( ) P = = =
|
|
- Natalie Holt
- 6 years ago
- Views:
Transcription
1 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 orange and apples = C ! 6! 5 4! 6 5 4! C C = = = = 75 (5 1)!1! (6 )!! 4! 4! 11! ! C = = = = ! 3! 8! ( ) P = = = Place the word True or False in front of each statement (justify your answer):.1 TRUE In a combination, the arrangement ABC is the same as BCA. FALSE If the events A and B are independent, then A and = A) + Ans: A and = A).3 FALSE If A) = 0.6 and A and = 0.3, then B A) = 0.9 A and.3 Ans: P ( B A) = = = A).6.4 FALSE If an event is certain to occur, its probability E) = 0. Ans: E)=1.5 FALSE If E is an event, then E) + E) > 1 Ans: E) + E) = 1 3. When 7 dice are rolled, the sample space consists of how many events? = 6 4. An inspector must select 3 tests to perform in a certain order on a manufactured part. He has a choice of 7 tests. How many ways can he perform the 3 different tests? Order is important Permutation: 7! ! 7 P3 = = = 10 (7 3)! 4! 5. An urn contains 5 red balls, 5 blue balls, and 6 white balls. A ball is selected, its color is noted, and then it is placed back into the urn. A nd ball is selected, its color noted, and then placed back into the urn. Finally, a 3rd ball is selected and its color noted. Find the probability of 5.a. Selecting a blue, a red, and a red ball Independent events: P ( B & R & = = = b. Selecting a white, a blue, and a red ball Independent events: P ( W & B & = W ) = = /0/007
2 6. Compute the mean for the grouped data shown below. Use the empty columns to organize your data. Show all formulas used. Classes Frequency: f Midpoint: m f * m TOTALS 6 39 f 39 = m = = 6.5 n 6 7. Compute the population Variance ( σ ) and Standard Deviation (σ ) for the following population: 1, 0, 3, 1,, 5. Use the definition formula and show all steps and calculations Calculate mean: µ = = = ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) σ = = = = = =.6667 round to one decimal position σ =.7 16 σ = = round to one decimal position σ = The lengths of service (in years) of the Chief Justice of the Supreme Court are: 7, 1, 5, 35, 8, 10, 15,, 11, 10, 1, 7 Arrange numbers in ascending order: a. Mean = ( )/1 = 163/1 = Mean = b. Median = (10+11)/ = c. Mode = 7, 10 (7 occurs times. 10 occurs times) 8.d. Five-number summary (Low, Q1, m, Q3, High): Low = 1, Q1 = (7+7) = 7, m = 10.5, Q3 = (15+)/ = 18.5, High = 35 8.e. Construct a boxplot BoxPlot Data 8.f. Is the number 35 an outliner? Justify your answer: No Q IQR = (1.5)(18.5 7) = = = > 35 6/0/007
3 8.g. Calculate a rough estimate of the standard deviation: Rule of thumb Range s = = = = In a dental survey of third-grade students, the following distribution for the number of cavities was found. Find the average number of cavities using the weighted mean method. Number of Students Number of cavities Weighted average = = = = Find the percentile rank for the test score 15 for the scores: 16, 1, 5, 15, 0, 1. Arrange scores in ascending order: 5, 1, 15, 16, 0, 1; P = (number values below ) / (total number of values) =.5/6 = Percentile rank = 41.67% 11. What score corresponds to the 75% percentile for the scores in problem 10? 0 c = (np)/100 = (6)(75)/100 = 4.5 Round up to next whole number: c = 5; Count to the 5 th position from the left, which corresponds to the score The average age of the accountants at YZ Corp. is 6 years, with a standard deviation of 6 years; the average salary of the accountants is $31,000, with a standard deviation of $4,000. Compare the variation of age and income. 6 CVar = σ age % = 6 = CVar salary Accountants age is more variable than their salary. = σ % = = 13. Which score indicates the highest relative position? a. A score of 3. on a test with mean of 4.6 and σ = 1.5 b. A score of 630 on a test with mean of 800 and σ = 00 c. A score of 43 on a test with mean of 50 and σ = 5 Calculate the z-score for each case and choose the highest value: µ a. z = = = 0.93 σ 1.5 µ b. z = = = 0.85 σ 00 c. µ z = = = 1.4 σ 5 Answer: the score of 630 has the highest relative position. 3 6/0/007
4 14. The probability that a student owns a car is 0.65, and the probability that a student owns a computer is 0.8. If the probability that a student owns both is 0.55, what is the probability that a given student owns neither a car nor a computer? car or computer) = car) + computer) - both) = = 0.9 neither) = = The numbers of endangered species for several groups are listed here. Location Mammals Birds Reptiles Amphibians TOTALS USA Foreign TOTALS If one endangered species is selected at random, find the probability that it is a) Foreign or a mammal b) It is a bird given that it is found in the USA c) Warm-blooded Add the TOTALS entries to the table a) Foreign or a Mammal) = 498/ /663 51/663 = 561/663 or b) Bird USA) = Bird & USA) / USA) = (78/663) / (164/663) = 78/165 = c) Warm-blooded) = 314/ /663 = 567/663 or A sales representative who visits customers at home finds that she sells 0, 1,, 3, or 4 items according to the following distribution: Items Sold Frequency Find the probability that she sells the following: a) Exactly one item: Exactly 1) = 10/4 = 5/1 b) At least one item: At Least 1) = ( )/4 = 16/4 = /3 17. In a distribution of 00 values, the mean is 50 and the standard deviation is 5. Use Chebyshev s theorem to answer the following questions: a. At least how many values will fall between 30 and 70? b. Find the range of values that at least 80% of the values lie a. Step 1: Subtract the mean from the larger value: = 0 (or subtract the lower value from the mean: = 0) Step : Divide the difference by the standard deviation to get k: k = 0/5 = 4 Step 3: Use Chebyshev s theorem to find the percentage: 1-1/k = 1 1/4 = 1 1/16 = = = or 93.75% Step 4: Multiply this percentage with the total values to find the number within the given interval: = /0/007
5 b. Step1: Use Chebyshev s theorem to find k (since we know the percentage): 0.80 = 1 1/k 1/k = 1 1/k = /k = = (0.0)k 1/0.0 = k 5 = k k = SQRT(5) k =.4 Step: Upper value: mean +.4(standard dev) = (5) = = 61. Lower Value: mean.4(standard dev) = 50.4(5) = = In a distribution of 500 values with a Bell-shaped distribution, the mean is 50 and the standard deviation is 5. Use the Empirical rule to answer the following: a. How many values will fall in the interval 35 to 65? b. Find the interval that contains 95% of the value a = 15 Divide by standard deviation: 15/5 = % of values will lie between 35 and 65. Multiply this percentage with 500 to find total number of values in interval 35-65: 500 * = b. 95% of the values lie within standard deviations of the mean: * 5 = 10 Interval: 50 ± 10 [40, 60] 99.7% 95% 68% 3s s 1s + 1s + s + 3s Empirical Rule for Bell-Shaped Distributions 5 6/0/007
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
More informationRefer 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
More informationMeasures of Variation. Section 2-5. Dotplots of Waiting Times. Waiting Times of Bank Customers at Different Banks in minutes. Bank of Providence
Measures of Variation Section -5 1 Waiting Times of Bank Customers at Different Banks in minutes Jefferson Valley Bank 6.5 6.6 6.7 6.8 7.1 7.3 7.4 Bank of Providence 4. 5.4 5.8 6. 6.7 8.5 9.3 10.0 Mean
More informationApplications 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
More informationNumerical 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 =
More information3.1 Measures of Central Tendency
3.1 Measures of Central Tendency n Summation Notation x i or x Sum observation on the variable that appears to the right of the summation symbol. Example 1 Suppose the variable x i is used to represent
More informationStatistics 114 September 29, 2012
Statistics 114 September 29, 2012 Third Long Examination TGCapistrano I. TRUE OR FALSE. Write True if the statement is always true; otherwise, write False. 1. The fifth decile is equal to the 50 th percentile.
More informationMath 2311 Bekki George Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment
Math 2311 Bekki George bekki@math.uh.edu Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment Class webpage: http://www.math.uh.edu/~bekki/math2311.html Math 2311 Class
More informationThe 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
More informationChapter Seven: Confidence Intervals and Sample Size
Chapter Seven: Confidence Intervals and Sample Size A point estimate is: The best point estimate of the population mean µ is the sample mean X. Three Properties of a Good Estimator 1. Unbiased 2. Consistent
More informationData 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
More information4.1 Probability Distributions
Probability and Statistics Mrs. Leahy Chapter 4: Discrete Probability Distribution ALWAYS KEEP IN MIND: The Probability of an event is ALWAYS between: and!!!! 4.1 Probability Distributions Random Variables
More informationChapter 3. Descriptive Measures. Copyright 2016, 2012, 2008 Pearson Education, Inc. Chapter 3, Slide 1
Chapter 3 Descriptive Measures Copyright 2016, 2012, 2008 Pearson Education, Inc. Chapter 3, Slide 1 Chapter 3 Descriptive Measures Mean, Median and Mode Copyright 2016, 2012, 2008 Pearson Education, Inc.
More informationMeasures of Center. Mean. 1. Mean 2. Median 3. Mode 4. Midrange (rarely used) Measure of Center. Notation. Mean
Measure of Center Measures of Center The value at the center or middle of a data set 1. Mean 2. Median 3. Mode 4. Midrange (rarely used) 1 2 Mean Notation The measure of center obtained by adding the values
More information3.3-Measures of Variation
3.3-Measures of Variation Variation: Variation is a measure of the spread or dispersion of a set of data from its center. Common methods of measuring variation include: 1. Range. Standard Deviation 3.
More informationReview. What is the probability of throwing two 6s in a row with a fair die? a) b) c) d) 0.333
Review In most card games cards are dealt without replacement. What is the probability of being dealt an ace and then a 3? Choose the closest answer. a) 0.0045 b) 0.0059 c) 0.0060 d) 0.1553 Review What
More informationLecture 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
More informationLecture 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
More informationEmpirical Rule (P148)
Interpreting the Standard Deviation Numerical Descriptive Measures for Quantitative data III Dr. Tom Ilvento FREC 408 We can use the standard deviation to express the proportion of cases that might fall
More informationExample - Let X be the number of boys in a 4 child family. Find the probability distribution table:
Chapter7 Probability Distributions and Statistics Distributions of Random Variables tthe value of the result of the probability experiment is a RANDOM VARIABLE. Example - Let X be the number of boys in
More informationCHAPTER 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
More information1. 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
More informationTest Bank Elementary Statistics 2nd Edition William Navidi
Test Bank Elementary Statistics 2nd Edition William Navidi Completed downloadable package TEST BANK for Elementary Statistics 2nd Edition by William Navidi, Barry Monk: https://testbankreal.com/download/elementary-statistics-2nd-edition-test-banknavidi-monk/
More informationECON 214 Elements of Statistics for Economists 2016/2017
ECON 214 Elements of Statistics for Economists 2016/2017 Topic The Normal Distribution Lecturer: Dr. Bernardin Senadza, Dept. of Economics bsenadza@ug.edu.gh College of Education School of Continuing and
More informationDavid Tenenbaum GEOG 090 UNC-CH Spring 2005
Simple Descriptive Statistics Review and Examples You will likely make use of all three measures of central tendency (mode, median, and mean), as well as some key measures of dispersion (standard deviation,
More informationChapter 7 Study Guide: The Central Limit Theorem
Chapter 7 Study Guide: The Central Limit Theorem Introduction Why are we so concerned with means? Two reasons are that they give us a middle ground for comparison and they are easy to calculate. In this
More informationChapter 3 Descriptive Statistics: Numerical Measures Part A
Slides Prepared by JOHN S. LOUCKS St. Edward s University Slide 1 Chapter 3 Descriptive Statistics: Numerical Measures Part A Measures of Location Measures of Variability Slide Measures of Location Mean
More informationHandout 4 numerical descriptive measures part 2. Example 1. Variance and Standard Deviation for Grouped Data. mf N 535 = = 25
Handout 4 numerical descriptive measures part Calculating Mean for Grouped Data mf Mean for population data: µ mf Mean for sample data: x n where m is the midpoint and f is the frequency of a class. Example
More informationExamples: Random Variables. Discrete and Continuous Random Variables. Probability Distributions
Random Variables Examples: Random variable a variable (typically represented by x) that takes a numerical value by chance. Number of boys in a randomly selected family with three children. Possible values:
More informationA.REPRESENTATION OF DATA
A.REPRESENTATION OF DATA (a) GRAPHS : PART I Q: Why do we need a graph paper? Ans: You need graph paper to draw: (i) Histogram (ii) Cumulative Frequency Curve (iii) Frequency Polygon (iv) Box-and-Whisker
More information2011 Pearson Education, Inc
Statistics for Business and Economics Chapter 4 Random Variables & Probability Distributions Content 1. Two Types of Random Variables 2. Probability Distributions for Discrete Random Variables 3. The Binomial
More information7 THE CENTRAL LIMIT THEOREM
CHAPTER 7 THE CENTRAL LIMIT THEOREM 373 7 THE CENTRAL LIMIT THEOREM Figure 7.1 If you want to figure out the distribution of the change people carry in their pockets, using the central limit theorem and
More informationCH 5 Normal Probability Distributions Properties of the Normal Distribution
Properties of the Normal Distribution Example A friend that is always late. Let X represent the amount of minutes that pass from the moment you are suppose to meet your friend until the moment your friend
More informationAs you draw random samples of size n, as n increases, the sample means tend to be normally distributed.
The Central Limit Theorem The central limit theorem (clt for short) is one of the most powerful and useful ideas in all of statistics. The clt says that if we collect samples of size n with a "large enough
More informationIOP 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,
More informationChapter 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.
More information(c) The probability that a randomly selected driver having a California drivers license
Statistics Test 2 Name: KEY 1 Classify each statement as an example of classical probability, empirical probability, or subjective probability (a An executive for the Krusty-O cereal factory makes an educated
More informationSTAB22 section 1.3 and Chapter 1 exercises
STAB22 section 1.3 and Chapter 1 exercises 1.101 Go up and down two times the standard deviation from the mean. So 95% of scores will be between 572 (2)(51) = 470 and 572 + (2)(51) = 674. 1.102 Same idea
More informationChapter 3. Lecture 3 Sections
Chapter 3 Lecture 3 Sections 3.4 3.5 Measure of Position We would like to compare values from different data sets. We will introduce a z score or standard score. This measures how many standard deviation
More information1 Exercise One. 1.1 Calculate the mean ROI. Note that the data is not grouped! Below you find the raw data in tabular form:
1 Exercise One Note that the data is not grouped! 1.1 Calculate the mean ROI Below you find the raw data in tabular form: Obs Data 1 18.5 2 18.6 3 17.4 4 12.2 5 19.7 6 5.6 7 7.7 8 9.8 9 19.9 10 9.9 11
More informationECON 214 Elements of Statistics for Economists
ECON 214 Elements of Statistics for Economists Session 7 The Normal Distribution Part 1 Lecturer: Dr. Bernardin Senadza, Dept. of Economics Contact Information: bsenadza@ug.edu.gh College of Education
More informationMAKING SENSE OF DATA Essentials series
MAKING SENSE OF DATA Essentials series THE NORMAL DISTRIBUTION Copyright by City of Bradford MDC Prerequisites Descriptive statistics Charts and graphs The normal distribution Surveys and sampling Correlation
More informationSimple 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
More informationChapter 3. Numerical Descriptive Measures. Copyright 2016 Pearson Education, Ltd. Chapter 3, Slide 1
Chapter 3 Numerical Descriptive Measures Copyright 2016 Pearson Education, Ltd. Chapter 3, Slide 1 Objectives In this chapter, you learn to: Describe the properties of central tendency, variation, and
More informationNo, because np = 100(0.02) = 2. The value of np must be greater than or equal to 5 to use the normal approximation.
1) If n 100 and p 0.02 in a binomial experiment, does this satisfy the rule for a normal approximation? Why or why not? No, because np 100(0.02) 2. The value of np must be greater than or equal to 5 to
More informationWhat type of distribution is this? tml
Warm Up Calculate the average Broncos score for the 2013 Season! 24, 27, 10, 10, 34, 37, 20, 51, 35, 31, 27, 28, 45, 33, 35, 52, 52, 37, 41, 49, 24, 26 What type of distribution is this? http://www.mathsisfun.com/data/quincunx.h
More informationDiscrete Probability Distribution
1 Discrete Probability Distribution Key Definitions Discrete Random Variable: Has a countable number of values. This means that each data point is distinct and separate. Continuous Random Variable: Has
More informationFirst Exam for MTH 23
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
More informationMANAGEMENT PRINCIPLES AND STATISTICS (252 BE)
MANAGEMENT PRINCIPLES AND STATISTICS (252 BE) Normal and Binomial Distribution Applied to Construction Management Sampling and Confidence Intervals Sr Tan Liat Choon Email: tanliatchoon@gmail.com Mobile:
More information5.4 Normal Approximation of the Binomial Distribution Lesson MDM4U Jensen
5.4 Normal Approximation of the Binomial Distribution Lesson MDM4U Jensen Review From Yesterday Bernoulli Trials have 3 properties: 1. 2. 3. Binomial Probability Distribution In a binomial experiment with
More informationInvitational Mathematics Competition. Statistics Individual Test
Invitational Mathematics Competition Statistics Individual Test December 12, 2016 1 MULTIPLE CHOICE. If you think that the correct answer is not present, then choose 'E' for none of the above. 1) What
More information2 DESCRIPTIVE STATISTICS
Chapter 2 Descriptive Statistics 47 2 DESCRIPTIVE STATISTICS Figure 2.1 When you have large amounts of data, you will need to organize it in a way that makes sense. These ballots from an election are rolled
More informationDATA SUMMARIZATION AND VISUALIZATION
APPENDIX DATA SUMMARIZATION AND VISUALIZATION PART 1 SUMMARIZATION 1: BUILDING BLOCKS OF DATA ANALYSIS 294 PART 2 PART 3 PART 4 VISUALIZATION: GRAPHS AND TABLES FOR SUMMARIZING AND ORGANIZING DATA 296
More informationSection3-2: Measures of Center
Chapter 3 Section3-: Measures of Center Notation Suppose we are making a series of observations, n of them, to be exact. Then we write x 1, x, x 3,K, x n as the values we observe. Thus n is the total number
More informationKey Objectives. Module 2: The Logic of Statistical Inference. Z-scores. SGSB Workshop: Using Statistical Data to Make Decisions
SGSB Workshop: Using Statistical Data to Make Decisions Module 2: The Logic of Statistical Inference Dr. Tom Ilvento January 2006 Dr. Mugdim Pašić Key Objectives Understand the logic of statistical inference
More informationThe Central Limit Theorem for Sample Means (Averages)
The Central Limit Theorem for Sample Means (Averages) By: OpenStaxCollege Suppose X is a random variable with a distribution that may be known or unknown (it can be any distribution). Using a subscript
More informationCounting Basics. Venn diagrams
Counting Basics Sets Ways of specifying sets Union and intersection Universal set and complements Empty set and disjoint sets Venn diagrams Counting Inclusion-exclusion Multiplication principle Addition
More informationSection 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
More information5.4 Normal Approximation of the Binomial Distribution
5.4 Normal Approximation of the Binomial Distribution Bernoulli Trials have 3 properties: 1. Only two outcomes - PASS or FAIL 2. n identical trials Review from yesterday. 3. Trials are independent - probability
More informationContents. The Binomial Distribution. The Binomial Distribution The Normal Approximation to the Binomial Left hander example
Contents The Binomial Distribution The Normal Approximation to the Binomial Left hander example The Binomial Distribution When you flip a coin there are only two possible outcomes - heads or tails. This
More informationThe 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
More informationNOTES: Chapter 4 Describing Data
NOTES: Chapter 4 Describing Data Intro to Statistics COLYER Spring 2017 Student Name: Page 2 Section 4.1 ~ What is Average? Objective: In this section you will understand the difference between the three
More informationMidterm 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
More informationReview: Chebyshev s Rule. Measures of Dispersion II. Review: Empirical Rule. Review: Empirical Rule. Auto Batteries Example, p 59.
Review: Chebyshev s Rule Measures of Dispersion II Tom Ilvento STAT 200 Is based on a mathematical theorem for any data At least ¾ of the measurements will fall within ± 2 standard deviations from the
More information7. 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. -
More informationBoth the quizzes and exams are closed book. However, For quizzes: Formulas will be provided with quiz papers if there is any need.
Both the quizzes and exams are closed book. However, For quizzes: Formulas will be provided with quiz papers if there is any need. For exams (MD1, MD2, and Final): You may bring one 8.5 by 11 sheet of
More informationVariance, Standard Deviation Counting Techniques
Variance, Standard Deviation Counting Techniques Section 1.3 & 2.1 Cathy Poliak, Ph.D. cathy@math.uh.edu Department of Mathematics University of Houston 1 / 52 Outline 1 Quartiles 2 The 1.5IQR Rule 3 Understanding
More information2017 Fall QMS102 Tip Sheet 2
Chapter 5: Basic Probability 2017 Fall QMS102 Tip Sheet 2 (Covering Chapters 5 to 8) EVENTS -- Each possible outcome of a variable is an event, including 3 types. 1. Simple event = Described by a single
More informationChapter 4 Probability Distributions
Slide 1 Chapter 4 Probability Distributions Slide 2 4-1 Overview 4-2 Random Variables 4-3 Binomial Probability Distributions 4-4 Mean, Variance, and Standard Deviation for the Binomial Distribution 4-5
More informationSTATS 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%
More informationPopulation Mean GOALS. Characteristics of the Mean. EXAMPLE Population Mean. Parameter Versus Statistics. Describing Data: Numerical Measures
GOALS Describing Data: Numerical Measures Chapter 3 McGraw-Hill/Irwin Copyright 010 by The McGraw-Hill Companies, Inc. All rights reserved. 3-1. Calculate the arithmetic mean, weighted mean, median, mode,
More informationChapter 7. Confidence Intervals and Sample Sizes. Definition. Definition. Definition. Definition. Confidence Interval : CI. Point Estimate.
Chapter 7 Confidence Intervals and Sample Sizes 7. Estimating a Proportion p 7.3 Estimating a Mean µ (σ known) 7.4 Estimating a Mean µ (σ unknown) 7.5 Estimating a Standard Deviation σ In a recent poll,
More informationChapter 5. Sampling Distributions
Lecture notes, Lang Wu, UBC 1 Chapter 5. Sampling Distributions 5.1. Introduction In statistical inference, we attempt to estimate an unknown population characteristic, such as the population mean, µ,
More informationMATH 446/546 Homework 1:
MATH 446/546 Homework 1: Due September 28th, 216 Please answer the following questions. Students should type there work. 1. At time t, a company has I units of inventory in stock. Customers demand the
More informationStatistics, Measures of Central Tendency I
Statistics, Measures of Central Tendency I We are considering a random variable X with a probability distribution which has some parameters. We want to get an idea what these parameters are. We perfom
More informationCSC 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.
More informationBasic Procedure for Histograms
Basic Procedure for Histograms 1. Compute the range of observations (min. & max. value) 2. Choose an initial # of classes (most likely based on the range of values, try and find a number of classes that
More information(a) salary of a bank executive (measured in dollars) quantitative. (c) SAT scores of students at Millersville University quantitative
Millersville University Name Answer Key Department of Mathematics MATH 130, Elements of Statistics I, Test 1 February 8, 2010, 10:00AM-10:50AM Please answer the following questions. Your answers will be
More informationTop Incorrect Problems
What is the z-score for scores in the bottom 5%? a) -1.645 b) 1.645 c).4801 d) The score is not listed in the table. A professor grades 120 research papers and reports that the average score was an 80%.
More informationMath146 - Chapter 3 Handouts. The Greek Alphabet. Source: Page 1 of 39
Source: www.mathwords.com The Greek Alphabet Page 1 of 39 Some Miscellaneous Tips on Calculations Examples: Round to the nearest thousandth 0.92431 0.75693 CAUTION! Do not truncate numbers! Example: 1
More informationMini-Lecture 3.1 Measures of Central Tendency
Mini-Lecture 3.1 Measures of Central Tendency Objectives 1. Determine the arithmetic mean of a variable from raw data 2. Determine the median of a variable from raw data 3. Explain what it means for a
More informationLecture Slides. Elementary Statistics Tenth Edition. by Mario F. Triola. and the Triola Statistics Series
Lecture Slides Elementary Statistics Tenth Edition and the Triola Statistics Series by Mario F. Triola Slide 1 Chapter 5 Probability Distributions 5-1 Overview 5-2 Random Variables 5-3 Binomial Probability
More informationA LEVEL MATHEMATICS ANSWERS AND MARKSCHEMES SUMMARY STATISTICS AND DIAGRAMS. 1. a) 45 B1 [1] b) 7 th value 37 M1 A1 [2]
1. a) 45 [1] b) 7 th value 37 [] n c) LQ : 4 = 3.5 4 th value so LQ = 5 3 n UQ : 4 = 9.75 10 th value so UQ = 45 IQR = 0 f.t. d) Median is closer to upper quartile Hence negative skew [] Page 1 . a) Orders
More informationChapter 6. y y. Standardizing with z-scores. Standardizing with z-scores (cont.)
Starter Ch. 6: A z-score Analysis Starter Ch. 6 Your Statistics teacher has announced that the lower of your two tests will be dropped. You got a 90 on test 1 and an 85 on test 2. You re all set to drop
More informationExample - Let X be the number of boys in a 4 child family. Find the probability distribution table:
Chapter8 Probability Distributions and Statistics Section 8.1 Distributions of Random Variables tthe value of the result of the probability experiment is a RANDOM VARIABLE. Example - Let X be the number
More informationLecture Slides. Elementary Statistics Tenth Edition. by Mario F. Triola. and the Triola Statistics Series. Slide 1
Lecture Slides Elementary Statistics Tenth Edition and the Triola Statistics Series by Mario F. Triola Slide 1 Chapter 6 Normal Probability Distributions 6-1 Overview 6-2 The Standard Normal Distribution
More informationMeasures of Dispersion (Range, standard deviation, standard error) Introduction
Measures of Dispersion (Range, standard deviation, standard error) Introduction We have already learnt that frequency distribution table gives a rough idea of the distribution of the variables in a sample
More informationChapter 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
More informationA probability distribution shows the possible outcomes of an experiment and the probability of each of these outcomes.
Introduction In the previous chapter we discussed the basic concepts of probability and described how the rules of addition and multiplication were used to compute probabilities. In this chapter we expand
More informationSTATISTICAL DISTRIBUTIONS AND THE CALCULATOR
STATISTICAL DISTRIBUTIONS AND THE CALCULATOR 1. Basic data sets a. Measures of Center - Mean ( ): average of all values. Characteristic: non-resistant is affected by skew and outliers. - Median: Either
More informationDetermining Sample Size. Slide 1 ˆ ˆ. p q n E = z α / 2. (solve for n by algebra) n = E 2
Determining Sample Size Slide 1 E = z α / 2 ˆ ˆ p q n (solve for n by algebra) n = ( zα α / 2) 2 p ˆ qˆ E 2 Sample Size for Estimating Proportion p When an estimate of ˆp is known: Slide 2 n = ˆ ˆ ( )
More information5.1 Mean, Median, & Mode
5.1 Mean, Median, & Mode definitions Mean: Median: Mode: Example 1 The Blue Jays score these amounts of runs in their last 9 games: 4, 7, 2, 4, 10, 5, 6, 7, 7 Find the mean, median, and mode: Example 2
More informationMidTerm 1) Find the following (round off to one decimal place):
MidTerm 1) 68 49 21 55 57 61 70 42 59 50 66 99 Find the following (round off to one decimal place): Mean = 58:083, round off to 58.1 Median = 58 Range = max min = 99 21 = 78 St. Deviation = s = 8:535,
More informationPROBABILITY AND STATISTICS CHAPTER 4 NOTES DISCRETE PROBABILITY DISTRIBUTIONS
PROBABILITY AND STATISTICS CHAPTER 4 NOTES DISCRETE PROBABILITY DISTRIBUTIONS I. INTRODUCTION TO RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS A. Random Variables 1. A random variable x represents a value
More informationHomework: Due Wed, Feb 20 th. Chapter 8, # 60a + 62a (count together as 1), 74, 82
Announcements: Week 5 quiz begins at 4pm today and ends at 3pm on Wed If you take more than 20 minutes to complete your quiz, you will only receive partial credit. (It doesn t cut you off.) Today: Sections
More informationMATH 118 Class Notes For Chapter 5 By: Maan Omran
MATH 118 Class Notes For Chapter 5 By: Maan Omran Section 5.1 Central Tendency Mode: the number or numbers that occur most often. Median: the number at the midpoint of a ranked data. Ex1: The test scores
More informationElementary Statistics Blue Book. The Normal Curve
Elementary Statistics Blue Book How to work smarter not harder The Normal Curve 68.2% 95.4% 99.7 % -4-3 -2-1 0 1 2 3 4 Z Scores John G. Blom May 2011 01 02 TI 30XA Key Strokes 03 07 TI 83/84 Key Strokes
More information1 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
More informationMgtOp 215 TEST 1 (Golden) Spring 2016 Dr. Ahn. Read the following instructions very carefully before you start the test.
MgtOp 15 TEST 1 (Golden) Spring 016 Dr. Ahn Name: ID: Section (Circle one): 4, 5, 6 Read the following instructions very carefully before you start the test. This test is closed book and notes; one summary
More informationPROBABILITY DISTRIBUTIONS
CHAPTER 3 PROBABILITY DISTRIBUTIONS Page Contents 3.1 Introduction to Probability Distributions 51 3.2 The Normal Distribution 56 3.3 The Binomial Distribution 60 3.4 The Poisson Distribution 64 Exercise
More information