATISTICS/BUSN - PRACTICE EXAM #2 - SUMMER DR. DAVID BRIDGE
MATH 2053 - CALCULUS & STATISTICS/BUSN - PRACTICE EXAM #2 - SUMMER 2007 - DR. DAVID BRIDGE MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Find the
The normal distribution is a theoretical model derived mathematically and not empirically.
Sociology 541 The Normal Distribution Probability and An Introduction to Inferential Statistics Normal Approximation The normal distribution is a theoretical model derived mathematically and not empirically.
Statistics, Their Distributions, and the Central Limit Theorem
Statistics, Their Distributions, and the Central Limit Theorem MATH 3342 Sections 5.3 and 5.4 Sample Means Suppose you sample from a popula0on 10 0mes. You record the following sample means: 10.1 9.5 9.6
Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc.
Chapter 8 Measures of Center Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc. Data that can only be integer
Factoring completely is factoring a product down to a product of prime factors. 24 (2)(12) (2)(2)(6) (2)(2)(2)(3)
Factoring Contents Introduction... 2 Factoring Polynomials... 4 Greatest Common Factor... 4 Factoring by Grouping... 5 Factoring a Trinomial with a Table... 5 Factoring a Trinomial with a Leading Coefficient
YEAR 12 Trial Exam Paper FURTHER MATHEMATICS. Written examination 1. Worked solutions
YEAR 12 Trial Exam Paper 2018 FURTHER MATHEMATICS Written examination 1 Worked solutions This book presents: worked solutions explanatory notes tips on how to approach the exam. This trial examination
Central Limit Theorem, Joint Distributions Spring 2018
Central Limit Theorem, Joint Distributions 18.5 Spring 218.5.4.3.2.1-4 -3-2 -1 1 2 3 4 Exam next Wednesday Exam 1 on Wednesday March 7, regular room and time. Designed for 1 hour. You will have the full
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Exam Name The bar graph shows the number of tickets sold each week by the garden club for their annual flower show. ) During which week was the most number of tickets sold? ) A) Week B) Week C) Week 5
Page Points Score Total: 100
Math 1130 Spring 2019 Sample Midterm 2b 2/28/19 Name (Print): Username.#: Lecturer: Rec. Instructor: Rec. Time: This exam contains 10 pages (including this cover page) and 9 problems. Check to see if any
Lecture 6: Confidence Intervals
Lecture 6: Confidence Intervals Taeyong Park Washington University in St. Louis February 22, 2017 Park (Wash U.) U25 PS323 Intro to Quantitative Methods February 22, 2017 1 / 29 Today... Review of sampling
MA 1125 Lecture 14 - Expected Values. Wednesday, October 4, Objectives: Introduce expected values.
MA 5 Lecture 4 - Expected Values Wednesday, October 4, 27 Objectives: Introduce expected values.. Means, Variances, and Standard Deviations of Probability Distributions Two classes ago, we computed the
Quantitative Methods
THE ASSOCIATION OF BUSINESS EXECUTIVES DIPLOMA PART 2 QM Quantitative Methods afternoon 26 May 2004 1 Time allowed: 3 hours. 2 Answer any FOUR questions. 3 All questions carry 25 marks. Marks for subdivisions
Review for Final Exam Spring 2014 Jeremy Orloff and Jonathan Bloom
Review for Final Exam 18.05 Spring 2014 Jeremy Orloff and Jonathan Bloom THANK YOU!!!! JON!! PETER!! RUTHI!! ERIKA!! ALL OF YOU!!!! Probability Counting Sets Inclusion-exclusion principle Rule of product
Point-Biserial and Biserial Correlations
Chapter 302 Point-Biserial and Biserial Correlations Introduction This procedure calculates estimates, confidence intervals, and hypothesis tests for both the point-biserial and the biserial correlations.
CSC Advanced Scientific Programming, Spring Descriptive Statistics
CSC 223 - Advanced Scientific Programming, Spring 2018 Descriptive Statistics Overview Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions.
T.I.H.E. IT 233 Statistics and Probability: Sem. 1: 2013 ESTIMATION
In Inferential Statistic, ESTIMATION (i) (ii) is called the True Population Mean and is called the True Population Proportion. You must also remember that are not the only population parameters. There
Statistics for Managers Using Microsoft Excel 7 th Edition
Statistics for Managers Using Microsoft Excel 7 th Edition Chapter 5 Discrete Probability Distributions Statistics for Managers Using Microsoft Excel 7e Copyright 014 Pearson Education, Inc. Chap 5-1 Learning
STAT 3090 Test 2 - Version B Fall Student s Printed Name: PLEASE READ DIRECTIONS!!!!
Student s Printed Name: Instructor: XID: Section #: Read each question very carefully. You are permitted to use a calculator on all portions of this exam. You are NOT allowed to use any textbook, notes,
Econometric Methods for Valuation Analysis
Econometric Methods for Valuation Analysis Margarita Genius Dept of Economics M. Genius (Univ. of Crete) Econometric Methods for Valuation Analysis Cagliari, 2017 1 / 26 Correlation Analysis Simple Regression