Characteristics of Binomial Distributions

Size: px
Start display at page:

Download "Characteristics of Binomial Distributions"

Transcription

1 Lesson2 Characteristics of Binomial Distributions In the last lesson, you constructed several binomial distributions, observed their shapes, and estimated their means and standard deviations. In Investigation 1 of this lesson, you will learn how to visualize the shape of a binomial distribution if you know n and p. In Investigation 2, you will discover simple formulas for the mean and the standard deviation of a binomial distribution. But first, consider the following situation. According to an annual nationwide survey of college freshmen, two-thirds of both male and female freshmen planned to earn a graduate degree (master s or doctorate) or an advanced professional degree (such as law or medicine). (Source: Higher Education Research Institute, Annual Freshman Survey UCLA, 1997.) Suppose you have a random sample of 2 college freshmen and count the number who say they plan to get an advanced degree. The graph of the binomial distribution of the number of freshmen who plan to get an advanced degree is shown below Number of Freshmen 3 UNIT 5 BINOMIAL DISTRIBUTIONS AND STATISTICAL INFERENCE

2 Think About This Situation Examine the binomial distribution on the previous page. a b c d e What is the approximate shape of the graph? Explain what the height of the bar between 14 and 141 means. How many freshmen in a random sample of 2 would you expect to say that they plan to earn advanced degrees? How can you find the standard deviation of this probability distribution? What information does the standard deviation give you? How do you think the shape, center, and spread would change if the sample size was much larger? Much smaller? How do you think the shape, center, and spread would change if you graphed the proportion of successes rather than the number of successes? INVESTIGATION 1 The Shapes of Binomial Distributions The graph of the binomial distribution for the number of freshmen who plan to get advanced degrees looks approximately normal. In this investigation, you will see that not all binomial distributions are approximately normal in shape. However, you will be able to predict when they will be if you know the probability of a success p and the sample size n. 1. According to the 2 U.S. Census, about 2% of the population of the United States are children; that is, age 13 or younger. (Source: Age: 2, Census 2 Brief, October 21 at c2kbr1-12.pdf) Suppose you take a random sample of people from the United States. The following graphs show the binomial distributions for the number of children in random samples varying in size from 5 to Sample Size n = Number of Children.4.3 Sample Size n = Number of Children 5 5 Sample Size n = Number of Children LESSON 2 CHARACTERISTICS OF BINOMIAL DISTRIBUTIONS 31

3 5 Sample Size n = 5 Sample Size n = Number of Children Number of Children a. Determine the exact height of the tallest bar on the graph for a sample size of 1. b. Why are there more bars as the sample size increases? How many bars should there be for a sample size of n? Why are there only 7 bars for a sample size of 1? c. What happens to the shape of the distribution as the sample size increases? d. What happens to the mean of the number of successes as the sample size increases? e. What happens to the standard deviation of the number of successes as the sample size increases? 2. Examine the following graphs. They show binomial distributions for the number of heads when a fair coin is tossed various numbers of times. n = 1 n = 2 n = 3 n = 4 n = Number of Heads x n = 6 32 UNIT 5 BINOMIAL DISTRIBUTIONS AND STATISTICAL INFERENCE

4 n = 7 n = 8 n = 9 n = Number of Heads x a. What happens to the shape of these distributions as the sample size n increases? b. What happens to the mean of the probability distribution as n increases? c. What happens to the standard deviation as n increases? 3. Now consider what happens when the sample size is fixed and the probability of success varies. The set of graphs below show the binomial distributions for a sample size of 4 and probabilities of success varying from to.9. p = 1% p = 2% p = 3% p = 4% p = 5% Number of Successes LESSON 2 CHARACTERISTICS OF BINOMIAL DISTRIBUTIONS 33

5 p = 6% p = 7% p = 8% p = 9% Number of Successes a. Which of these distributions are a bit skewed? What happens to the shape of the distributions as the probability of success p increases? Which of these distributions has a shape that is closest to normal? b. What happens to the mean of the number of successes as the probability of success p increases? c. What happens to the standard deviation as p increases? d. What symmetries do you see in this set of graphs? e. How is this set of graphs similar to the box plot charts from fixed sample sizes you made in Course 3, Unit 2, Modeling Public Opinion? The box plot chart for samples of size 4 is shown below. Population Percent 9% Box Plots from Samples of Size 4 Sample Outcome as a Proportion Sample Outcome as a Total 34 UNIT 5 BINOMIAL DISTRIBUTIONS AND STATISTICAL INFERENCE

6 4. Based on your work in Activities 1 through 3, draw conclusions about the effect of sample size or value of p on the shape of the graph of a binomial distribution. a. Complete each sentence using the word more or less. With a fixed sample size, the farther p is away from.5, the skewed the binomial distribution. Usually, with a fixed value of p, the larger the sample size, the skewed the binomial distribution. b. What are the exceptions to the statement in the second item of Part a? 5. To predict whether the graph of a binomial distribution will look approximately normal, compute both np and n(1 p) and check to see if both are at least 1. a. The value np can be interpreted as the expected number of successes. How can you interpret n(1 p)? b. Which of the distributions in Activity 1 can be considered approximately normal using the above guideline? Does this agree with your visual impression? c. Which of the distributions in Activity 2 can be considered approximately normal using this guideline? Does this agree with your visual impression? d. Which of the distributions in Activity 3 can be considered approximately normal using this guideline? Does this agree with your visual impression? 6. Imagine the binomial distribution with n = 35 and p =. a. Is this distribution skewed left, skewed right, or symmetric? b. Where is it centered? Estimate its standard deviation. c. Check your answers to Parts a and b using the binomial function capabilities of your calculator to make a graph of this distribution. Checkpoint Think about a binomial distribution with probability of a success p and sample size n. a b As n increases, but the probability of a success p remains the same, what happens to the shape, center, and spread of the binomial distribution for the number of successes? As p increases from 1 to.99, but n remains the same (for example, n = 5) what happens to the shape, center, and spread of the binomial distribution for the number of successes? Be prepared to discuss your descriptions of changes in the binomial distributions. LESSON 2 CHARACTERISTICS OF BINOMIAL DISTRIBUTIONS 35

7 On Your Own Think about patterns of change in binomial distributions as the sample size or probability of success varies. a. How does the binomial distribution for the number of successes in a sample of size 25 and probability of success.3 differ from a binomial distribution with sample size 5 and probability of success.3? b. How does the binomial distribution for the number of successes in a sample of size 25 and probability of success.3 compare to the binomial distribution with sample size 25 and probability of success.7? In the first part of this investigation, you examined the shape, center, and spread of binomial distributions of the number of successes in a random sample. Now examine these same characteristics of distributions of the proportion ˆp of successes. 7. How do you convert the number of successes in a binomial situation to the proportion of successes? 8. Recall from Activity 1 (page 31) that about 2% of the population of the United States are children age 13 or younger. Shown below are the graphs from Activity 1 with the scale on the x-axes changed to one that gives the proportion ˆp of children in random samples, for sample sizes varying from 5 to Sample Size n = 5 1. Proportion of Children.4.3 Sample Size n = Proportion of Children 5 5 Sample Size n = Proportion of Children 5 5 Sample Size n = Sample Size n = Proportion of Children Proportion of Children a. What happens to the mean of the sample proportions as the sample size increases? b. What happens to the standard deviation of the sample proportions as the sample size increases? Why does this make sense? 36 UNIT 5 BINOMIAL DISTRIBUTIONS AND STATISTICAL INFERENCE

8 9. On a copy of each of the graphs from Activity 2, change the scale on the x- axis to one that gives the sample proportion of heads, ˆp. a. What happens to the mean of the proportion of heads as the sample size increases? b. What happens to the standard deviation of the proportion of heads as the sample size increases? Checkpoint Consider a binomial situation with probability of success p and sample size n. a b c If n increases but p remains the same, what happens to the shape, center, and spread of the distribution of the sample proportions? Compare the graphs of the distributions of ˆp and of 1 ˆp for a fixed sample size n. Summarize the differences between the binomial distribution for the number of successes and the distribution for the corresponding sample proportions. Be prepared to discuss the variations in the distributions. On Your Own Think about patterns of change in distributions for the sample proportion ˆp as the sample size, or probability of success varies. a. How does the distribution of the sample proportion ˆp from a sample of size 25 and probability of success.3 differ from the distribution with sample size 5 and probability of success.3? b. How does the distribution of the sample proportion ˆp from a sample size of 25 and probability of success.3 compare to the distribution with sample size 25 and probability of success.7? c. Compare your answers for Parts a and b with your corresponding answers to Parts a and b of the On Your Own on page 36. INVESTIGATION 2 Simple Formulas for the Mean and the Standard Deviation Recall that the formula for the mean of a probability distribution is = x p(x) Similarly, the formula for the standard deviation of a probability distribution is = ( x ) 2 p (x ) LESSON 2 CHARACTERISTICS OF BINOMIAL DISTRIBUTIONS 37

9 In this investigation, you will see that these formulas can be simplified in the case of binomial distributions. 1. According to the 2 U.S. Census, about 2% of the population of the United States are children (aged 13 or younger). Suppose you take a random sample of four people living in the United States and count the number of children. a. Complete this probability distribution table for the number of children in your sample of size 4. Number of Children x p(x) b. Use the formulas at the bottom of page 37 to compute the mean and standard deviation of the probability distribution in Part a. c. In computing the mean of the above distribution, you could also simply reason that if 2% of the population are children, the mean number of children in a random sample of four people should be 2% of 4 or.8. Compare this value with the mean value you computed in Part b. 2. Part c of Activity 1 illustrates a general formula for computing the mean of a binomial distribution. The mean number of successes in n trials with probability of success p is = np There is also a much simpler formula for the standard deviation of a binomial distribution: = n p (1 p ) a. Verify that this formula for the standard deviation gives the same result as that in Activity 1 Part b. b. Now refer back to the graphs in Activity 1 of Investigation 1 of this lesson. Complete the table at the top of page 39 for those graphs. In each case, p =. 38 UNIT 5 BINOMIAL DISTRIBUTIONS AND STATISTICAL INFERENCE

10 Sample Size n Mean Standard Deviation c. If you double the sample size, what happens to the mean? To the standard deviation? d. How does the mean vary with the sample size? How does the standard deviation vary with the sample size? 3. Consider this table for binomial distributions with n = 9 and various values of p. The variance 2 is the square of the standard deviation. of a Success p Mean = np Variance a. Complete the table. b. What patterns do you see in the table? c. Plot the points (p, 2 ). What type of function would model the pattern of change shown in the graph? d. Describe at least three possible methods for finding an equation showing the relationship between the probability of success and the variance. e. Use two of these methods to find an equation. 4. About 26% of the U.S. population 25 and over have completed a bachelor s degree. (Source: Educational Attainment in the United States (Update). Current Population Reports, March 2, U.S. Census Bureau.) a. Describe the shape, mean, and standard deviation of the binomial distribution for samples of size 1, for this situation. b. What numbers of people with bachelor s degrees would be rare events? LESSON 2 CHARACTERISTICS OF BINOMIAL DISTRIBUTIONS 39

11 5. You can use the formulas for the mean and standard deviation of the number of successes to derive corresponding formulas for the proportion of successes. a. If the mean number of successes is = np, what computation would you do to get the mean proportion of successes? Write the formula for the mean proportion of successes. b. Why does the result in Part a make sense intuitively? c. If the standard deviation of the number of successes is = n p (1 p ),what computation would you do to get the standard deviation of the proportion of successes? Write the formula in the simplest form possible. d. By examining your formula from Part c, what can you determine about the standard deviation for the proportion of successes as the sample size increases? e. Why does the result in Part d make sense intuitively? Checkpoint When studying a binomial situation, it is often helpful to know the mean and standard deviation of the number of successes or the proportion of successes. a b Compare the formulas for the mean and standard deviation of the number of successes in a binomial distribution to the corresponding formulas for the proportion of successes. Suppose you fix a value of p and n and construct the probability distributions for the number of successes and the proportion of successes. Describe how the shape, mean, and standard deviation of each distribution changes if you increase n but keep p fixed. Be prepared to share your comparison and explanation with the class. On Your Own Consider a binomial distribution with n = 3 and p =.45. a. Make a probability distribution table giving the number of successes. b. Find the mean of the number of successes using the formula = np and then using the formula = x p(x). c. Find the standard deviation of the number of successes using the formula = n p (1 p ) and then by using the formula = ( x ) 2 p (x ). d. Find the mean and standard deviation of the distribution of the sample proportion ˆp. 31 UNIT 5 BINOMIAL DISTRIBUTIONS AND STATISTICAL INFERENCE

Week 3&4: Z tables and the Sampling Distribution of X

Week 3&4: Z tables and the Sampling Distribution of X Week 3&4: Z tables and the Sampling Distribution of X 2 / 36 The Standard Normal Distribution, or Z Distribution, is the distribution of a random variable, Z N(0, 1 2 ). The distribution of any other normal

More information

TEACHER NOTES MATH NSPIRED

TEACHER NOTES MATH NSPIRED Math Objectives Students will understand that normal distributions can be used to approximate binomial distributions whenever both np and n(1 p) are sufficiently large. Students will understand that when

More information

4. Continuous Random Variables, the Pareto and Normal Distributions

4. Continuous Random Variables, the Pareto and Normal Distributions 4. Continuous Random Variables, the Pareto and Normal Distributions A continuous random variable X can take any value in a given range (e.g. height, weight, age). The distribution of a continuous random

More information

The right edge of the box is the third quartile, Q 3, which is the median of the data values above the median. Maximum Median

The right edge of the box is the third quartile, Q 3, which is the median of the data values above the median. Maximum Median CONDENSED LESSON 2.1 Box Plots In this lesson you will create and interpret box plots for sets of data use the interquartile range (IQR) to identify potential outliers and graph them on a modified box

More information

CA200 Quantitative Analysis for Business Decisions. File name: CA200_Section_04A_StatisticsIntroduction

CA200 Quantitative Analysis for Business Decisions. File name: CA200_Section_04A_StatisticsIntroduction CA200 Quantitative Analysis for Business Decisions File name: CA200_Section_04A_StatisticsIntroduction Table of Contents 4. Introduction to Statistics... 1 4.1 Overview... 3 4.2 Discrete or continuous

More information

Chapter 5: Normal Probability Distributions - Solutions

Chapter 5: Normal Probability Distributions - Solutions Chapter 5: Normal Probability Distributions - Solutions Note: All areas and z-scores are approximate. Your answers may vary slightly. 5.2 Normal Distributions: Finding Probabilities If you are given that

More information

DETERMINE whether the conditions for a binomial setting are met. COMPUTE and INTERPRET probabilities involving binomial random variables

DETERMINE whether the conditions for a binomial setting are met. COMPUTE and INTERPRET probabilities involving binomial random variables 1 Section 7.B Learning Objectives After this section, you should be able to DETERMINE whether the conditions for a binomial setting are met COMPUTE and INTERPRET probabilities involving binomial random

More information

MEASURES OF VARIATION

MEASURES OF VARIATION NORMAL DISTRIBTIONS MEASURES OF VARIATION In statistics, it is important to measure the spread of data. A simple way to measure spread is to find the range. But statisticians want to know if the data are

More information

You flip a fair coin four times, what is the probability that you obtain three heads.

You flip a fair coin four times, what is the probability that you obtain three heads. Handout 4: Binomial Distribution Reading Assignment: Chapter 5 In the previous handout, we looked at continuous random variables and calculating probabilities and percentiles for those type of variables.

More information

Def: The standard normal distribution is a normal probability distribution that has a mean of 0 and a standard deviation of 1.

Def: The standard normal distribution is a normal probability distribution that has a mean of 0 and a standard deviation of 1. Lecture 6: Chapter 6: Normal Probability Distributions A normal distribution is a continuous probability distribution for a random variable x. The graph of a normal distribution is called the normal curve.

More information

Probability Distributions

Probability Distributions CHAPTER 5 Probability Distributions CHAPTER OUTLINE 5.1 Probability Distribution of a Discrete Random Variable 5.2 Mean and Standard Deviation of a Probability Distribution 5.3 The Binomial Distribution

More information

Lesson 17: Margin of Error When Estimating a Population Proportion

Lesson 17: Margin of Error When Estimating a Population Proportion Margin of Error When Estimating a Population Proportion Classwork In this lesson, you will find and interpret the standard deviation of a simulated distribution for a sample proportion and use this information

More information

6.4 Normal Distribution

6.4 Normal Distribution Contents 6.4 Normal Distribution....................... 381 6.4.1 Characteristics of the Normal Distribution....... 381 6.4.2 The Standardized Normal Distribution......... 385 6.4.3 Meaning of Areas under

More information

Session 7 Bivariate Data and Analysis

Session 7 Bivariate Data and Analysis Session 7 Bivariate Data and Analysis Key Terms for This Session Previously Introduced mean standard deviation New in This Session association bivariate analysis contingency table co-variation least squares

More information

Introduction to the Practice of Statistics Fifth Edition Moore, McCabe

Introduction to the Practice of Statistics Fifth Edition Moore, McCabe Introduction to the Practice of Statistics Fifth Edition Moore, McCabe Section 5.1 Homework Answers 5.7 In the proofreading setting if Exercise 5.3, what is the smallest number of misses m with P(X m)

More information

Important Probability Distributions OPRE 6301

Important Probability Distributions OPRE 6301 Important Probability Distributions OPRE 6301 Important Distributions... Certain probability distributions occur with such regularity in real-life applications that they have been given their own names.

More information

6 3 The Standard Normal Distribution

6 3 The Standard Normal Distribution 290 Chapter 6 The Normal Distribution Figure 6 5 Areas Under a Normal Distribution Curve 34.13% 34.13% 2.28% 13.59% 13.59% 2.28% 3 2 1 + 1 + 2 + 3 About 68% About 95% About 99.7% 6 3 The Distribution Since

More information

Section 5 Part 2. Probability Distributions for Discrete Random Variables

Section 5 Part 2. Probability Distributions for Discrete Random Variables Section 5 Part 2 Probability Distributions for Discrete Random Variables Review and Overview So far we ve covered the following probability and probability distribution topics Probability rules Probability

More information

MATH THAT MAKES ENTS

MATH THAT MAKES ENTS The Bureau of Labor statistics share this data to describe the difference in earnings and unemployment rates by the amount of education attained. (1) Take a look at this table, describe what you notice

More information

WHERE DOES THE 10% CONDITION COME FROM?

WHERE DOES THE 10% CONDITION COME FROM? 1 WHERE DOES THE 10% CONDITION COME FROM? The text has mentioned The 10% Condition (at least) twice so far: p. 407 Bernoulli trials must be independent. If that assumption is violated, it is still okay

More information

Normal distribution. ) 2 /2σ. 2π σ

Normal distribution. ) 2 /2σ. 2π σ Normal distribution The normal distribution is the most widely known and used of all distributions. Because the normal distribution approximates many natural phenomena so well, it has developed into a

More information

Fairfield Public Schools

Fairfield Public Schools Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity

More information

c. Construct a boxplot for the data. Write a one sentence interpretation of your graph.

c. Construct a boxplot for the data. Write a one sentence interpretation of your graph. MBA/MIB 5315 Sample Test Problems Page 1 of 1 1. An English survey of 3000 medical records showed that smokers are more inclined to get depressed than non-smokers. Does this imply that smoking causes depression?

More information

Irrational Numbers. A. Rational Numbers 1. Before we discuss irrational numbers, it would probably be a good idea to define rational numbers.

Irrational Numbers. A. Rational Numbers 1. Before we discuss irrational numbers, it would probably be a good idea to define rational numbers. Irrational Numbers A. Rational Numbers 1. Before we discuss irrational numbers, it would probably be a good idea to define rational numbers. Definition: Rational Number A rational number is a number that

More information

Review for Test 2. Chapters 4, 5 and 6

Review for Test 2. Chapters 4, 5 and 6 Review for Test 2 Chapters 4, 5 and 6 1. You roll a fair six-sided die. Find the probability of each event: a. Event A: rolling a 3 1/6 b. Event B: rolling a 7 0 c. Event C: rolling a number less than

More information

Exploratory data analysis (Chapter 2) Fall 2011

Exploratory data analysis (Chapter 2) Fall 2011 Exploratory data analysis (Chapter 2) Fall 2011 Data Examples Example 1: Survey Data 1 Data collected from a Stat 371 class in Fall 2005 2 They answered questions about their: gender, major, year in school,

More information

SOLUTIONS: 4.1 Probability Distributions and 4.2 Binomial Distributions

SOLUTIONS: 4.1 Probability Distributions and 4.2 Binomial Distributions SOLUTIONS: 4.1 Probability Distributions and 4.2 Binomial Distributions 1. The following table contains a probability distribution for a random variable X. a. Find the expected value (mean) of X. x 1 2

More information

MBA 611 STATISTICS AND QUANTITATIVE METHODS

MBA 611 STATISTICS AND QUANTITATIVE METHODS MBA 611 STATISTICS AND QUANTITATIVE METHODS Part I. Review of Basic Statistics (Chapters 1-11) A. Introduction (Chapter 1) Uncertainty: Decisions are often based on incomplete information from uncertain

More information

Binomial Probability Distribution

Binomial Probability Distribution Binomial Probability Distribution In a binomial setting, we can compute probabilities of certain outcomes. This used to be done with tables, but with graphing calculator technology, these problems are

More information

Point and Interval Estimates

Point and Interval Estimates Point and Interval Estimates Suppose we want to estimate a parameter, such as p or µ, based on a finite sample of data. There are two main methods: 1. Point estimate: Summarize the sample by a single number

More information

5/31/2013. 6.1 Normal Distributions. Normal Distributions. Chapter 6. Distribution. The Normal Distribution. Outline. Objectives.

5/31/2013. 6.1 Normal Distributions. Normal Distributions. Chapter 6. Distribution. The Normal Distribution. Outline. Objectives. The Normal Distribution C H 6A P T E R The Normal Distribution Outline 6 1 6 2 Applications of the Normal Distribution 6 3 The Central Limit Theorem 6 4 The Normal Approximation to the Binomial Distribution

More information

Probability Distributions

Probability Distributions Learning Objectives Probability Distributions Section 1: How Can We Summarize Possible Outcomes and Their Probabilities? 1. Random variable 2. Probability distributions for discrete random variables 3.

More information

The Math. P (x) = 5! = 1 2 3 4 5 = 120.

The Math. P (x) = 5! = 1 2 3 4 5 = 120. The Math Suppose there are n experiments, and the probability that someone gets the right answer on any given experiment is p. So in the first example above, n = 5 and p = 0.2. Let X be the number of correct

More information

Summary of Formulas and Concepts. Descriptive Statistics (Ch. 1-4)

Summary of Formulas and Concepts. Descriptive Statistics (Ch. 1-4) Summary of Formulas and Concepts Descriptive Statistics (Ch. 1-4) Definitions Population: The complete set of numerical information on a particular quantity in which an investigator is interested. We assume

More information

Chapter 4. iclicker Question 4.4 Pre-lecture. Part 2. Binomial Distribution. J.C. Wang. iclicker Question 4.4 Pre-lecture

Chapter 4. iclicker Question 4.4 Pre-lecture. Part 2. Binomial Distribution. J.C. Wang. iclicker Question 4.4 Pre-lecture Chapter 4 Part 2. Binomial Distribution J.C. Wang iclicker Question 4.4 Pre-lecture iclicker Question 4.4 Pre-lecture Outline Computing Binomial Probabilities Properties of a Binomial Distribution Computing

More information

Experimental Design. Power and Sample Size Determination. Proportions. Proportions. Confidence Interval for p. The Binomial Test

Experimental Design. Power and Sample Size Determination. Proportions. Proportions. Confidence Interval for p. The Binomial Test Experimental Design Power and Sample Size Determination Bret Hanlon and Bret Larget Department of Statistics University of Wisconsin Madison November 3 8, 2011 To this point in the semester, we have largely

More information

Random variables, probability distributions, binomial random variable

Random variables, probability distributions, binomial random variable Week 4 lecture notes. WEEK 4 page 1 Random variables, probability distributions, binomial random variable Eample 1 : Consider the eperiment of flipping a fair coin three times. The number of tails that

More information

ST 371 (IV): Discrete Random Variables

ST 371 (IV): Discrete Random Variables ST 371 (IV): Discrete Random Variables 1 Random Variables A random variable (rv) is a function that is defined on the sample space of the experiment and that assigns a numerical variable to each possible

More information

REPEATED TRIALS. The probability of winning those k chosen times and losing the other times is then p k q n k.

REPEATED TRIALS. The probability of winning those k chosen times and losing the other times is then p k q n k. REPEATED TRIALS Suppose you toss a fair coin one time. Let E be the event that the coin lands heads. We know from basic counting that p(e) = 1 since n(e) = 1 and 2 n(s) = 2. Now suppose we play a game

More information

Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur

Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur Module No. #01 Lecture No. #15 Special Distributions-VI Today, I am going to introduce

More information

Mathematics (Project Maths Phase 1)

Mathematics (Project Maths Phase 1) 2012. M128 S Coimisiún na Scrúduithe Stáit State Examinations Commission Leaving Certificate Examination, 2012 Sample Paper Mathematics (Project Maths Phase 1) Paper 2 Ordinary Level Time: 2 hours, 30

More information

99.37, 99.38, 99.38, 99.39, 99.39, 99.39, 99.39, 99.40, 99.41, 99.42 cm

99.37, 99.38, 99.38, 99.39, 99.39, 99.39, 99.39, 99.40, 99.41, 99.42 cm Error Analysis and the Gaussian Distribution In experimental science theory lives or dies based on the results of experimental evidence and thus the analysis of this evidence is a critical part of the

More information

Pr(X = x) = f(x) = λe λx

Pr(X = x) = f(x) = λe λx Old Business - variance/std. dev. of binomial distribution - mid-term (day, policies) - class strategies (problems, etc.) - exponential distributions New Business - Central Limit Theorem, standard error

More information

How To Write A Data Analysis

How To Write A Data Analysis Mathematics Probability and Statistics Curriculum Guide Revised 2010 This page is intentionally left blank. Introduction The Mathematics Curriculum Guide serves as a guide for teachers when planning instruction

More information

The normal approximation to the binomial

The normal approximation to the binomial The normal approximation to the binomial The binomial probability function is not useful for calculating probabilities when the number of trials n is large, as it involves multiplying a potentially very

More information

Descriptive Statistics. Purpose of descriptive statistics Frequency distributions Measures of central tendency Measures of dispersion

Descriptive Statistics. Purpose of descriptive statistics Frequency distributions Measures of central tendency Measures of dispersion Descriptive Statistics Purpose of descriptive statistics Frequency distributions Measures of central tendency Measures of dispersion Statistics as a Tool for LIS Research Importance of statistics in research

More information

Statistics 2014 Scoring Guidelines

Statistics 2014 Scoring Guidelines AP Statistics 2014 Scoring Guidelines College Board, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks of the College Board. AP Central is the official online home

More information

BNG 202 Biomechanics Lab. Descriptive statistics and probability distributions I

BNG 202 Biomechanics Lab. Descriptive statistics and probability distributions I BNG 202 Biomechanics Lab Descriptive statistics and probability distributions I Overview The overall goal of this short course in statistics is to provide an introduction to descriptive and inferential

More information

Section 14 Simple Linear Regression: Introduction to Least Squares Regression

Section 14 Simple Linear Regression: Introduction to Least Squares Regression Slide 1 Section 14 Simple Linear Regression: Introduction to Least Squares Regression There are several different measures of statistical association used for understanding the quantitative relationship

More information

STAT 35A HW2 Solutions

STAT 35A HW2 Solutions STAT 35A HW2 Solutions http://www.stat.ucla.edu/~dinov/courses_students.dir/09/spring/stat35.dir 1. A computer consulting firm presently has bids out on three projects. Let A i = { awarded project i },

More information

Lecture 10: Depicting Sampling Distributions of a Sample Proportion

Lecture 10: Depicting Sampling Distributions of a Sample Proportion Lecture 10: Depicting Sampling Distributions of a Sample Proportion Chapter 5: Probability and Sampling Distributions 2/10/12 Lecture 10 1 Sample Proportion 1 is assigned to population members having a

More information

The Point-Slope Form

The Point-Slope Form 7. The Point-Slope Form 7. OBJECTIVES 1. Given a point and a slope, find the graph of a line. Given a point and the slope, find the equation of a line. Given two points, find the equation of a line y Slope

More information

10.1. Solving Quadratic Equations. Investigation: Rocket Science CONDENSED

10.1. Solving Quadratic Equations. Investigation: Rocket Science CONDENSED CONDENSED L E S S O N 10.1 Solving Quadratic Equations In this lesson you will look at quadratic functions that model projectile motion use tables and graphs to approimate solutions to quadratic equations

More information

Lesson 20. Probability and Cumulative Distribution Functions

Lesson 20. Probability and Cumulative Distribution Functions Lesson 20 Probability and Cumulative Distribution Functions Recall If p(x) is a density function for some characteristic of a population, then Recall If p(x) is a density function for some characteristic

More information

Lecture 19: Chapter 8, Section 1 Sampling Distributions: Proportions

Lecture 19: Chapter 8, Section 1 Sampling Distributions: Proportions Lecture 19: Chapter 8, Section 1 Sampling Distributions: Proportions Typical Inference Problem Definition of Sampling Distribution 3 Approaches to Understanding Sampling Dist. Applying 68-95-99.7 Rule

More information

AP Statistics Solutions to Packet 2

AP Statistics Solutions to Packet 2 AP Statistics Solutions to Packet 2 The Normal Distributions Density Curves and the Normal Distribution Standard Normal Calculations HW #9 1, 2, 4, 6-8 2.1 DENSITY CURVES (a) Sketch a density curve that

More information

1.7 Graphs of Functions

1.7 Graphs of Functions 64 Relations and Functions 1.7 Graphs of Functions In Section 1.4 we defined a function as a special type of relation; one in which each x-coordinate was matched with only one y-coordinate. We spent most

More information

The Big Picture. Describing Data: Categorical and Quantitative Variables Population. Descriptive Statistics. Community Coalitions (n = 175)

The Big Picture. Describing Data: Categorical and Quantitative Variables Population. Descriptive Statistics. Community Coalitions (n = 175) Describing Data: Categorical and Quantitative Variables Population The Big Picture Sampling Statistical Inference Sample Exploratory Data Analysis Descriptive Statistics In order to make sense of data,

More information

The Binomial Probability Distribution

The Binomial Probability Distribution The Binomial Probability Distribution MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2015 Objectives After this lesson we will be able to: determine whether a probability

More information

Measurement with Ratios

Measurement with Ratios Grade 6 Mathematics, Quarter 2, Unit 2.1 Measurement with Ratios Overview Number of instructional days: 15 (1 day = 45 minutes) Content to be learned Use ratio reasoning to solve real-world and mathematical

More information

CURVE FITTING LEAST SQUARES APPROXIMATION

CURVE FITTING LEAST SQUARES APPROXIMATION CURVE FITTING LEAST SQUARES APPROXIMATION Data analysis and curve fitting: Imagine that we are studying a physical system involving two quantities: x and y Also suppose that we expect a linear relationship

More information

3: Summary Statistics

3: Summary Statistics 3: Summary Statistics Notation Let s start by introducing some notation. Consider the following small data set: 4 5 30 50 8 7 4 5 The symbol n represents the sample size (n = 0). The capital letter X denotes

More information

9. Sampling Distributions

9. Sampling Distributions 9. Sampling Distributions Prerequisites none A. Introduction B. Sampling Distribution of the Mean C. Sampling Distribution of Difference Between Means D. Sampling Distribution of Pearson's r E. Sampling

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics Suppose following data have been collected (heights of 99 five-year-old boys) 117.9 11.2 112.9 115.9 18. 14.6 17.1 117.9 111.8 16.3 111. 1.4 112.1 19.2 11. 15.4 99.4 11.1 13.3 16.9

More information

3.4. The Binomial Probability Distribution. Copyright Cengage Learning. All rights reserved.

3.4. The Binomial Probability Distribution. Copyright Cengage Learning. All rights reserved. 3.4 The Binomial Probability Distribution Copyright Cengage Learning. All rights reserved. The Binomial Probability Distribution There are many experiments that conform either exactly or approximately

More information

Sampling Distributions

Sampling Distributions Sampling Distributions You have seen probability distributions of various types. The normal distribution is an example of a continuous distribution that is often used for quantitative measures such as

More information

16. THE NORMAL APPROXIMATION TO THE BINOMIAL DISTRIBUTION

16. THE NORMAL APPROXIMATION TO THE BINOMIAL DISTRIBUTION 6. THE NORMAL APPROXIMATION TO THE BINOMIAL DISTRIBUTION It is sometimes difficult to directly compute probabilities for a binomial (n, p) random variable, X. We need a different table for each value of

More information

Solutions of Equations in Two Variables

Solutions of Equations in Two Variables 6.1 Solutions of Equations in Two Variables 6.1 OBJECTIVES 1. Find solutions for an equation in two variables 2. Use ordered pair notation to write solutions for equations in two variables We discussed

More information

Chapter 4. Probability and Probability Distributions

Chapter 4. Probability and Probability Distributions Chapter 4. robability and robability Distributions Importance of Knowing robability To know whether a sample is not identical to the population from which it was selected, it is necessary to assess the

More information

The normal approximation to the binomial

The normal approximation to the binomial The normal approximation to the binomial In order for a continuous distribution (like the normal) to be used to approximate a discrete one (like the binomial), a continuity correction should be used. There

More information

Chapter 4 Lecture Notes

Chapter 4 Lecture Notes Chapter 4 Lecture Notes Random Variables October 27, 2015 1 Section 4.1 Random Variables A random variable is typically a real-valued function defined on the sample space of some experiment. For instance,

More information

The Normal Distribution

The Normal Distribution The Normal Distribution Continuous Distributions A continuous random variable is a variable whose possible values form some interval of numbers. Typically, a continuous variable involves a measurement

More information

Means, standard deviations and. and standard errors

Means, standard deviations and. and standard errors CHAPTER 4 Means, standard deviations and standard errors 4.1 Introduction Change of units 4.2 Mean, median and mode Coefficient of variation 4.3 Measures of variation 4.4 Calculating the mean and standard

More information

Continuing, we get (note that unlike the text suggestion, I end the final interval with 95, not 85.

Continuing, we get (note that unlike the text suggestion, I end the final interval with 95, not 85. Chapter 3 -- Review Exercises Statistics 1040 -- Dr. McGahagan Problem 1. Histogram of male heights. Shaded area shows percentage of men between 66 and 72 inches in height; this translates as "66 inches

More information

1.3 Measuring Center & Spread, The Five Number Summary & Boxplots. Describing Quantitative Data with Numbers

1.3 Measuring Center & Spread, The Five Number Summary & Boxplots. Describing Quantitative Data with Numbers 1.3 Measuring Center & Spread, The Five Number Summary & Boxplots Describing Quantitative Data with Numbers 1.3 I can n Calculate and interpret measures of center (mean, median) in context. n Calculate

More information

Density Curve. A density curve is the graph of a continuous probability distribution. It must satisfy the following properties:

Density Curve. A density curve is the graph of a continuous probability distribution. It must satisfy the following properties: Density Curve A density curve is the graph of a continuous probability distribution. It must satisfy the following properties: 1. The total area under the curve must equal 1. 2. Every point on the curve

More information

2. Discrete random variables

2. Discrete random variables 2. Discrete random variables Statistics and probability: 2-1 If the chance outcome of the experiment is a number, it is called a random variable. Discrete random variable: the possible outcomes can be

More information

Thursday, November 13: 6.1 Discrete Random Variables

Thursday, November 13: 6.1 Discrete Random Variables Thursday, November 13: 6.1 Discrete Random Variables Read 347 350 What is a random variable? Give some examples. What is a probability distribution? What is a discrete random variable? Give some examples.

More information

Algebra 1 Course Title

Algebra 1 Course Title Algebra 1 Course Title Course- wide 1. What patterns and methods are being used? Course- wide 1. Students will be adept at solving and graphing linear and quadratic equations 2. Students will be adept

More information

Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools

Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools Occam s razor.......................................................... 2 A look at data I.........................................................

More information

Descriptive Statistics and Measurement Scales

Descriptive Statistics and Measurement Scales Descriptive Statistics 1 Descriptive Statistics and Measurement Scales Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample

More information

Unit 7 Quadratic Relations of the Form y = ax 2 + bx + c

Unit 7 Quadratic Relations of the Form y = ax 2 + bx + c Unit 7 Quadratic Relations of the Form y = ax 2 + bx + c Lesson Outline BIG PICTURE Students will: manipulate algebraic expressions, as needed to understand quadratic relations; identify characteristics

More information

Lecture 2: Descriptive Statistics and Exploratory Data Analysis

Lecture 2: Descriptive Statistics and Exploratory Data Analysis Lecture 2: Descriptive Statistics and Exploratory Data Analysis Further Thoughts on Experimental Design 16 Individuals (8 each from two populations) with replicates Pop 1 Pop 2 Randomly sample 4 individuals

More information

The Binomial Distribution

The Binomial Distribution The Binomial Distribution James H. Steiger November 10, 00 1 Topics for this Module 1. The Binomial Process. The Binomial Random Variable. The Binomial Distribution (a) Computing the Binomial pdf (b) Computing

More information

STT315 Chapter 4 Random Variables & Probability Distributions KM. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables

STT315 Chapter 4 Random Variables & Probability Distributions KM. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables Discrete vs. continuous random variables Examples of continuous distributions o Uniform o Exponential o Normal Recall: A random

More information

Exponents and Radicals

Exponents and Radicals Exponents and Radicals (a + b) 10 Exponents are a very important part of algebra. An exponent is just a convenient way of writing repeated multiplications of the same number. Radicals involve the use of

More information

Lecture 14. Chapter 7: Probability. Rule 1: Rule 2: Rule 3: Nancy Pfenning Stats 1000

Lecture 14. Chapter 7: Probability. Rule 1: Rule 2: Rule 3: Nancy Pfenning Stats 1000 Lecture 4 Nancy Pfenning Stats 000 Chapter 7: Probability Last time we established some basic definitions and rules of probability: Rule : P (A C ) = P (A). Rule 2: In general, the probability of one event

More information

Summarizing and Displaying Categorical Data

Summarizing and Displaying Categorical Data Summarizing and Displaying Categorical Data Categorical data can be summarized in a frequency distribution which counts the number of cases, or frequency, that fall into each category, or a relative frequency

More information

Statistics 104: Section 6!

Statistics 104: Section 6! Page 1 Statistics 104: Section 6! TF: Deirdre (say: Dear-dra) Bloome Email: dbloome@fas.harvard.edu Section Times Thursday 2pm-3pm in SC 109, Thursday 5pm-6pm in SC 705 Office Hours: Thursday 6pm-7pm SC

More information

STATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI

STATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI STATS8: Introduction to Biostatistics Data Exploration Babak Shahbaba Department of Statistics, UCI Introduction After clearly defining the scientific problem, selecting a set of representative members

More information

Lecture 2: Discrete Distributions, Normal Distributions. Chapter 1

Lecture 2: Discrete Distributions, Normal Distributions. Chapter 1 Lecture 2: Discrete Distributions, Normal Distributions Chapter 1 Reminders Course website: www. stat.purdue.edu/~xuanyaoh/stat350 Office Hour: Mon 3:30-4:30, Wed 4-5 Bring a calculator, and copy Tables

More information

Lecture 5 : The Poisson Distribution

Lecture 5 : The Poisson Distribution Lecture 5 : The Poisson Distribution Jonathan Marchini November 10, 2008 1 Introduction Many experimental situations occur in which we observe the counts of events within a set unit of time, area, volume,

More information

Radiometric Dating Lab By Vicky Jordan

Radiometric Dating Lab By Vicky Jordan Science 8: The Deep Time Diaries Name Date Per Radiometric Dating Lab By Vicky Jordan Problem: How long will it take for 100 atoms of the radioactive parent Carbon-14 to completely decay to the stable

More information

Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010

Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different

More information

Normal Distribution as an Approximation to the Binomial Distribution

Normal Distribution as an Approximation to the Binomial Distribution Chapter 1 Student Lecture Notes 1-1 Normal Distribution as an Approximation to the Binomial Distribution : Goals ONE TWO THREE 2 Review Binomial Probability Distribution applies to a discrete random variable

More information

Center: Finding the Median. Median. Spread: Home on the Range. Center: Finding the Median (cont.)

Center: Finding the Median. Median. Spread: Home on the Range. Center: Finding the Median (cont.) Center: Finding the Median When we think of a typical value, we usually look for the center of the distribution. For a unimodal, symmetric distribution, it s easy to find the center it s just the center

More information

WEEK #23: Statistics for Spread; Binomial Distribution

WEEK #23: Statistics for Spread; Binomial Distribution WEEK #23: Statistics for Spread; Binomial Distribution Goals: Study measures of central spread, such interquartile range, variance, and standard deviation. Introduce standard distributions, including the

More information

ALGEBRA 2: 4.1 Graph Quadratic Functions in Standard Form

ALGEBRA 2: 4.1 Graph Quadratic Functions in Standard Form ALGEBRA 2: 4.1 Graph Quadratic Functions in Standard Form Goal Graph quadratic functions. VOCABULARY Quadratic function A function that can be written in the standard form y = ax 2 + bx+ c where a 0 Parabola

More information

Variables. Exploratory Data Analysis

Variables. Exploratory Data Analysis Exploratory Data Analysis Exploratory Data Analysis involves both graphical displays of data and numerical summaries of data. A common situation is for a data set to be represented as a matrix. There is

More information

CALCULATIONS & STATISTICS

CALCULATIONS & STATISTICS CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents

More information