Introducing Sample Proportions

Similar documents
Introducing Sample Proportions

Confidence intervals for proportions

Distribution of Sample Proportions

STAT 350 Handout 19 Sampling Distribution, Central Limit Theorem (6.6)

Statistics 511 Additional Materials

Pearson Edexcel Level 3 Advanced Subsidiary and Advanced GCE in Statistics

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01

AAEC/ECON 5126 FINAL EXAM: SOLUTIONS

Sampling Error. Chapter 6 Student Lecture Notes 6-1. Business Statistics: A Decision-Making Approach, 6e. Chapter Goals


Building Sequences and Series with a Spreadsheet (Create)

MATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4

Parameter, Statistic and Random Samples

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Solutions Descriptive Statistics. None at all!

Chapter 6 Part 5. Confidence Intervals t distribution chi square distribution. October 23, 2008

Chapter 8: STATISTICAL INTERVALS FOR A SINGLE SAMPLE. Part 3: Summary of CI for µ Confidence Interval for a Population Proportion p

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals

Properties and Hypothesis Testing

Class 23. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

1 Inferential Methods for Correlation and Regression Analysis

Big Picture. 5. Data, Estimates, and Models: quantifying the accuracy of estimates.

Understanding Samples

Expectation and Variance of a random variable

PH 425 Quantum Measurement and Spin Winter SPINS Lab 1

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ

Estimation for Complete Data

MATH/STAT 352: Lecture 15

Sampling Distributions, Z-Tests, Power

Random Variables, Sampling and Estimation

Y i n. i=1. = 1 [number of successes] number of successes = n

Mathematical Notation Math Introduction to Applied Statistics

The Poisson Distribution

Sampling Distribution of Differences

Topic 9: Sampling Distributions of Estimators

KLMED8004 Medical statistics. Part I, autumn Estimation. We have previously learned: Population and sample. New questions

MATHEMATICS. The assessment objectives of the Compulsory Part are to test the candidates :

Stat 200 -Testing Summary Page 1

µ and π p i.e. Point Estimation x And, more generally, the population proportion is approximately equal to a sample proportion

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 9

Median and IQR The median is the value which divides the ordered data values in half.

Sample Size Determination (Two or More Samples)

Statistics Lecture 27. Final review. Administrative Notes. Outline. Experiments. Sampling and Surveys. Administrative Notes

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

Estimation of a population proportion March 23,

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc.

Chapter 2 Descriptive Statistics

Understanding Dissimilarity Among Samples

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1.

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

Statistical Properties of OLS estimators

On a Smarandache problem concerning the prime gaps

Successful HE applicants. Information sheet A Number of applicants. Gender Applicants Accepts Applicants Accepts. Age. Domicile

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

Chapter 8: Estimating with Confidence

MEASURES OF DISPERSION (VARIABILITY)

S160 #12. Sampling Distribution of the Proportion, Part 2. JC Wang. February 25, 2016

S160 #12. Review of Large Sample Result for Sample Proportion

Lecture 2: Monte Carlo Simulation

NCSS Statistical Software. Tolerance Intervals

Confidence intervals summary Conservative and approximate confidence intervals for a binomial p Examples. MATH1005 Statistics. Lecture 24. M.

STAT 515 fa 2016 Lec Sampling distribution of the mean, part 2 (central limit theorem)

Confidence Intervals for the Population Proportion p

Final Examination Solutions 17/6/2010

(6) Fundamental Sampling Distribution and Data Discription

April 18, 2017 CONFIDENCE INTERVALS AND HYPOTHESIS TESTING, UNDERGRADUATE MATH 526 STYLE

Common Large/Small Sample Tests 1/55

BIOSTATISTICS. Lecture 5 Interval Estimations for Mean and Proportion. dr. Petr Nazarov

Chapter 13, Part A Analysis of Variance and Experimental Design

Read through these prior to coming to the test and follow them when you take your test.

Power and Type II Error

Chapter 6 Principles of Data Reduction

For nominal data, we use mode to describe the central location instead of using sample mean/median.

Introduction to Probability and Statistics Twelfth Edition

(7 One- and Two-Sample Estimation Problem )

Describing the Relation between Two Variables

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. Comments:

[ ] ( ) ( ) [ ] ( ) 1 [ ] [ ] Sums of Random Variables Y = a 1 X 1 + a 2 X 2 + +a n X n The expected value of Y is:

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Elementary Statistics

Simulation. Two Rule For Inverting A Distribution Function

MBACATÓLICA. Quantitative Methods. Faculdade de Ciências Económicas e Empresariais UNIVERSIDADE CATÓLICA PORTUGUESA 9. SAMPLING DISTRIBUTIONS

Binomial Distribution

Chapter If n is odd, the median is the exact middle number If n is even, the median is the average of the two middle numbers

Statistics 300: Elementary Statistics

multiplies all measures of center and the standard deviation and range by k, while the variance is multiplied by k 2.

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

Open book and notes. 120 minutes. Cover page and six pages of exam. No calculators.

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

AP Statistics Review Ch. 8

BIOSTATS 640 Intermediate Biostatistics Frequently Asked Questions Topic 1 FAQ 1 Review of BIOSTATS 540 Introductory Biostatistics

Topic 10: Introduction to Estimation

Statistical Intervals for a Single Sample

Instructor: Judith Canner Spring 2010 CONFIDENCE INTERVALS How do we make inferences about the population parameters?

Topic 9: Sampling Distributions of Estimators

Continuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

Comparing Two Populations. Topic 15 - Two Sample Inference I. Comparing Two Means. Comparing Two Pop Means. Background Reading

Statistical inference: example 1. Inferential Statistics

Math 140 Introductory Statistics

Transcription:

Itroducig Sample Proportios Probability ad statistics Studet Activity TI-Nspire Ivestigatio Studet 60 mi 7 8 9 10 11 12 Itroductio A 2010 survey of attitudes to climate chage, coducted i Australia by the CSIRO, reported that 40% of respodets thought of climate chage i terms of atural temperature variability, rather tha i terms of huma-iduced temperature chage. (Referece: https://publicatios.csiro.au/rpr/dowload?pid=csiro:ep105359&dsid=ds3). How reliable is this proportio, which is based o a radom sample? Does this statistic reflect the proportio of the etire Australia populatio with this belief about climate chage? I this activity, you will ivestigate how much we ca expect proportios from radom samples to vary from sample to sample. Simulatig radom samplig for attitudes to climate chage Ope the TI-Nspire documet Sample_proportios. You will be usig this documet to geerate radom samples from a large populatio. To esure that your results are ot idetical to those of other studets, you will seed the radom umber geerator, as follows. Navigate to Page 1.2. I the Math Box, after the RadSeed commad, iput a space followed by a umber uique to you such as the last 4 digits of your phoe umber. Press eter to execute the commad. Questio 1 Why do you thik that you might get idetical results to those of other studets i the room, if you do ot seed the radom umber geerator of your hadheld? 1.0 Drawig radom samples of size 50 I this sectio, you will simulate drawig radom samples of size 50 from a large populatio. You will use the sample results to estimate the proportio of the populatio who believe that climate chage is due to atural temperature variability (hereafter referred to as atural climate chage). Let deote the sample size; therefore, i this sectio, 50. Let p deote proportio of the populatio who believe i atural climate chage. Let X deote the umber, i radom samples, who believe i atural climate chage, ad let x deote the values of the variable, X. Let ˆP deote the proportio, i radom samples, who believe i atural climate chage, ad let ˆp deote the values of the variable, ˆP. The values, ˆp, are estimators of the true populatio proportio, p. Texas Istrumets 2015. You may copy, commuicate ad modify this material for o-commercial educatioal purposes

Itroducig sample proportios - Studet Worksheet 2 1.1 Numerical represetatio of sample proportios: = 50 Navigate to Page 2.2. Adjust the slider value to k 1, to simulate drawig a sigle radom sample from a large populatio, where the value of the populatio proportio, p, is ukow to you. The umber of people, x, i the sample, who believe i atural climate chage is displayed. Note that the values of x are displayed i the spreadsheet (i the colum amed x ) ad as a list i the left-had pael. Questio 2 a. I the Math Box idicated, use the observed value of x to calculate a poit estimate of the proportio of the populatio who believe i atural climate chage. b. How likely do you thik it is that the poit estimate, calculated above, eds up beig idetical to the true populatio proportio? O Page 2.2, press /+e util the spreadsheet pael of the split scree is selected. Press /+R to simulate drawig a differet sample of size 50 from the populatio. Questio 3 a. Use 3 more observed value of x, obtaied after pressig /+R, to calculate 3 more poit estimates of the proportio of the populatio who believe i atural climate chage. b. How much variability is there betwee poit estimates obtaied from the differet samples? c. Isert a ew Mathbox (/+M) i the left-had pael. I this Math Box, calculate the mea of the four poit estimates. d. Explai why the mea of the four sample proportios is likely to give a better estimate of the populatio proportio tha the idividual poit estimates. 1.2 Simulatig multiple samples: = 50 O Page 2.2, adjust the slider value to k 20, which simulate drawig 20 radom samples of size 50. The umber of atural climate chage believers i each simulated sample is displayed i the list ad i the spreadsheet. I the Math Box from Questio 2a, calculate the sample proportios by iputtig the expressio: 50 x. Store these proportios as a variable, ˆp ; i.e. press Ë(/+h), the Ð(/+k) ad select symbol, ˆp. Press to execute the calculatio. Questio 4 Compare the largest ad smallest umber of atural climate chage believers i the samples, ad their correspodig sample proportios. Calculate the percetage differece betwee these two proportios (i.e. the magitude of the differece, divided by the maximum value ad multiplied by 100). Texas Istrumets 2015. You may copy, commuicate ad modify this material for o-commercial educatioal purposes

Itroducig sample proportios - Studet Worksheet 3 I the Math Box from Questio 3c, calculate the mea value of all the sample proportios, by iputtig mea p ˆ, ad pressig to execute the calculatio. the expressio: Icremetally icrease the umber of samples draw by chagig the value of k to 40, 60, 80 200. Questio 5 Do you otice ay patter i the mea of the sample proportios, as the umber of samples, k, icreases? 1.3 Graphical represetatio of sample proportios: = 50 Navigate to Page 3.1. I Problem 3 you will simulate drawig samples of size 50 from a large populatio where the populatio proportio of atural climate chage believers is kow to be p 0.4. Of course, i practice we would ot carry out samplig if we already kew the value of the populatio proportio. I this activity, the purpose of samplig is to uderstad how samplig behaves. O Page 3.2 you ca view umerical represetatios of the simulatios, similar to that of Problem 2. However, i the pages that follow, you will see the graphical represetatio of these results. Navigate to Page 3.3 ad adjust the slider value to k 1, to simulate drawig a sigle radom sample of size 50, from a populatio where p 0.4. The umber of people, x, i the sample, who believe i atural climate chage is displayed graphically. Adjust the slider value to simulate drawig 2, 3, 10 samples from this populatio. The vertical lie idicates the mea value. Questio 6 a. For your 10 samples ( k 10 ), what are the observed maximum, miimum ad mea umber of atural climate chage believers? b. I a sample of 50 people, how may atural climate chage believers would you expect, if the populatio proportio is 0.4? Why is t this expected umber obtaied each time you draw a sample? c. Adjust the slider value to k 200. I how may of the 200 samples was the observed value of x equal to the expected value? 1.4 The sample cout as a radom variable I the precedig activities, you have see that the value, x, couts the umber of atural climate chage believers i a sample. The cout ca therefore be cosidered as radom variable X, whose values, x, vary from sample to sample. Furthermore, X is biomially distributed, with parameters (sample size) X Bi, p. ad p (populatio proportio); that is Questio 7 Assume that coutig a atural climate chage believer deotes success. Explai why X is a biomial radom variable, with the populatio proportio beig the probability of success i a sigle trial. Texas Istrumets 2015. You may copy, commuicate ad modify this material for o-commercial educatioal purposes

Itroducig sample proportios - Studet Worksheet 4 1.5 The sample proportio as a radom variable Navigate to Page 3.4, ad icremetally icrease the umber of samples observed, by adjustig the value of k, up to k 200. The observed sample proportio for each of the k samples is displayed graphically. The vertical lie shows the mea of the sample proportios. If you avigate back to Page 3.3, you will observe a aalogous graphical patter for the success cout, for the samples of size 50. Questio 8 a. Explai why the graphs i Pages 3.3 ad 3.4 have idetical shapes. b. For a particular sample, what is the relatioship betwee the sample cout of successes, x, ad the sample proportio of successes, ˆp. You will have observed that each time you take a radom sample from a large populatio, the sample cout of successes, x, ad the sample proportio of successes, ˆp, vary from sample to sample; x furthermore, for a particular sample of size, ˆp. Just as the cout ca be cosidered a radom variable X, whose values, x, vary from sample to sample, the sample proportio ca, likewise, be cosidered a radom variable ˆP, whose values, ˆp, vary i the same fashio as x. Therefore, cosiderig the radom variables, ˆP X. 2. Expectatio ad stadard deviatio of the sample proportio Navigate to Page 3.5, ad icremetally icrease the umber of samples observed, by adjustig the value of k, up to k 200. The observed frequecies of sample proportios for the k samples is displayed as a histogram. Navigate to Page 3.6 to view the same data, displayed as a boxplot. From these, we observe that the sample proportio has a distributio, for which a mea ad stadard deviatio ca be computed. Below we will cosider the theoretical expectatio (mea) ad stadard deviatio of the sample proportio. Texas Istrumets 2015. You may copy, commuicate ad modify this material for o-commercial educatioal purposes

Itroducig sample proportios - Studet Worksheet 5 Questio 9 Recall that ˆP Further, if Y X ax, where X Bi, p. Also, for a biomial radom variable, E X, where a is a costat, the EY ae X a. Usig the above iformatio, show that EPˆ p. b. Explai the sigificace of the result obtaied i part a. above. Questio 10 For a biomial radom variable, var X p1 p. 2 Further, if Y ax, where a is a costat, the var Y a var X. p. a. Usig the above iformatio, ad Questio 9, show that the stadard deviatio of the sample proportio, SDPˆ p 1 p. b. The variace ad stadard deviatio of ˆP have i the deomiator. Explai the implicatios of this, i terms of the spread of the distributio of ˆP. The cocepts itroduced i this activity are explored further i the activity titled Distributio of sample proportios. Texas Istrumets 2015. You may copy, commuicate ad modify this material for o-commercial educatioal purposes