Satterthwaite’s Approximation for Degrees of Freedom Art Instruction Effect on Reading Development J.C. Mills (1973), “The Effect of Art Instruction Upon.

Slides:



Advertisements
Similar presentations
Distributions of sampling statistics Chapter 6 Sample mean & sample variance.
Advertisements

Hypothesis testing Another judgment method of sampling data.
Psy302 Quantitative Methods
Lesson #23 Analysis of Variance. In Analysis of Variance (ANOVA), we have: H 0 :  1 =  2 =  3 = … =  k H 1 : at least one  i does not equal the others.
Hypothesis testing applied to means. Characteristics of the Sampling Distribution of the mean The sampling distribution of means will have the same mean.
BHS Methods in Behavioral Sciences I
IEEM 3201 Two-Sample Estimation: Paired Observation, Difference.
Testing for differences between 2 means Does the mean weight of cats in Toledo differ from the mean weight of cats in Cleveland? Do the mean quiz scores.
Hypothesis testing for the mean [A] One population that follows a normal distribution H 0 :  =  0 vs H 1 :    0 Suppose that we collect independent.
1 Confidence Intervals for Means. 2 When the sample size n< 30 case1-1. the underlying distribution is normal with known variance case1-2. the underlying.
Hypothesis Testing :The Difference between two population mean :
Measures of Variability: Range, Variance, and Standard Deviation
1/49 EF 507 QUANTITATIVE METHODS FOR ECONOMICS AND FINANCE FALL 2008 Chapter 9 Estimation: Additional Topics.
Quiz 2 Measures of central tendency Measures of variability.
T-test Mechanics. Z-score If we know the population mean and standard deviation, for any value of X we can compute a z-score Z-score tells us how far.
Aim: How do we test a comparison group? Exam Tomorrow.
Important facts. Review Reading pages: P330-P337 (6 th ), or P (7 th )
Lesson Confidence Intervals about a Population Standard Deviation.
Statistics for Engineer Week II and Week III: Random Variables and Probability Distribution.
Inference for distributions: - Comparing two means IPS chapter 7.2 © 2006 W.H. Freeman and Company.
Chapter 9 Section 2 Testing the Difference Between Two Means: t Test 1.
© 2002 Thomson / South-Western Slide 10-1 Chapter 10 Hypothesis Testing with Two Samples.
B AD 6243: Applied Univariate Statistics Hypothesis Testing and the T-test Professor Laku Chidambaram Price College of Business University of Oklahoma.
H1H1 H1H1 HoHo Z = 0 Two Tailed test. Z score where 2.5% of the distribution lies in the tail: Z = Critical value for a two tailed test.
AP STATISTICS LESSON 11 – 2 (DAY 2) More Accurate Levels in The t Procedures.
Last Time Today we will learn how to do t-tests using excel, and we will review for Monday’s exam.
Comparing Three or More Means ANOVA (One-Way Analysis of Variance)
Slide Slide 1 Section 8-6 Testing a Claim About a Standard Deviation or Variance.
July, 2000Guang Jin Statistics in Applied Science and Technology Chapter 7 - Sampling Distribution of Means.
CHAPTER 9 Estimation from Sample Data
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics S eventh Edition By Brase and Brase Prepared by: Lynn Smith.
Factorial Analysis of Variance
ANOVA Assumptions 1.Normality (sampling distribution of the mean) 2.Homogeneity of Variance 3.Independence of Observations - reason for random assignment.
ANOVA ANOVA is used when more than two groups are compared In order to conduct an ANOVA, several assumptions must be made – The population from which the.
STAT 401 EXPERIMENTAL DESIGN AND ANALYSIS Assist.Prof.Dr. R. Serkan Albayrak Department of Business Administration Yaşar University.
Comparison of 2 Population Means Goal: To compare 2 populations/treatments wrt a numeric outcome Sampling Design: Independent Samples (Parallel Groups)
Math 4030 – 9b Comparing Two Means 1 Dependent and independent samples Comparing two means.
Ch8.2 Ch8.2 Population Mean Test Case I: A Normal Population With Known Null hypothesis: Test statistic value: Alternative Hypothesis Rejection Region.
ANOVA = Analysis of Variance
Confidence Intervals for a Population Mean, Standard Deviation Unknown.
CHAPTER 10 ANOVA - One way ANOVa.
Aim: How do we test the difference between two means? HW#14: complete slide.
Chapter 9 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypothesis.
Applied Epidemiologic Analysis - P8400 Fall 2002 Lab 3 Type I, II Error, Sample Size, and Power Henian Chen, M.D., Ph.D.
ENGR 610 Applied Statistics Fall Week 7 Marshall University CITE Jack Smith.
BHS Methods in Behavioral Sciences I May 9, 2003 Chapter 6 and 7 (Ray) Control: The Keystone of the Experimental Method.
95% CI and Width for Mean # of hrs watching TV for Three Different Sample Sizes Sample SizeConfidence Interval Interval WidthStand DevStand Error N=
Lecture 22 Dustin Lueker.  Similar to testing one proportion  Hypotheses are set up like two sample mean test ◦ H 0 :p 1 -p 2 =0  Same as H 0 : p 1.
Student ’ s t-distribution. In cases where the population variance σ 2 is unknown we can use the sample variance S 2 as the best point estimate for the.
Ch5.4 Central Limit Theorem
STA 291 Spring 2010 Lecture 21 Dustin Lueker.
Math 4030 – 10a Tests for Population Mean(s)
Psychology 202a Advanced Psychological Statistics
Hypothesis tests Single sample Z
Two Sample Tests When do use independent
Repeated Measures ANOVA
Repeated Measures ANOVA
Levene's Test for Equality of Variances
The t distribution and the independent sample t-test
Comparing 2 Groups Most Research is Interested in Comparing 2 (or more) Groups (Populations, Treatments, Conditions) Longitudinal: Same subjects at different.
Year-3 The standard deviation plus or minus 3 for 99.2% for year three will cover a standard deviation from to To calculate the normal.
Statistical Process Control
Summary of Tests Confidence Limits
Chapter 13: Inferences about Comparing Two Populations Lecture 7a
(This presentation may be used for instructional purposes)
Introduction to the t Test
Testing a Claim About a Standard Deviation or Variance
STA 291 Spring 2008 Lecture 22 Dustin Lueker.
STA 291 Summer 2008 Lecture 21 Dustin Lueker.
STA 291 Spring 2008 Lecture 21 Dustin Lueker.
Presentation transcript:

Satterthwaite’s Approximation for Degrees of Freedom Art Instruction Effect on Reading Development J.C. Mills (1973), “The Effect of Art Instruction Upon a Reading Development Test: An Experimental Study with Rural Appalachian Children,” Studies in Art Education, Vol. 14, #3, pp.4-8

Setting Comparing Means from 2 Normal Distributions Small Samples (Computer Packages Solve for any sizes) Distributions have Possibly Different Variances

Case 1 – Variances are Equal

Case 2 – Variances are Unequal - I

Case 2 – Variances are Unequal - II

Example – Art Instruction Effect on Reading Experiment to Determine Effect of Art Instruction on a Reading Development Test  52 Children Given Baseline Reading Test  26 Received Art Instruction (Trt), 26 Did not (Control)  Y=Post-Test – Pre-Test Score