2011.5.22 1 Medical Statistics Medical Statistics Tao Yuchun Tao Yuchun Practice 2.

Slides:



Advertisements
Similar presentations
Lecture (11,12) Parameter Estimation of PDF and Fitting a Distribution Function.
Advertisements

Inference Sampling distributions Hypothesis testing.
Inferential Statistics & Hypothesis Testing
AP Statistics – Chapter 9 Test Review
Confidence Interval and Hypothesis Testing for:
The Normal Distribution. n = 20,290  =  = Population.
9-1 Hypothesis Testing Statistical Hypotheses Statistical hypothesis testing and confidence interval estimation of parameters are the fundamental.
Chapter 6 Hypotheses texts. Central Limit Theorem Hypotheses and statistics are dependent upon this theorem.
Final Jeopardy $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 LosingConfidenceLosingConfidenceTesting.
Hypothesis : Statement about a parameter Hypothesis testing : decision making procedure about the hypothesis Null hypothesis : the main hypothesis H 0.
Statistics 101 Class 9. Overview Last class Last class Our FAVORATE 3 distributions Our FAVORATE 3 distributions The one sample Z-test The one sample.
Chapter 11: Inference for Distributions
Chapter 9 Hypothesis Testing.
Hypothesis Testing Using The One-Sample t-Test
Quantitative Business Methods for Decision Making Estimation and Testing of Hypotheses.
Chapter 9 Hypothesis Testing II. Chapter Outline  Introduction  Hypothesis Testing with Sample Means (Large Samples)  Hypothesis Testing with Sample.
The t-test Inferences about Population Means when population SD is unknown.
Statistical hypothesis testing – Inferential statistics I.
Chapter 9 Title and Outline 1 9 Tests of Hypotheses for a Single Sample 9-1 Hypothesis Testing Statistical Hypotheses Tests of Statistical.
AM Recitation 2/10/11.
Warm-up Day of 8.1 and 8.2 Quiz and Types of Errors Notes.
Chapter 13 – 1 Chapter 12: Testing Hypotheses Overview Research and null hypotheses One and two-tailed tests Errors Testing the difference between two.
1/2555 สมศักดิ์ ศิวดำรงพงศ์
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 11 Section 2 – Slide 1 of 25 Chapter 11 Section 2 Inference about Two Means: Independent.
Lesson 11 - R Review of Testing a Claim. Objectives Explain the logic of significance testing. List and explain the differences between a null hypothesis.
More About Significance Tests
June 18, 2008Stat Lecture 11 - Confidence Intervals 1 Introduction to Inference Sampling Distributions, Confidence Intervals and Hypothesis Testing.
+ Chapter 9 Summary. + Section 9.1 Significance Tests: The Basics After this section, you should be able to… STATE correct hypotheses for a significance.
1 Power and Sample Size in Testing One Mean. 2 Type I & Type II Error Type I Error: reject the null hypothesis when it is true. The probability of a Type.
Chapter 9 Hypothesis Testing II: two samples Test of significance for sample means (large samples) The difference between “statistical significance” and.
9-1 Hypothesis Testing Statistical Hypotheses Definition Statistical hypothesis testing and confidence interval estimation of parameters are.
Statistical Decision Making. Almost all problems in statistics can be formulated as a problem of making a decision. That is given some data observed from.
Maximum Likelihood Estimator of Proportion Let {s 1,s 2,…,s n } be a set of independent outcomes from a Bernoulli experiment with unknown probability.
Practice 1 Tao Yuchun Medical Statistics
Agresti/Franklin Statistics, 1 of 122 Chapter 8 Statistical inference: Significance Tests About Hypotheses Learn …. To use an inferential method called.
STT 315 Ashwini Maurya Acknowledgement: Author is indebted to Dr. Ashok Sinha, Dr. Jennifer Kaplan and Dr. Parthanil Roy for allowing him to use/edit many.
Statistics - methodology for collecting, analyzing, interpreting and drawing conclusions from collected data Anastasia Kadina GM presentation 6/15/2015.
Chapter 23 Inference for One- Sample Means. Steps for doing a confidence interval: 1)State the parameter 2)Conditions 1) The sample should be chosen randomly.
1 ConceptsDescriptionHypothesis TheoryLawsModel organizesurprise validate formalize The Scientific Method.
EMIS 7300 SYSTEMS ANALYSIS METHODS FALL 2005 Dr. John Lipp Copyright © Dr. John Lipp.
Introduction to Inferece BPS chapter 14 © 2010 W.H. Freeman and Company.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 8 Hypothesis Testing.
1 9 Tests of Hypotheses for a Single Sample. © John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. 9-1.
Chapter 8 Parameter Estimates and Hypothesis Testing.
MeanVariance Sample Population Size n N IME 301. b = is a random value = is probability means For example: IME 301 Also: For example means Then from standard.
Chapter 9: Testing Hypotheses Overview Research and null hypotheses One and two-tailed tests Type I and II Errors Testing the difference between two means.
Inen 460 Lecture 2. Estimation (ch. 6,7) and Hypothesis Testing (ch.8) Two Important Aspects of Statistical Inference Point Estimation – Estimate an unknown.
Mystery 1Mystery 2Mystery 3.
AP Statistics Unit 5 Addie Lunn, Taylor Lyon, Caroline Resetar.
Medical Statistics Medical Statistics Tao Yuchun Tao Yuchun 4
Medical Statistics (full English class) Ji-Qian Fang School of Public Health Sun Yat-Sen University.
© Copyright McGraw-Hill 2004
WS 2007/08Prof. Dr. J. Schütze, FB GW KI 1 Hypothesis testing Statistical Tests Sometimes you have to make a decision about a characteristic of a population.
- We have samples for each of two conditions. We provide an answer for “Are the two sample means significantly different from each other, or could both.
Medical Statistics Medical Statistics Tao Yuchun Tao Yuchun 5
Medical Statistics Medical Statistics Tao Yuchun Tao Yuchun Practice 3
Statistical Inference Statistical inference is concerned with the use of sample data to make inferences about unknown population parameters. For example,
Inference ConceptsSlide #1 1-sample Z-test H o :  =  o (where  o = specific value) Statistic: Test Statistic: Assume: –  is known – n is “large” (so.
Medical Statistics Medical Statistics Tao Yuchun Tao Yuchun 7
Medical Statistics Medical Statistics Tao Yuchun Tao Yuchun 9
Tests of Significance: The Basics ESS chapter 15 © 2013 W.H. Freeman and Company.
Inference About Means Chapter 23. Getting Started Now that we know how to create confidence intervals and test hypotheses about proportions, it’d be nice.
Chapter 1 Introduction to Statistics. Section 1.1 Fundamental Statistical Concepts.
Medical Statistics Medical Statistics Tao Yuchun Tao Yuchun 6
 List the characteristics of the F distribution.  Conduct a test of hypothesis to determine whether the variances of two populations are equal.  Discuss.
16/23/2016Inference about µ1 Chapter 17 Inference about a Population Mean.
Chapter 9 Hypothesis Testing.
8-1 of 23.
Statistical inference
Statistical inference
Presentation transcript:

Medical Statistics Medical Statistics Tao Yuchun Tao Yuchun Practice 2

Review centrosymmetric μ — position parameter I. Normal Distribution character The center is μ. Determine location of the peak (center) for a normal distribution.

σ — shape parameter Determine shape of a normal curve. A normal distribution with μ and σ denoted with The area under the curve The area under the curve is probability. The area of certain range under the curve is: μ±1.96σ 95% μ±2.58σ 99% 

Standard Normal Distribution A normal distribution with μ=0 and σ=1 denoted with. II. Reference Range definition steps of establishment In health-related fields, a reference range (or reference values or interval) is a set of values of some measurement that a physician or other health professional can use to interpret a set of results for a particular patient. It is determined by collecting data from vast numbers of laboratory tests From Wikipedia, the free encyclopedia

Statistical methods (1) For normal distributed data -- normal Two sides (1-α)range: One side (1-α)range: or Two sides: One side: 

(2) For skew distributed data -- percentiles Two sides (1-0.05)range: One side (1-0.05)range: or Two sides or one side according to professional knowledge.

III. Estimation of population parameter Sampling error is related to the variation of the population. Sampling error is also related to sample size The sampling error and standard error of mean Sampling errorSampling error The distribution of sample mean Central Limit Theorem

Standard error S is estimation of σ, is estimation of.  t distribution Standard error of mean

character (1) (1) centrosymmetric (2) (2) ν — shape parameter The center is 0. Determine shape of a t curve. When ν is increasing, t curve is close to standard normal curve.  (3) (3) the area under t curve — t Table α is probability, ν is degree of freedom, ν = n-1.

Confidence Interval of Population Mean Statistical inference Estimation parameter Hypothesis testing point estimation interval estimation  Point estimation of population mean -- sample mean  Interval estimation of population mean -- (1-α) confidence interval  Confidence level: 1-α, such as 95% or 99%.

(1-α) confidence interval of population meanThe formula of (1-α) confidence interval of population mean for two sides is:  came from the Table of t distribution. Reference Range Distinguish Reference Range from Confidence Interval Standard Deviation Distinguish Standard Deviation from Standard Error Standard Error You can see this table  

IV. Hypothesis testing IV. Hypothesis testing The idea and steps of Hypothesis testing (1) (1) The idea Null hypothesis Alternative hypothesis Null hypothesis and Alternative hypothesis P-value α () P-value and α (level of a test) A small-probability event A small-probability event One-sided test Two-sided test One-sided test or Two-sided test Test statistic Distribution Test statistic and its Distribution

A.Set hypotheses and the level of test B.Select an appropriate test and calculate the test statistic the test statistic C.Determine P-value, and make decision (2) (2) The steps If P ≤α , then reject H 0 at significance level If P ≤α , then reject H 0 at significance level α=0.05. α=0.05. If P > α , then accept H 0 at significance level If P > α , then accept H 0 at significance level α=0.05. α=0.05. 

t tests (part) (1) (1) Comparing to a given population mean ( One-sample t test) H 0 : μ = μ 0 H 1 : μ ≠ μ 0 α= 0.05 When t ≥ t α,ν, then P ≤ α, reject H 0 ; When t < t α,ν, then P > α, accept H 0. t α,ν came from the Table of t distribution.

Excel’s statistical method You can use Excel’s statistical function TINV(Probability,Deg_freedom) TINV(Probability,Deg_freedom) to get t α,ν, here Probability is α , Deg_freedom is ν (degree of freedom). It is for two sides. You can use the t-test method of the macro of statistical analysis tools. See the example updated [ stat1(English).xls updated ]

Practice in class Exercise 1 Exercise 1: the blood-glucose(mmol/L) values from 12 randomly selected patients. 5.31, 6.12, 6.53, 6.53, 6.65, 6.66, 6.71, 6.93, 7.05, 7.15, 7.21, 7.35 Please to infer the population mean of the patients is whether greater than or equal the standard value 6.1?

Exercise 2 Exercise 2: the frequency table of latent period (day) from 110 certain infectious disease patients. Please estimate the 95% Reference Range.

Answer Answer See the Excel file ( practice2key.xls ) Exercise 3 Exercise 3: the RBC of 144 healthy male adults, got sample mean and SD: mean= × / L, SD = 0.44 × / L. Please estimate the 95% Reference Range and 95% CI.

Homework The content (mg/L) of CaCo 3 within a material was independently measured 15 times, resulting in: 20.99, 20.41, 20.62, 20.75, 20.10, 20.00, 20.80, 20.91, 22.60, 22.30, 20.99, 20.41, 20.50, 23.00, Please check whether the true value was 20.7mg/L ?

C The temperature (℃ ) for 102 female students from certain college resulting in: sample mean= ℃, SD= ℃ Please estimate the 95% Reference Range and 99% CI?