χ L = χ R =

Size: px
Start display at page:

Download "χ L = χ R ="

Transcription

1 Chapter 7 3C: Examples of Constructing a Confidence Interval for the true value of the Population Standard Deviation σ for a Normal Population. Example 1 Construct a 95% confidence interval for the true value of the population standard deviation σ if the population is normal. A random sample of size 31( n = 31) results in a sample mean ( x ) of x =1.8 and a sample standard deviation of s x =1.6 The population is given as normal so we can use the Chi Square χ Table. The confidence level is 95% so α =.05 If α =.05 then α =.05 DF = n 1 = 31 1 = 30 x =1.8 s x =1.6 n = 31 DF = 30 α =.05 = χr = The Confidence Interval for the population standard deviation α is given by (n 1)(s x ) (n 1)(s x ) (30)(1.6) (30)(1.6) Confidence Interval Statement: I am 95% confident that the interval contains the value of the true population standard deviation α Finding and χr right of is.975 α =.05 α =.05 right of is.05 χ = = Stat C Examples Page 1 of Eitel

2 Example 1 detailed explanation for finding To find the value for a right tail area of 0.05 and Degrees of Freedom 30 use the part of the χ table shown below. the value is + tα = DF = 30 right of is.05 right tail area α =.05 0 = χ Area to the right of the Chi-Square value D of F Stat C Examples Page of Eitel

3 Example 1 detailed explanation for finding To find you must use the AREA TO THE RIGHT of if the left tail has an area of.05 then RIGHT of is.975 To find the value for a right tail area of and Degrees of Freedom 30 use the part of the χ table shown below the value is + tα = Degrees of Freedom = 30 right of is.975 α =.05 = χ Area to the right of the Chi-Square value D of F Stat C Examples Page 3 of Eitel

4 Example Construct a 99 % confidence interval for the true value of the population standard deviation σ if the population is normal. A random sample of size 71( n = 71) results in a sample mean ( x ) of x = 7.1 and a sample standard deviation of s x = 5.8 The population is given as normal so we can use the Chi Square χ Table. The confidence level is 99 % so α =.01 and α =.005 DF = n 1 = 71 1 = 70 x = 7.1 s x = 5.8 n = 71 DF = 70 α =.005 = χr = The Confidence Interval for the population standard deviation σ is given by (n 1)(s x ) (n 1)(s x ) (70)(5.8) (70)(5.8) Confidence Interval Statement: I am 99 % confident that the interval contains the value of the true population standard deviation α Finding and χr right of is.995 right of is.005 α = α = χ = = Area to the RIGHT of the Chi-Square value D of F Stat C Examples Page 4 of Eitel

5 Example detailed explanation for finding Finding To find the value for a right tail area of and Degrees of Freedom 70 use the part of the χ table shown below. the value is + tα = DF = 70 right of is.005 right tail area α = = χ Area to the RIGHT of the Chi-Square value D of F Stat C Examples Page 5 of Eitel

6 Example detailed explanation for finding To find you must use the AREA TO THE RIGHT of if the left tail has an area of.005 then RIGHT of is.995 To find the value for a right tail area of and Degrees of Freedom 70 use the part of the χ table shown below the value is + tα = DF = 70 left tail area α =.005 right of is.995 χ = Area to the right of the Chi-Square value D of F Stat C Examples Page 6 of Eitel

7 Example 3 The California Consumer Protection Agency decides to conduct an investigation into the quality of car tires. They sample 41 randomly selected tires and find an average tread wear of 79,000 and a standard deviation of,300 miles. Construct a 90% confidence interval for the true value of the population standard deviation σ. Assume that if the population of car tire tread wear is normally distributed. The population is given as normal so we can use the Chi Square χ Table. The confidence level is 90% so α =.10 and α =.05 DF = n 1 = 41 1 = 40 x = 79,000 s x =,300 n = 41 DF = 40 α =.05 = χr = The Confidence Interval for the population standard deviation α is given by (n 1)(s x ) (n 1)(s x ) (40)(300) (40)(300) Confidence Interval Statement: I am 99% confident that the interval contains the value of the true population standard deviation α Finding and χr right of is.95 right of is.05 α = α = χ = = Stat C Examples Page 7 of Eitel

8 Area to the RIGHT of the Chi-Square value D of F Example 3 detailed explanation for finding Finding To find the value for a right tail area of α = 0.05 and Degrees of Freedom 40 use the part of the χ table shown below. the value is + tα = DF = 40 right of is.05 right tail area α =.05 0 = χ Area to the RIGHT of the Chi-Square value D of F Stat C Examples Page 8 of Eitel

9 Example 3 detailed explanation for finding To find you must use the AREA TO THE RIGHT of if the left tail has an area of.05 then RIGHT of is.95 To find the value for a right tail area of 0.95 and Degrees of Freedom 40 use the part of the χ table shown below. the value is + tα = DF = 40 left tail area α =.05 right of is.95 χ = Area to the right of the Chi-Square value D of F Stat C Examples Page 9 of Eitel

10 Stat C Examples Page 10 of Eitel

Section 9 2B:!! Using Confidence Intervals to Estimate the Difference ( µ 1 µ 2 ) in Two Population Means using Two Independent Samples.

Section 9 2B:!! Using Confidence Intervals to Estimate the Difference ( µ 1 µ 2 ) in Two Population Means using Two Independent Samples. Section 9 2B:!! Using Confidence Intervals to Estimate the Difference ( µ 1 µ 2 ) in Two Population Means using Two Independent Samples Requirements 1.A random sample of each population is taken. The sample

More information

Tables Table A Table B Table C Table D Table E 675

Tables Table A Table B Table C Table D Table E 675 BMTables.indd Page 675 11/15/11 4:25:16 PM user-s163 Tables Table A Standard Normal Probabilities Table B Random Digits Table C t Distribution Critical Values Table D Chi-square Distribution Critical Values

More information

Inference About Means and Proportions with Two Populations. Chapter 10

Inference About Means and Proportions with Two Populations. Chapter 10 Inference About Means and Proportions with Two Populations Chapter 10 Two Populations? Chapter 8 we found interval estimates for the population mean and population proportion based on a random sample Chapter

More information

The Chi-Square Distributions

The Chi-Square Distributions MATH 183 The Chi-Square Distributions Dr. Neal, WKU The chi-square distributions can be used in statistics to analyze the standard deviation σ of a normally distributed measurement and to test the goodness

More information

Chapter 10: Inferences based on two samples

Chapter 10: Inferences based on two samples November 16 th, 2017 Overview Week 1 Week 2 Week 4 Week 7 Week 10 Week 12 Chapter 1: Descriptive statistics Chapter 6: Statistics and Sampling Distributions Chapter 7: Point Estimation Chapter 8: Confidence

More information

Estimating a Population Mean

Estimating a Population Mean Estimating a Population Mean MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2017 Objectives At the end of this lesson we will be able to: obtain a point estimate for

More information

The goodness-of-fit test Having discussed how to make comparisons between two proportions, we now consider comparisons of multiple proportions.

The goodness-of-fit test Having discussed how to make comparisons between two proportions, we now consider comparisons of multiple proportions. The goodness-of-fit test Having discussed how to make comparisons between two proportions, we now consider comparisons of multiple proportions. A common problem of this type is concerned with determining

More information

Stats Review Chapter 14. Mary Stangler Center for Academic Success Revised 8/16

Stats Review Chapter 14. Mary Stangler Center for Academic Success Revised 8/16 Stats Review Chapter 14 Revised 8/16 Note: This review is meant to highlight basic concepts from the course. It does not cover all concepts presented by your instructor. Refer back to your notes, unit

More information

Sampling distribution of t. 2. Sampling distribution of t. 3. Example: Gas mileage investigation. II. Inferential Statistics (8) t =

Sampling distribution of t. 2. Sampling distribution of t. 3. Example: Gas mileage investigation. II. Inferential Statistics (8) t = 2. The distribution of t values that would be obtained if a value of t were calculated for each sample mean for all possible random of a given size from a population _ t ratio: (X - µ hyp ) t s x The result

More information

Hypothesis Tests and Estimation for Population Variances. Copyright 2014 Pearson Education, Inc.

Hypothesis Tests and Estimation for Population Variances. Copyright 2014 Pearson Education, Inc. Hypothesis Tests and Estimation for Population Variances 11-1 Learning Outcomes Outcome 1. Formulate and carry out hypothesis tests for a single population variance. Outcome 2. Develop and interpret confidence

More information

Section 9.4. Notation. Requirements. Definition. Inferences About Two Means (Matched Pairs) Examples

Section 9.4. Notation. Requirements. Definition. Inferences About Two Means (Matched Pairs) Examples Objective Section 9.4 Inferences About Two Means (Matched Pairs) Compare of two matched-paired means using two samples from each population. Hypothesis Tests and Confidence Intervals of two dependent means

More information

POLI 443 Applied Political Research

POLI 443 Applied Political Research POLI 443 Applied Political Research Session 6: Tests of Hypotheses Contingency Analysis Lecturer: Prof. A. Essuman-Johnson, Dept. of Political Science Contact Information: aessuman-johnson@ug.edu.gh College

More information

MBA 605, Business Analytics Donald D. Conant, Ph.D. Master of Business Administration

MBA 605, Business Analytics Donald D. Conant, Ph.D. Master of Business Administration t-distribution Summary MBA 605, Business Analytics Donald D. Conant, Ph.D. Types of t-tests There are several types of t-test. In this course we discuss three. The single-sample t-test The two-sample t-test

More information

10.2: The Chi Square Test for Goodness of Fit

10.2: The Chi Square Test for Goodness of Fit 10.2: The Chi Square Test for Goodness of Fit We can perform a hypothesis test to determine whether the distribution of a single categorical variable is following a proposed distribution. We call this

More information

The Chi-Square Distributions

The Chi-Square Distributions MATH 03 The Chi-Square Distributions Dr. Neal, Spring 009 The chi-square distributions can be used in statistics to analyze the standard deviation of a normally distributed measurement and to test the

More information

STAT 115:Experimental Designs

STAT 115:Experimental Designs STAT 115:Experimental Designs Josefina V. Almeda 2013 Multisample inference: Analysis of Variance 1 Learning Objectives 1. Describe Analysis of Variance (ANOVA) 2. Explain the Rationale of ANOVA 3. Compare

More information

STAT 135 Lab 6 Duality of Hypothesis Testing and Confidence Intervals, GLRT, Pearson χ 2 Tests and Q-Q plots. March 8, 2015

STAT 135 Lab 6 Duality of Hypothesis Testing and Confidence Intervals, GLRT, Pearson χ 2 Tests and Q-Q plots. March 8, 2015 STAT 135 Lab 6 Duality of Hypothesis Testing and Confidence Intervals, GLRT, Pearson χ 2 Tests and Q-Q plots March 8, 2015 The duality between CI and hypothesis testing The duality between CI and hypothesis

More information

Chapter 9. Inferences from Two Samples. Objective. Notation. Section 9.2. Definition. Notation. q = 1 p. Inferences About Two Proportions

Chapter 9. Inferences from Two Samples. Objective. Notation. Section 9.2. Definition. Notation. q = 1 p. Inferences About Two Proportions Chapter 9 Inferences from Two Samples 9. Inferences About Two Proportions 9.3 Inferences About Two s (Independent) 9.4 Inferences About Two s (Matched Pairs) 9.5 Comparing Variation in Two Samples Objective

More information

Chapter 12: Inference about One Population

Chapter 12: Inference about One Population Chapter 1: Inference about One Population 1.1 Introduction In this chapter, we presented the statistical inference methods used when the problem objective is to describe a single population. Sections 1.

More information

12.10 (STUDENT CD-ROM TOPIC) CHI-SQUARE GOODNESS- OF-FIT TESTS

12.10 (STUDENT CD-ROM TOPIC) CHI-SQUARE GOODNESS- OF-FIT TESTS CDR4_BERE601_11_SE_C1QXD 1//08 1:0 PM Page 1 110: (Student CD-ROM Topic) Chi-Square Goodness-of-Fit Tests CD1-1 110 (STUDENT CD-ROM TOPIC) CHI-SQUARE GOODNESS- OF-FIT TESTS In this section, χ goodness-of-fit

More information

Study Ch. 13.1, # 1 4 all Study Ch. 13.2, # 9 15, 25, 27, 31 [# 11 17, ~27, 29, ~33]

Study Ch. 13.1, # 1 4 all Study Ch. 13.2, # 9 15, 25, 27, 31 [# 11 17, ~27, 29, ~33] GOALS: 1. Learn the properties of the χ 2 Distribution. 2. Understand how the shape of the χ 2 Distribution changes as the df increases. 3. Be able to find p values. 4. Recognize that χ 2 tests are right

More information

6. CONFIDENCE INTERVALS. Training is everything cauliflower is nothing but cabbage with a college education.

6. CONFIDENCE INTERVALS. Training is everything cauliflower is nothing but cabbage with a college education. CIVL 3103 Approximation and Uncertainty J.W. Hurley, R.W. Meier 6. CONFIDENCE INTERVALS Training is everything cauliflower is nothing but cabbage with a college education. Mark Twain At the beginning of

More information

Review 6. n 1 = 85 n 2 = 75 x 1 = x 2 = s 1 = 38.7 s 2 = 39.2

Review 6. n 1 = 85 n 2 = 75 x 1 = x 2 = s 1 = 38.7 s 2 = 39.2 Review 6 Use the traditional method to test the given hypothesis. Assume that the samples are independent and that they have been randomly selected ) A researcher finds that of,000 people who said that

More information

40.2. Interval Estimation for the Variance. Introduction. Prerequisites. Learning Outcomes

40.2. Interval Estimation for the Variance. Introduction. Prerequisites. Learning Outcomes Interval Estimation for the Variance 40.2 Introduction In Section 40.1 we have seen that the sampling distribution of the sample mean, when the data come from a normal distribution (and even, in large

More information

Final Exam Review STAT 212

Final Exam Review STAT 212 Final Exam Review STAT 212 1/ A market researcher randomly selects 100 homeowners under 60 years of age and 200 homeowners over 60 years of age. What sampling technique was used? A. Systematic B. Convenience

More information

Chapter 7: Hypothesis Testing - Solutions

Chapter 7: Hypothesis Testing - Solutions Chapter 7: Hypothesis Testing - Solutions 7.1 Introduction to Hypothesis Testing The problem with applying the techniques learned in Chapter 5 is that typically, the population mean (µ) and standard deviation

More information

7.2 One-Sample Correlation ( = a) Introduction. Correlation analysis measures the strength and direction of association between

7.2 One-Sample Correlation ( = a) Introduction. Correlation analysis measures the strength and direction of association between 7.2 One-Sample Correlation ( = a) Introduction Correlation analysis measures the strength and direction of association between variables. In this chapter we will test whether the population correlation

More information

Which of the two quantities should be set equal to x?

Which of the two quantities should be set equal to x? Section 3 2: Let Statements Algebraic Expressions For Two Quantities Almost all word problems are based on a comparison between two (or more) unknown quantities. In any word problem, one quantity is being

More information

Problem Set 4 - Solutions

Problem Set 4 - Solutions Problem Set 4 - Solutions Econ-310, Spring 004 8. a. If we wish to test the research hypothesis that the mean GHQ score for all unemployed men exceeds 10, we test: H 0 : µ 10 H a : µ > 10 This is a one-tailed

More information

:the actual population proportion are equal to the hypothesized sample proportions 2. H a

:the actual population proportion are equal to the hypothesized sample proportions 2. H a AP Statistics Chapter 14 Chi- Square Distribution Procedures I. Chi- Square Distribution ( χ 2 ) The chi- square test is used when comparing categorical data or multiple proportions. a. Family of only

More information

Testing a Claim about the Difference in 2 Population Means Independent Samples. (there is no difference in Population Means µ 1 µ 2 = 0) against

Testing a Claim about the Difference in 2 Population Means Independent Samples. (there is no difference in Population Means µ 1 µ 2 = 0) against Section 9 2A Lecture Testing a Claim about the Difference i Population Means Independent Samples Test H 0 : µ 1 = µ 2 (there is no difference in Population Means µ 1 µ 2 = 0) against H 1 : µ 1 > µ 2 or

More information

Chapter Eight: Assessment of Relationships 1/42

Chapter Eight: Assessment of Relationships 1/42 Chapter Eight: Assessment of Relationships 1/42 8.1 Introduction 2/42 Background This chapter deals, primarily, with two topics. The Pearson product-moment correlation coefficient. The chi-square test

More information

Objectives Simple linear regression. Statistical model for linear regression. Estimating the regression parameters

Objectives Simple linear regression. Statistical model for linear regression. Estimating the regression parameters Objectives 10.1 Simple linear regression Statistical model for linear regression Estimating the regression parameters Confidence interval for regression parameters Significance test for the slope Confidence

More information

An interval estimator of a parameter θ is of the form θl < θ < θu at a

An interval estimator of a parameter θ is of the form θl < θ < θu at a Chapter 7 of Devore CONFIDENCE INTERVAL ESTIMATORS An interval estimator of a parameter θ is of the form θl < θ < θu at a confidence pr (or a confidence coefficient) of 1 α. When θl =, < θ < θu is called

More information

ME3620. Theory of Engineering Experimentation. Spring Chapter IV. Decision Making for a Single Sample. Chapter IV

ME3620. Theory of Engineering Experimentation. Spring Chapter IV. Decision Making for a Single Sample. Chapter IV Theory of Engineering Experimentation Chapter IV. Decision Making for a Single Sample Chapter IV 1 4 1 Statistical Inference The field of statistical inference consists of those methods used to make decisions

More information

SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.

SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Chapter 7 Exam A Name 1) How do you determine whether to use the z or t distribution in computing the margin of error, E = z α/2 σn or E = t α/2 s n? 1) Use the given degree of confidence and sample data

More information

Quantitative Analysis and Empirical Methods

Quantitative Analysis and Empirical Methods Hypothesis testing Sciences Po, Paris, CEE / LIEPP Introduction Hypotheses Procedure of hypothesis testing Two-tailed and one-tailed tests Statistical tests with categorical variables A hypothesis A testable

More information

The independent-means t-test:

The independent-means t-test: The independent-means t-test: Answers the question: is there a "real" difference between the two conditions in my experiment? Or is the difference due to chance? Previous lecture: (a) Dependent-means t-test:

More information

Statistics for Business and Economics

Statistics for Business and Economics Statistics for Business and Economics Chapter 6 Sampling and Sampling Distributions Ch. 6-1 6.1 Tools of Business Statistics n Descriptive statistics n Collecting, presenting, and describing data n Inferential

More information

3. (a) (8 points) There is more than one way to correctly express the null hypothesis in matrix form. One way to state the null hypothesis is

3. (a) (8 points) There is more than one way to correctly express the null hypothesis in matrix form. One way to state the null hypothesis is Stat 501 Solutions and Comments on Exam 1 Spring 005-4 0-4 1. (a) (5 points) Y ~ N, -1-4 34 (b) (5 points) X (X,X ) = (5,8) ~ N ( 11.5, 0.9375 ) 3 1 (c) (10 points, for each part) (i), (ii), and (v) are

More information

STAT Chapter 11: Regression

STAT Chapter 11: Regression STAT 515 -- Chapter 11: Regression Mostly we have studied the behavior of a single random variable. Often, however, we gather data on two random variables. We wish to determine: Is there a relationship

More information

Applied Statistics I

Applied Statistics I Applied Statistics I Liang Zhang Department of Mathematics, University of Utah July 17, 2008 Liang Zhang (UofU) Applied Statistics I July 17, 2008 1 / 23 Large-Sample Confidence Intervals Liang Zhang (UofU)

More information

Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual

Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual Question 1. Suppose you want to estimate the percentage of

More information

Inferences Based on Two Samples

Inferences Based on Two Samples Chapter 6 Inferences Based on Two Samples Frequently we want to use statistical techniques to compare two populations. For example, one might wish to compare the proportions of families with incomes below

More information

QUIZ 4 (CHAPTER 7) - SOLUTIONS MATH 119 SPRING 2013 KUNIYUKI 105 POINTS TOTAL, BUT 100 POINTS = 100%

QUIZ 4 (CHAPTER 7) - SOLUTIONS MATH 119 SPRING 2013 KUNIYUKI 105 POINTS TOTAL, BUT 100 POINTS = 100% QUIZ 4 (CHAPTER 7) - SOLUTIONS MATH 119 SPRING 013 KUNIYUKI 105 POINTS TOTAL, BUT 100 POINTS = 100% 1) We want to conduct a study to estimate the mean I.Q. of a pop singer s fans. We want to have 96% confidence

More information

T.I.H.E. IT 233 Statistics and Probability: Sem. 1: 2013 ESTIMATION AND HYPOTHESIS TESTING OF TWO POPULATIONS

T.I.H.E. IT 233 Statistics and Probability: Sem. 1: 2013 ESTIMATION AND HYPOTHESIS TESTING OF TWO POPULATIONS ESTIMATION AND HYPOTHESIS TESTING OF TWO POPULATIONS In our work on hypothesis testing, we used the value of a sample statistic to challenge an accepted value of a population parameter. We focused only

More information

CHAPTER 13: F PROBABILITY DISTRIBUTION

CHAPTER 13: F PROBABILITY DISTRIBUTION CHAPTER 13: F PROBABILITY DISTRIBUTION continuous probability distribution skewed to the right variable values on horizontal axis are 0 area under the curve represents probability horizontal asymptote

More information

Mathematical statistics

Mathematical statistics November 15 th, 2018 Lecture 21: The two-sample t-test Overview Week 1 Week 2 Week 4 Week 7 Week 10 Week 14 Probability reviews Chapter 6: Statistics and Sampling Distributions Chapter 7: Point Estimation

More information

Ch. 7 Statistical Intervals Based on a Single Sample

Ch. 7 Statistical Intervals Based on a Single Sample Ch. 7 Statistical Intervals Based on a Single Sample Before discussing the topics in Ch. 7, we need to cover one important concept from Ch. 6. Standard error The standard error is the standard deviation

More information

Chi square test of independence

Chi square test of independence Chi square test of independence Eyeball differences between percentages: large enough to be important Better: Are they statistically significant? Statistical significance: are observed differences significantly

More information

Hypothesis testing for µ:

Hypothesis testing for µ: University of California, Los Angeles Department of Statistics Statistics 10 Elements of a hypothesis test: Hypothesis testing Instructor: Nicolas Christou 1. Null hypothesis, H 0 (always =). 2. Alternative

More information

Confidence Intervals, Testing and ANOVA Summary

Confidence Intervals, Testing and ANOVA Summary Confidence Intervals, Testing and ANOVA Summary 1 One Sample Tests 1.1 One Sample z test: Mean (σ known) Let X 1,, X n a r.s. from N(µ, σ) or n > 30. Let The test statistic is H 0 : µ = µ 0. z = x µ 0

More information

Stat 427/527: Advanced Data Analysis I

Stat 427/527: Advanced Data Analysis I Stat 427/527: Advanced Data Analysis I Review of Chapters 1-4 Sep, 2017 1 / 18 Concepts you need to know/interpret Numerical summaries: measures of center (mean, median, mode) measures of spread (sample

More information

Categorical Data Analysis. The data are often just counts of how many things each category has.

Categorical Data Analysis. The data are often just counts of how many things each category has. Categorical Data Analysis So far we ve been looking at continuous data arranged into one or two groups, where each group has more than one observation. E.g., a series of measurements on one or two things.

More information

AMS7: WEEK 7. CLASS 1. More on Hypothesis Testing Monday May 11th, 2015

AMS7: WEEK 7. CLASS 1. More on Hypothesis Testing Monday May 11th, 2015 AMS7: WEEK 7. CLASS 1 More on Hypothesis Testing Monday May 11th, 2015 Testing a Claim about a Standard Deviation or a Variance We want to test claims about or 2 Example: Newborn babies from mothers taking

More information

As an example, consider the Bond Strength data in Table 2.1, atop page 26 of y1 y 1j/ n , S 1 (y1j y 1) 0.

As an example, consider the Bond Strength data in Table 2.1, atop page 26 of y1 y 1j/ n , S 1 (y1j y 1) 0. INSY 7300 6 F01 Reference: Chapter of Montgomery s 8 th Edition Point Estimation As an example, consider the Bond Strength data in Table.1, atop page 6 of By S. Maghsoodloo Montgomery s 8 th edition, on

More information

Chi square test of independence

Chi square test of independence Chi square test of independence We can eyeball differences between percentages to determine whether they seem large enough to be important Better: Are differences in percentages statistically significant?

More information

Lecture 2. Estimating Single Population Parameters 8-1

Lecture 2. Estimating Single Population Parameters 8-1 Lecture 2 Estimating Single Population Parameters 8-1 8.1 Point and Confidence Interval Estimates for a Population Mean Point Estimate A single statistic, determined from a sample, that is used to estimate

More information

CHAPTER SIX Statistical Estimation

CHAPTER SIX Statistical Estimation 75 CHAPTER SIX Statistical Estimation 6. Point Estimation The following table contains some of the well known population parameters and their point estimates based on a random sample. Table Population

More information

Stat 412/512 REVIEW OF SIMPLE LINEAR REGRESSION. Jan Charlotte Wickham. stat512.cwick.co.nz

Stat 412/512 REVIEW OF SIMPLE LINEAR REGRESSION. Jan Charlotte Wickham. stat512.cwick.co.nz Stat 412/512 REVIEW OF SIMPLE LINEAR REGRESSION Jan 7 2015 Charlotte Wickham stat512.cwick.co.nz Announcements TA's Katie 2pm lab Ben 5pm lab Joe noon & 1pm lab TA office hours Kidder M111 Katie Tues 2-3pm

More information

Unit 10: Simple Linear Regression and Correlation

Unit 10: Simple Linear Regression and Correlation Unit 10: Simple Linear Regression and Correlation Statistics 571: Statistical Methods Ramón V. León 6/28/2004 Unit 10 - Stat 571 - Ramón V. León 1 Introductory Remarks Regression analysis is a method for

More information

" M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2

 M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2 Notation and Equations for Final Exam Symbol Definition X The variable we measure in a scientific study n The size of the sample N The size of the population M The mean of the sample µ The mean of the

More information

Chapter 1. Hypothesis Testing. 1.1 Z - Test for µ. Procedure. State the hypothesis. n(x µ) (test statistic) Step 2 Find Z = X µ.

Chapter 1. Hypothesis Testing. 1.1 Z - Test for µ. Procedure. State the hypothesis. n(x µ) (test statistic) Step 2 Find Z = X µ. Chapter 1 Hypothesis Testing 1.1 Z - Test for µ Procedure Step 1 State the hypothesis H 0 : µ = µ 0 H a : µ < µ 0, µ > µ 0, µ µ 0, Step 2 Find Z = X µ σ/ n or Z = n(x µ) σ (test statistic) Step 3 Find

More information

hypotheses. P-value Test for a 2 Sample z-test (Large Independent Samples) n > 30 P-value Test for a 2 Sample t-test (Small Samples) n < 30 Identify α

hypotheses. P-value Test for a 2 Sample z-test (Large Independent Samples) n > 30 P-value Test for a 2 Sample t-test (Small Samples) n < 30 Identify α Chapter 8 Notes Section 8-1 Independent and Dependent Samples Independent samples have no relation to each other. An example would be comparing the costs of vacationing in Florida to the cost of vacationing

More information

Chapter 9 Inferences from Two Samples

Chapter 9 Inferences from Two Samples Chapter 9 Inferences from Two Samples 9-1 Review and Preview 9-2 Two Proportions 9-3 Two Means: Independent Samples 9-4 Two Dependent Samples (Matched Pairs) 9-5 Two Variances or Standard Deviations Review

More information

CIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8

CIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8 CIVL - 7904/8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8 Chi-square Test How to determine the interval from a continuous distribution I = Range 1 + 3.322(logN) I-> Range of the class interval

More information

Probability and Statistics

Probability and Statistics The big picture Probability and Statistics Sample Population 1) Data Collection Data ) Explanatory Data Analysis (EDA) Inference on Relationship Between two Variables 4) Inference 3) Probability The Big

More information

Slides by. John Loucks. St. Edward s University. Slide South-Western, a part of Cengage Learning

Slides by. John Loucks. St. Edward s University. Slide South-Western, a part of Cengage Learning Slides by John Loucks St. Edward s University Slide 1 Chapter 10 Comparisons Involving Means Part A Inferences About the Difference Between Two Population Means: s 1 and s 2 Known Inferences About the

More information

Inference for the mean of a population. Testing hypotheses about a single mean (the one sample t-test). The sign test for matched pairs

Inference for the mean of a population. Testing hypotheses about a single mean (the one sample t-test). The sign test for matched pairs Stat 528 (Autumn 2008) Inference for the mean of a population (One sample t procedures) Reading: Section 7.1. Inference for the mean of a population. The t distribution for a normal population. Small sample

More information

Other Continuous Probability Distributions

Other Continuous Probability Distributions CHAPTER Probability, Statistics, and Reliability for Engineers and Scientists Second Edition PROBABILITY DISTRIBUTION FOR CONTINUOUS RANDOM VARIABLES A. J. Clar School of Engineering Department of Civil

More information

Hypothesis Testing. Introduction to Data Science Algorithms Jordan Boyd-Graber and Michael Paul OCTOBER 11, 2016

Hypothesis Testing. Introduction to Data Science Algorithms Jordan Boyd-Graber and Michael Paul OCTOBER 11, 2016 Hypothesis Testing Introduction to Data Science Algorithms Jordan Boyd-Graber and Michael Paul OCTOBER 11, 2016 Introduction to Data Science Algorithms Boyd-Graber and Paul Hypothesis Testing 1 of 12 χ

More information

Chapter 6. Estimates and Sample Sizes

Chapter 6. Estimates and Sample Sizes Chapter 6 Estimates and Sample Sizes Lesson 6-1/6-, Part 1 Estimating a Population Proportion This chapter begins the beginning of inferential statistics. There are two major applications of inferential

More information

DATA IN SERIES AND TIME I. Several different techniques depending on data and what one wants to do

DATA IN SERIES AND TIME I. Several different techniques depending on data and what one wants to do DATA IN SERIES AND TIME I Several different techniques depending on data and what one wants to do Data can be a series of events scaled to time or not scaled to time (scaled to space or just occurrence)

More information

Example. χ 2 = Continued on the next page. All cells

Example. χ 2 = Continued on the next page. All cells Section 11.1 Chi Square Statistic k Categories 1 st 2 nd 3 rd k th Total Observed Frequencies O 1 O 2 O 3 O k n Expected Frequencies E 1 E 2 E 3 E k n O 1 + O 2 + O 3 + + O k = n E 1 + E 2 + E 3 + + E

More information

Linear Models and Estimation by Least Squares

Linear Models and Estimation by Least Squares Linear Models and Estimation by Least Squares Jin-Lung Lin 1 Introduction Causal relation investigation lies in the heart of economics. Effect (Dependent variable) cause (Independent variable) Example:

More information

Statistics 251: Statistical Methods

Statistics 251: Statistical Methods Statistics 251: Statistical Methods 1-sample Hypothesis Tests Module 9 2018 Introduction We have learned about estimating parameters by point estimation and interval estimation (specifically confidence

More information

10: Crosstabs & Independent Proportions

10: Crosstabs & Independent Proportions 10: Crosstabs & Independent Proportions p. 10.1 P Background < Two independent groups < Binary outcome < Compare binomial proportions P Illustrative example ( oswege.sav ) < Food poisoning following church

More information

Chi Square Analysis M&M Statistics. Name Period Date

Chi Square Analysis M&M Statistics. Name Period Date Chi Square Analysis M&M Statistics Name Period Date Have you ever wondered why the package of M&Ms you just bought never seems to have enough of your favorite color? Or, why is it that you always seem

More information

Statistical Analysis for QBIC Genetics Adapted by Ellen G. Dow 2017

Statistical Analysis for QBIC Genetics Adapted by Ellen G. Dow 2017 Statistical Analysis for QBIC Genetics Adapted by Ellen G. Dow 2017 I. χ 2 or chi-square test Objectives: Compare how close an experimentally derived value agrees with an expected value. One method to

More information

Chapter 10. Chapter 10. Multinomial Experiments and. Multinomial Experiments and Contingency Tables. Contingency Tables.

Chapter 10. Chapter 10. Multinomial Experiments and. Multinomial Experiments and Contingency Tables. Contingency Tables. Chapter 10 Multinomial Experiments and Contingency Tables 1 Chapter 10 Multinomial Experiments and Contingency Tables 10-1 1 Overview 10-2 2 Multinomial Experiments: of-fitfit 10-3 3 Contingency Tables:

More information

Chapter 5: HYPOTHESIS TESTING

Chapter 5: HYPOTHESIS TESTING MATH411: Applied Statistics Dr. YU, Chi Wai Chapter 5: HYPOTHESIS TESTING 1 WHAT IS HYPOTHESIS TESTING? As its name indicates, it is about a test of hypothesis. To be more precise, we would first translate

More information

Stat 704 Data Analysis I Probability Review

Stat 704 Data Analysis I Probability Review 1 / 39 Stat 704 Data Analysis I Probability Review Dr. Yen-Yi Ho Department of Statistics, University of South Carolina A.3 Random Variables 2 / 39 def n: A random variable is defined as a function that

More information

STAT Chapter 8: Hypothesis Tests

STAT Chapter 8: Hypothesis Tests STAT 515 -- Chapter 8: Hypothesis Tests CIs are possibly the most useful forms of inference because they give a range of reasonable values for a parameter. But sometimes we want to know whether one particular

More information

LECTURE 12 CONFIDENCE INTERVAL AND HYPOTHESIS TESTING

LECTURE 12 CONFIDENCE INTERVAL AND HYPOTHESIS TESTING LECTURE 1 CONFIDENCE INTERVAL AND HYPOTHESIS TESTING INTERVAL ESTIMATION Point estimation of : The inference is a guess of a single value as the value of. No accuracy associated with it. Interval estimation

More information

MATH 728 Homework 3. Oleksandr Pavlenko

MATH 728 Homework 3. Oleksandr Pavlenko MATH 78 Homewor 3 Olesandr Pavleno 4.5.8 Let us say the life of a tire in miles, say X, is normally distributed with mean θ and standard deviation 5000. Past experience indicates that θ = 30000. The manufacturer

More information

Lecture 45 Sections Wed, Nov 19, 2008

Lecture 45 Sections Wed, Nov 19, 2008 The Lecture 45 Sections 14.5 Hampden-Sydney College Wed, Nov 19, 2008 Outline The 1 2 3 The 4 5 The Exercise 14.20, page 949. A certain job in a car assembly plant involves a great deal of stress. A study

More information

1)I have 4 red pens, 3 purple pens and 1 green pen that I use for grading. If I randomly choose a pen,

1)I have 4 red pens, 3 purple pens and 1 green pen that I use for grading. If I randomly choose a pen, Introduction to Statistics 252 1I have 4 red pens, 3 purple pens and 1 green pen that I use for grading. If I randomly choose a pen, a what is the probability that I choose a red pen? 4 8 1 2 b what is

More information

Psychology 282 Lecture #4 Outline Inferences in SLR

Psychology 282 Lecture #4 Outline Inferences in SLR Psychology 282 Lecture #4 Outline Inferences in SLR Assumptions To this point we have not had to make any distributional assumptions. Principle of least squares requires no assumptions. Can use correlations

More information

Inference for Regression Inference about the Regression Model and Using the Regression Line

Inference for Regression Inference about the Regression Model and Using the Regression Line Inference for Regression Inference about the Regression Model and Using the Regression Line PBS Chapter 10.1 and 10.2 2009 W.H. Freeman and Company Objectives (PBS Chapter 10.1 and 10.2) Inference about

More information

Theoretical Probability Models

Theoretical Probability Models CHAPTER Duxbury Thomson Learning Maing Hard Decision Third Edition Theoretical Probability Models A. J. Clar School of Engineering Department of Civil and Environmental Engineering 9 FALL 003 By Dr. Ibrahim.

More information

Inferential statistics

Inferential statistics Inferential statistics Inference involves making a Generalization about a larger group of individuals on the basis of a subset or sample. Ahmed-Refat-ZU Null and alternative hypotheses In hypotheses testing,

More information

CHAPTER 9, 10. Similar to a courtroom trial. In trying a person for a crime, the jury needs to decide between one of two possibilities:

CHAPTER 9, 10. Similar to a courtroom trial. In trying a person for a crime, the jury needs to decide between one of two possibilities: CHAPTER 9, 10 Hypothesis Testing Similar to a courtroom trial. In trying a person for a crime, the jury needs to decide between one of two possibilities: The person is guilty. The person is innocent. To

More information

Lecture 26: Chapter 10, Section 2 Inference for Quantitative Variable Confidence Interval with t

Lecture 26: Chapter 10, Section 2 Inference for Quantitative Variable Confidence Interval with t Lecture 26: Chapter 10, Section 2 Inference for Quantitative Variable Confidence Interval with t t Confidence Interval for Population Mean Comparing z and t Confidence Intervals When neither z nor t Applies

More information

INTERVAL ESTIMATION AND HYPOTHESES TESTING

INTERVAL ESTIMATION AND HYPOTHESES TESTING INTERVAL ESTIMATION AND HYPOTHESES TESTING 1. IDEA An interval rather than a point estimate is often of interest. Confidence intervals are thus important in empirical work. To construct interval estimates,

More information

Difference between means - t-test /25

Difference between means - t-test /25 Difference between means - t-test 1 Discussion Question p492 Ex 9-4 p492 1-3, 6-8, 12 Assume all variances are not equal. Ignore the test for variance. 2 Students will perform hypothesis tests for two

More information

The Purpose of Hypothesis Testing

The Purpose of Hypothesis Testing Section 8 1A:! An Introduction to Hypothesis Testing The Purpose of Hypothesis Testing See s Candy states that a box of it s candy weighs 16 oz. They do not mean that every single box weights exactly 16

More information

Inference About Means and Proportions with Two Populations

Inference About Means and Proportions with Two Populations Inference About Means and Proportions with Two Populations Content Inferences About the Difference Between Two Population Means: 1 and Known Inferences About the Difference Between Two Population Means:

More information

1 MA421 Introduction. Ashis Gangopadhyay. Department of Mathematics and Statistics. Boston University. c Ashis Gangopadhyay

1 MA421 Introduction. Ashis Gangopadhyay. Department of Mathematics and Statistics. Boston University. c Ashis Gangopadhyay 1 MA421 Introduction Ashis Gangopadhyay Department of Mathematics and Statistics Boston University c Ashis Gangopadhyay 1.1 Introduction 1.1.1 Some key statistical concepts 1. Statistics: Art of data analysis,

More information

Additional Notes: Investigating a Random Slope. When we have fixed level-1 predictors at level 2 we show them like this:

Additional Notes: Investigating a Random Slope. When we have fixed level-1 predictors at level 2 we show them like this: Ron Heck, Summer 01 Seminars 1 Multilevel Regression Models and Their Applications Seminar Additional Notes: Investigating a Random Slope We can begin with Model 3 and add a Random slope parameter. If

More information

Midterm 2 - Solutions

Midterm 2 - Solutions Ecn 102 - Analysis of Economic Data University of California - Davis February 24, 2010 Instructor: John Parman Midterm 2 - Solutions You have until 10:20am to complete this exam. Please remember to put

More information