Apr. 26 Statistic for the day: Chance that a college student expects to be a millionaire by the age of 40: 1 in 2 Assignment: Begin to review for final.

Slides:



Advertisements
Similar presentations
Chapter 15 ANOVA.
Advertisements

Apr. 23 Statistic for the day: Chance that a woman first elected to the U.S. house or senate before 1993 was a congressional widow: 1 in 4 Assignment:
Hypothesis Testing and Comparing Two Proportions Hypothesis Testing: Deciding whether your data shows a “real” effect, or could have happened by chance.
Hypothesis Testing: Intervals and Tests
Hypothesis Testing making decisions using sample data.
Section 1.3 Experimental Design © 2012 Pearson Education, Inc. All rights reserved. 1 of 61.
Section 1.3 Experimental Design.
Decision Errors and Power
Statistic for the day: Portion of all international arms sales since 1980 that went to the middle East: 2 out of 5 Assignment: Read Chapter 12 pp
Chapter 6: Correlational Research Examine whether variables are related to one another (whether they vary together). Correlation coefficient: statistic.
Analysis of frequency counts with Chi square
Copyright ©2011 Brooks/Cole, Cengage Learning More about Inference for Categorical Variables Chapter 15 1.
Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. More About Categorical Variables Chapter 15.
Section 4.2: How to Look for Differences. Cross-Tabulations College student binge drinkers experienced many personal and social problems, the researchers.
Chi-square Test of Independence
Fall 2006 – Fundamentals of Business Statistics 1 Chapter 8 Introduction to Hypothesis Testing.
Section 4.4 Creating Randomization Distributions.
Research Methods Steps in Psychological Research Experimental Design
Chi-Square and Analysis of Variance (ANOVA)
Presentation 12 Chi-Square test.
Chapter 12 ANOVA.
Psy B07 Chapter 1Slide 1 ANALYSIS OF VARIANCE. Psy B07 Chapter 1Slide 2 t-test refresher  In chapter 7 we talked about analyses that could be conducted.
Chapter 10 Hypothesis Testing
Fundamentals of Hypothesis Testing: One-Sample Tests
Chapter 9 Hypothesis Testing: Single Population
Statistics: Unlocking the Power of Data Lock 5 Synthesis STAT 250 Dr. Kari Lock Morgan SECTIONS 4.4, 4.5 Connecting bootstrapping and randomization (4.4)
Apr. 8 Stat 100. To do Read Chapter 21, try problems 1-6 Skim Chapter 22.
Experimental Design 1 Section 1.3. Section 1.3 Objectives 2 Discuss how to design a statistical study Discuss data collection techniques Discuss how to.
Chapter 10 Hypothesis Testing
Chapter 8 Introduction to Hypothesis Testing
Major Types of Quantitative Studies Descriptive research –Correlational research –Evaluative –Meta Analysis Causal-comparative research Experimental Research.
Copyright © Cengage Learning. All rights reserved. 10 Inferences Involving Two Populations.
Copyright © 2015, 2012, and 2009 Pearson Education, Inc. 1 Chapter Introduction to Statistics 1.
Copyright © 2009 Pearson Education, Inc LEARNING GOAL Interpret and carry out hypothesis tests for independence of variables with data organized.
Inference We want to know how often students in a medium-size college go to the mall in a given year. We interview an SRS of n = 10. If we interviewed.
Gathering Useful Data. 2 Principle Idea: The knowledge of how the data were generated is one of the key ingredients for translating data intelligently.
Statistical Power The power of a test is the probability of detecting a difference or relationship if such a difference or relationship really exists.
10.1: Confidence Intervals Falls under the topic of “Inference.” Inference means we are attempting to answer the question, “How good is our answer?” Mathematically:
+ Chi Square Test Homogeneity or Independence( Association)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 8-1 Chapter 8 Fundamentals of Hypothesis Testing: One-Sample Tests Statistics.
Introduction to the Practice of Statistics Fifth Edition Chapter 6: Introduction to Inference Copyright © 2005 by W. H. Freeman and Company David S. Moore.
Chapter 13 - ANOVA. ANOVA Be able to explain in general terms and using an example what a one-way ANOVA is (370). Know the purpose of the one-way ANOVA.
Statistical Significance for a two-way table Inference for a two-way table We often gather data and arrange them in a two-way table to see if two categorical.
AP STATISTICS LESSON 10 – 2 DAY 2 MORE DETAIL: STATING HYPOTHESES.
Foundations of Sociological Inquiry Statistical Analysis.
Lecture: Forensic Evidence and Probability Characteristics of evidence Class characteristics Individual characteristics  features that place the item.
4 normal probability plots at once par(mfrow=c(2,2)) for(i in 1:4) { qqnorm(dataframe[,1] [dataframe[,2]==i],ylab=“Data quantiles”) title(paste(“yourchoice”,i,sep=“”))}
Section 12.2: Tests for Homogeneity and Independence in a Two-Way Table.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 10 Comparing Two Groups Section 10.1 Categorical Response: Comparing Two Proportions.
Section 1.3 Experimental Design.
Statistic for the day: A 1943 copper penny sold in 1996 for $82,500. Assignment: Read Chapter 21 Exercises p : 3, 5, 6, 11, 13 These slides were.
Cross Tabs and Chi-Squared Testing for a Relationship Between Nominal/Ordinal Variables.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 11 Inference for Distributions of Categorical.
Chi-Square Chapter 14. Chi Square Introduction A population can be divided according to gender, age group, type of personality, marital status, religion,
Apr. 28 Statistic for the day: Average number of zeros per in- class quiz in Stat (not counting students who late- dropped): 68 Assignment: Answer.
Section 1.3 Objectives Discuss how to design a statistical study Discuss data collection techniques Discuss how to design an experiment Discuss sampling.
AP Test Practice. A student organization at a university is interested in estimating the proportion of students in favor of showing movies biweekly instead.
Copyright © 2009 Pearson Education, Inc LEARNING GOAL Interpret and carry out hypothesis tests for independence of variables with data organized.
Lecture #8 Thursday, September 15, 2016 Textbook: Section 4.4
Statistics 200 Lecture #9 Tuesday, September 20, 2016
Presentation 12 Chi-Square test.
Assignment: Read Chapter 12 Exercises p : 9, 11, 17, 19, 20
Assignment: Solve practice problems WITHOUT looking at the answers.
Hypothesis testing. Chi-square test
Lecture: Forensic Evidence and Probability Characteristics of evidence
Section 11.1: Significance Tests: Basics
Data Collection and Experimental Design
Assignment: Read Chapter 23 Do exercises 1, 2, 5
Presentation transcript:

Apr. 26 Statistic for the day: Chance that a college student expects to be a millionaire by the age of 40: 1 in 2 Assignment: Begin to review for final exam. These slides were created by Tom Hettmansperger and in some cases modified by David Hunter Source: Harper’s index May 2000

Rows: gender Columns: cell phone no yes All female male All Exercise : Follow the 4 steps and answer the Research Question: Is there a relationship between gender and ownership of cell phones in Stat 100.2? Data

Rows: gender Columns: cell phone no yes All female male All Step 1: Formulate hypotheses Data Null: There is no relationship between gender and cell phone usage. Alternative: There is a relationship.

Rows: gender Columns: cell phone no yes All female 12 | | male 14 | | All Step 2: Calculate test statistic Observed | Expected

Step 3: Find p-value From p. 137, a standardized score of 1.22 has area.11 to the right. This means that the 2-sided p-value is.22. Step 4: Make decision The p-value of.22 means we have no evidence of a difference between men and women in Stat 100 with regard to cell phone usage.

Meta-analysis A collection of statistical techniques for combining studies. A collection of statistical techniques for combining studies. By combining many studies, we may sometimes be able to obtain a large “meta- study” that helps to answer difficult questions that are not clear from smaller studies. By combining many studies, we may sometimes be able to obtain a large “meta- study” that helps to answer difficult questions that are not clear from smaller studies.

Vote-counting method Simply find all studies on a particular topic and count how many had found a statistically significant result. Simply find all studies on a particular topic and count how many had found a statistically significant result. Bad idea unless sample size is also taken into account: Bad idea unless sample size is also taken into account: Imagine taking a single study involving 1000 participants and breaking it up into 100 studies of 10 participants each. Probably, none of the 10-participant studies would amount to anything statistically significant even if the larger study would.

Which studies should be included? Different studies may differ widely in their quality of work. Often, many studies must be eliminated from a meta-analysis because it is not absolutely clear that what is being studied in them is the desired focus of the research. A meta-analysis of the effect of behavior on blood pressure eliminated all but 26 out of 857 possible studies!

Should studies be compared or combined? If one wishes to combine studies, make sure they’re really measuring the same thing on the same population! Consider two studies comparing surgery to relaxation for treating chronic back pain. One is conducted at a back-care specialty clinic, the other at a suburban medical center. Where will the people with the most severe back pain go? The two studies are probably conducted on different populations.

Is smoking related to lower sperm count in men? One study found a 22.8% reduction in sperm count for smokers, but it only used 88 subjects and the finding was not statistically significant. An accompanying meta-analysis estimated a similar reduction, but with the power of the combined studies, the p-value was found to be less than (Remember, these findings are based on observational studies and do not imply causation.)

Are mammograms an effective screening device for women aged 40-49? A 1993 meta-analysis said NO. This raises a potential problem with meta-analyses: The possibility of type 2 errors might be ignored because it seems unlikely that such a large study could miss any significant result! The 1993 meta-analysis did not dissuade the American Cancer Society from recommending mammograms for women The ACS and others have pointed to various potential flaws with the meta-analysis.