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### Day4

Course: STAT 110, Fall 2009
School: Los Angeles Southwest...
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4: Chapter Sample Surveys in the Real World Sampling and statistics seem simple, but some problems can arise. Example: Prediction poll mistake of the 1948 presidential election that proclaimed Thomas Dewey as the winner over Harry Truman. Sampling Errors Sampling Errors are errors caused by the act of selecting a sample. They cause sample results to be different from the results of a census. (50) Improper...

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4: Chapter Sample Surveys in the Real World Sampling and statistics seem simple, but some problems can arise. Example: Prediction poll mistake of the 1948 presidential election that proclaimed Thomas Dewey as the winner over Harry Truman. Sampling Errors Sampling Errors are errors caused by the act of selecting a sample. They cause sample results to be different from the results of a census. (50) Improper sampling techniques used. Undercoverage occurs when some groups in the population are left out of the process of choosing the sample. (51) Random Sampling Error: This results from chance selection in the simple random sample. (50) The error is due to chance. The best we can do to control this is to select a large sample. Margin of error includes only random sampling error. 1 Nonsampling Errors Nonsampling Errors are errors not related to the act of selecting a sample from the population. They can be present even in a census. (50) Nonresponse (Missing Data): Refusal to answer survey, subject is not available for survey Response Errors: Subject may lie or remember incorrectly, subject may not understand question Processing Errors: Math errors, coding data incorrectly Effects of Data Collection Procedure: Wording of the question, Timing How the survey is administered: Mail (low response), telephone, personal interview How to Live with Sampling Errors Substitute other households for nonresponders Can help reduce bias Weight the responses Example: Number of males and females in the sample may differ, weighting can account for differences. Can also account for differences by race, age, household size Helps correct bias Increases variability 2 More Complex Sample Designs Sometimes a strict simple random sample is difficult to obtain. Therefore, we need to find other ways of randomly selecting units for a sample. Multistage Sampling Design: This type of sampling is used to select a sample from a very large population where certain groups and subgroups are available. Example a of Multistage Sample Suppose we wish to obtain a sample of people in the United States. 1) Randomly select a few states (SRS) 2) Randomly select a few counties (SRS) 3) Randomly select a few neighborhoods (SRS) 4) Randomly select people (SRS) 3 More Sampling Designs Cluster sampling: Divide population into clusters. Select one or more cluster and include everyone in sample. Example: SC has 46 counties. Select 5 counties at random, use all household in each selected county as sample. Systematic sampling: Take every nth item from the sampling frame. Example: Take every 10th towel from a roll of paper towels Stratified Random Sampling Design (p. 58) Step 1: Divide the sampling frame into groups of units called strata. The strata are chosen using some characteristic of the units already known (i.e.: race, gender, location, etc.). The strata are chosen to be of special interest in these groups within the population. (Note: strata is plural, stratum is singular.) Step 2: Take a separate simple random sample in each stratum and combine these to make up the stratified random sample. 4 Example: Males and Females The class has been separated into males and females. Ther...

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Los Angeles Southwest College - STAT - 110
Understanding prediction (p. 289) Prediction is based on fitting some model to the data. Prediction works best when the model fits the data closely. Will get better predictions if data have a tight linear relationship compare Figure 15.1 on p. 285
Los Angeles Southwest College - STAT - 110
Chapter 13: Normal DistributionsExploring data for one quantitative variable: Always plot the data: Histogram or stemplot Look for an overall pattern and for striking deviations such as outliers. Describe center and spread with the five-number su
Los Angeles Southwest College - STAT - 110
Los Angeles Southwest College - STAT - 110
Chapter 5: Experiments, Good and Bad Three studies on pp. 71-72. Observational studies are passive data collections. Experiments are active data production. If properly designed, we can observe whether cause and effect relationships are present.
Los Angeles Southwest College - STAT - 110
Chapter 7: Basic Data Ethics (p. 110) Institutional review board: Reviews all planned studies in advance in order to protect subjects from possible harm. All organizations that conduct studies must have such a board. Informed consent: This means t
Los Angeles Southwest College - STAT - 110
Exam 1, Statistics 110 Spring 2003Multiple Choice Circle the correct answer for each question. No partial credit will be given. Each question is worth 1 points. 1. A study of a drug to prevent hair loss showed that 86% of the men who took it mainta
Los Angeles Southwest College - STAT - 110
Chapter 24: Two-Way Tables (to p. 469) A two-way table is a way to display bivariate data when both variables are categorical. This is sometimes called a contingency table. With cross-classified data, one variable is displayed in rows and the othe
Los Angeles Southwest College - STAT - 110
Chapter 6: Experiments in the Real WorldWays to control for bias of people in experiments. Single Blind: An experiment is single blind if the units are unaware of the exact treatment being imposed on them. Controls for subject bias. Double Blind:
Los Angeles Southwest College - STAT - 110
Mean and Standard DeviationAnother type of numerical summary for a data set Mean: The mean of a set of n observations is the arithmetic average; it is the sum of the observations divided by the number of observations, n. (p. 227) Formula: x1 + x2
Los Angeles Southwest College - STAT - 110
Chapter 2: Samples, Good and Bad Biased design: Systematically favors certain outcomes. (p. 20) Sampling designs that are often biased:Convenience sample: Selects whichever individuals are easiest to reach. (p. 20) Example: Interviewing people goi
Los Angeles Southwest College - STAT - 110
Bias and VariabilityMargin of Error Surveys often report a percentage and a &quot;margin of error&quot;. &quot;Margin of error plus or minus three percentage points&quot; means: If we took many samples using the same method we used to get this one sample, 95% of th
Los Angeles Southwest College - STAT - 110
Course Syllabus for STAT 110 Spring 2003Purpose of Course: To provide an integrated introduction to the basic statistical ideas that a person would encounter in everyday life. Instructor: R. Webster West, Leconte 217A, 777-3792 or 777-7800, websterw
Los Angeles Southwest College - STAT - 110
r h ss us k }2}yy@y`s h ss us k 2Q}2 }yy@y`h }2}yy@y`q h ss us k h x ss us k }2wyy@y`k x h ss us k o2wyy@y` g 2btxby4xdbtx4gb4x2&quot;0bg2w024vu rs rxq u ux u id u sl ghs u s bswt24vsyys4x2}@m tx4s4v4xmyIsj&quot; q g u u rk id x u gn
Los Angeles Southwest College - STAT - 110
Independent Events Two outcomes are independent if knowing the outcome of one does not change the probabilities for outcomes of the other. When two events are independent, we find the probability of both events happening by multiply their individua
Los Angeles Southwest College - STAT - 110
Los Angeles Southwest College - STAT - 110
Chapter 15: Describing Relationships: Regression, Prediction, and Causation If we have a strong linear correlation between two variables, then we can use a linear regression model to predict the value of a response variable, y, based on an explanato
Los Angeles Southwest College - STAT - 110
Chapter 12: Describing Distributions with Numbers We create graphs to give us a picture of the data. We also need numbers to summarize the center and spread of a distribution. Two types of descriptive statistics for categorical variables:1) Count
Los Angeles Southwest College - STAT - 110
Inference (Chapter 22) Inference is the term used to describe the process of making decisions based on data. We would like to make decisions about population parameters based on sample statistics. We will discuss making an inference about a popula
Los Angeles Southwest College - K - 12
Title: Is it hot in here? Exploring the greenhouse effect and global climate changeAuthor: Kimberly Schneider, GK-12 Program, Department of Biological Sciences, University of South Carolina Implemented: St. Andrews Middle School, Columbia SCOvervi
Los Angeles Southwest College - K - 12
Who Polluted the Potomac?Introduction:As human populations have increased and land uses have changed, many of our rivers have become polluted. This example demonstrates that, just as we each contribute to the problem, we must also be part of the so
Los Angeles Southwest College - K - 12
Title: What Do We Have In Common? (Comparing Bivalves to Humans)Author: Kimberly Schneider (GK-12 program), Crystal Welch (Rising Tide), Nouran Ragaban (Rising Tide), Department of Biological Sciences, University of South Carolina Implemented: St. A
Los Angeles Southwest College - K - 12
What do we all have in common? A bivalve dissection!I. External Observations: 1. List two qualitative observations about your bivalve specimen: Various answers: black, smooth shell, funny strings coming out of one side, smells like fish etc 2. List
Los Angeles Southwest College - K - 12
Mussel Distribution Map of Mystery BaySample sites in Mystery Bay17 18 9 11 12 19 22 20 23 15 14 13 16 24 10 21Color Gallant galloTroublesome trossolus25 26 27 302933 342831 3235 36 37 3839 5 6 1 2 7 4 3 8 42 41 4043 44 50 45 49
Los Angeles Southwest College - K - 12
Student Question Sheet for Traits1. What would be the benefit in having more byssal threads?Since mussels use byssal threads to attach, more byssal threads means having a stronger hold. Therefore, mussels are less susceptible to being dislodged.2
Los Angeles Southwest College - K - 12
Who Polluted the Potomac?1. Who polluted the Potomac?_ _ 2. What effect did the increasing population have on the health of the river? _ _ 3. Think about the pollution contained in the canisters. Could something be done to prevent those types of m
Los Angeles Southwest College - K - 12
BIVALVE DISSECTIONMETHODS: 1. Follow ALL the safety guidelines that your teacher has set in your classroom. 2. Carefully pull the shell apart. You may need to cut the abductor mussels so you can lay out the shells side by side. 3. Get six (2 for let
Los Angeles Southwest College - K - 12
Wave Action Map of Mystery BayKeyHeavy wave action -Medium wave action -Light wave action -
Los Angeles Southwest College - K - 12
THE INSTITUTE OF BIOLOGY
Los Angeles Southwest College - K - 12
Los Angeles Southwest College - K - 12
Whos Dominant? Analyzing Traits of MusselsAuthor: Brice Gill, St. Andrews Middle School, Columbia SC Implemented: St. Andrews Middle SchoolOverview: Students create a mock population of two competitive species of mussels in order to examine the ef
Los Angeles Southwest College - K - 12
The Greenhouse Effect1) What is the greenhouse effect, and why is it so called? . .. 2) What gases are the most responsible for causing the greenhouse effect? .. .. 3) What changes are likely to happen to the sea levels as the greenhouse effect gets
Los Angeles Southwest College - K - 12
Title: Who polluted the Potomac? Author: Populations Connection (http:/www.populationconnection.org/). Modified by Kimberly R. Schneider (GK-12 program, Department of Biological Sciences, University of South Carolina) Implemented: St. Andrews Middle
Los Angeles Southwest College - K - 12
Title: Where in the world is carbon?Author: Kimberly R. Schneider, GK-12 program, Department of Biological Sciences, University of South Carolina - NOTE: some of the materials in the lesson were taken from the ChemSoc http:/www.chemsoc.org/networks/
Los Angeles Southwest College - E - 2
Starting a pollution prevention project using resources at hand makes good financial sense. However, if your project is complex, hiring a consultant to supplement your internal capabilities may help you complete the job in a thorough and efficient ma
Los Angeles Southwest College - EJMAY - 05
An Overview of the GAO Report on Key National IndicatorsRichard D. YoungIntroduction As the 21st century begins, many significant challenges and opportunities face Americans at all levels of jurisdictionnationwide as well as at state and local lev
Los Angeles Southwest College - EJNOV - 08
Lies, Damn Lies, and Energy Independence A Book Review Gusher of Lies: The Dangerous Delusions of Energy IndependenceMark A. BondoThe term &quot;energy independence&quot; is unavoidable to even the most casual followers of public policy and politics. Durin
Los Angeles Southwest College - EJMAY - 05
The South Carolina Washington Semester ProgramBeth H. Burn and Rebecca J. JohnsonIntroduction In the late 1980s, Senator Ernst Hollings and House Members Floyd Spence, Butler Derrick, Liz Patterson, and John Spratt approached Steve Beckham, the Di
Los Angeles Southwest College - EJMAY - 05
Around The InstituteEdwin C. Thomas Survey Research Laboratory Undertakes BRFSS Project In January of this year, the Institutes survey unit began telephone data collection for the South Carolina Behavioral Health Risk Factor Survey (BRFSS) for the S
Los Angeles Southwest College - EJMAY - 05
The Sex Education Curriculum in South Carolinas Public Schools: The Publics ViewForrest L. Alton, Robert W. Oldendick, and Katherine A. DraughonIntroduction Sex. The word alone is enough to stir up strong feelings among the South Carolina public,
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2007 Exam 1Name:_ANSWER KEY_s2 =i =1n( yi y ) 2 (n 1)P{E1 U E2} = P{E1} + P{E2} P{E1 E2} P{E1 E2} = P{E1}P{E2| E1}Y = yi P{Y = yi} Y2 = (yi Y)2 P{Y = yi} = E(Y2) (E(Y)2P{Y = j} = nCj pj (1 p)n-j Y = np Y2 = np(1
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2007 Exam 3Name:_~Z p 21 2 Y + Z ~ (1 - ~ ) p p 2 2 p where ~ = 2 2 n + Z n + Z22p p ( ~1 - ~2 ) Z 2~ (1 - ~ ) ~ (1 - ~ ) p1 p1 p p2 Y + 1 Y2 + 1 p p - where ~1 - ~2 = 1 + 2 n1 + 2 n2 + 2 n1 + 2 n2 + 2n2 Z p 0 (1
Los Angeles Southwest College - STAT - 205
STAT 205: Elementary Statistics for the Biological and Life Sciences Spring 2009 Section 2 M W F 9:05 9:55 in Sloan 105 Section 3 M W F 11:15 12:05 in BS 463Instructor Information: Leslie Hendrix Office LeConte 219B E-mail leslieahendrix@gmail.c
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2007 Exam 1Name:_s2 =i =1n( yi - y ) 2 (n - 1)P{E1 U E2} = P{E1} + P{E2} P{E1 E2} P{E1 E2} = P{E1}P{E2| E1}Y = yi P{Y = yi} Y2 = (yi Y)2 P{Y = yi} = E(Y2) (E(Y)2P{Y = j} = nCj pj (1 p)n-j Y = np Y2 = np(1 p)Z=
Los Angeles Southwest College - STAT - 205
STAT205 Exam2Name_ANSWERKEY_ Fall2006 PY j Z Y tnCjpj 1pnjY n s2 nY s n Y1 Y2 tt22 s1 n12 s2 n2Y1 Y2 0 s2 1 n1 s2 2 n2 Part I: Answer eight of the following nine questions. If you complete more than eig
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2006 Final Exam~Z p Name:_21 2 Y + Z ~ (1 ~ ) p p 2 2 where ~ = p 2 2 n + Z n + Z22p p ( ~1 ~2 ) Z 2~ (1 ~ ) ~ (1 ~ ) p1 p1 p p2 Y + 1 Y2 + 1 where ~1 ~2 = 1 + 2 p p n1 + 2 n2 + 2 n1 + 2 n2 + 2(Oi Ei ) 2 E i =
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2006 Exam 1Name:_s2 =i =1n( yi y ) 2 (n 1)P{E1 U E2} = P{E1} + P{E2} P{E1 E2} P{E1 E2} = P{E1}P{E2| E1}Y = yi P{Y = yi} Y2 = (yi Y)2 P{Y = yi} = E(Y2) (E(Y)2P{Y = j} = nCj pj (1 p)n-j Y = np Y2 = np(1 p)Z=(
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2006 Final Exam~Z p Name:_ANSWER KEY_21 2 Y + Z ~ (1 ~ ) p p 2 2 where ~ = p 2 2 n + Z n + Z22p p ( ~1 ~2 ) Z 2~ (1 ~ ) ~ (1 ~ ) p1 p1 p p2 Y + 1 Y2 + 1 where ~1 ~2 = 1 + 2 p p n1 + 2 n2 + 2 n1 + 2 n2 + 2(Oi Ei
Los Angeles Southwest College - STAT - 205
STAT 205: Elementary Statistics for the Biological and Life Sciences Fall 2008 Section 2 M W F 9:05 9:55 in Currell 203 Section 4 M W F 11:15 12:05 in Sumwalt 213Instructor Information: Leslie Hendrix Office LeConte 219B E-mail lesliehendrix@yma
Los Angeles Southwest College - STAT - 205
STAT 205 Spring 2007 Exam 1Name:_ANSWER KEY_s2 =i =1n( yi y ) 2 (n 1)P{E1 U E2} = P{E1} + P{E2} P{E1 E2} P{E1 E2} = P{E1}P{E2| E1}Y = yi P{Y = yi} Y2 = (yi Y)2 P{Y = yi} = E(Y2) (E(Y)2P{Y = j} = nCj pj (1 p)n-j Y = np Y2 = np
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2007 Exam 2Name:_P{Y = j} = nCj pj (1 p)n-jY Z=nY ts2nt=Y s n(Y1 Y2 ) t22 s12 s 2 + n1 n2t=(Y1 Y2 ) 02 s12 s 2 + n1 n2Part I: Answer eight of the following nine questions. If you complete more th
Los Angeles Southwest College - STAT - 205
M W F M W F M W F M W F M W F M W F M W F M W F M W F M W F M W FDate 12Jan 14Jan 16Jan 19Jan 21Jan 23Jan 26Jan 28Jan 30Jan 2Feb 4Feb 6Feb 9Feb 11Feb 14Feb 16Feb 18Feb 20Feb 23Feb 25Feb 27Feb 2Mar 4Mar 6Mar 9Mar 11Mar 13Mar 16Mar 18Mar 20Mar 23Mar
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2007 Exam 3Name: ANSWER KEY_~Z p 21 2 Y + Z ~ (1 - ~ ) p p 2 2 p where ~ = 2 2 n + Z n + Z222 p p ( ~1 - ~2 ) Z 2~ (1 - ~ ) ~ (1 - ~ ) p1 p1 p p2 Y + 1 Y2 + 1 p p - where ~1 - ~2 = 1 + 2 n1 + 2 n2 + 2 n1 + 2 n2 + 2n
Los Angeles Southwest College - STAT - 205
STAT 205 Spring 2007 Final Exam~Z p Name:_21 2 Y + Z ~ (1 - ~ ) p p 2 2 where ~ = p 2 2 n + Z n + Z22p p ( ~1 - ~2 ) Z 2~ (1 - ~ ) ~ (1 - ~ ) p1 p1 p p2 Y + 1 Y2 + 1 where ~1 - ~2 = 1 + 2 p p - n1 + 2 n2 + 2 n1 + 2 n2 + 2(Oi - Ei )
Los Angeles Southwest College - STAT - 205
STAT 205 Fall 2007 Exam 2Name:_ANSWER KEY_P{Y = j} = nCj pj (1 p)n-jY Z=nY ts2nt=Y s n(Y1 Y2 ) t22 s12 s 2 + n1 n2t=(Y1 Y2 ) 02 s12 s 2 + n1 n2Part I: Answer eight of the following nine questions. If you compl
Los Angeles Southwest College - STAT - 701
Statistics 701 Using PROC ANOVA in SAS to Perform ANOVA and Multiple Comparisons (Source of Notes: SAS Online Manual) Comparing Group Means with PROC ANOVA and PROC GLM When you have more than two means to compare, an F test in PROC ANOVA or PROC GLM
Los Angeles Southwest College - STAT - 701
Stat 701 Handout Matrix Computations Using PROC IML in SAS The following is an implementation of the computations in the lecture example using PROC IML in SAS. The SAS Program:proc iml; /* Defining the design matrix, X */ X={1 85 22, 1 83 23, 1 88 2
Los Angeles Southwest College - STAT - 701
Stat 701 Factorial Studies Analysis of Hay Fever Data Using SAS Program /* Hay Fever Drug Development */ options ls = 80; data hay; input relief FactorA \$ FactorB \$ RepNum @; cards; 2.4 1 1 1 2.7 1 1 2 2.3 1 1 3 2.5 1 1 4 4.6 1 2 1 4.2 1 2 2 4.9 1 2
Los Angeles Southwest College - STAT - 701
Statistics 701 Multiple Regression ApplicationResponse Variable: BodyFat Predictor Variables: 1. 2. 3. 4. 5. 6. 7. AbdoCirc Weight WristCirc ForeArmCirc Height NeckCirc ChestCircOutput from MinitabRegression Analysis The regression equation is Pe
Los Angeles Southwest College - STAT - 701
Stat 701 Handout on Binary Logistic Regression The Study Of Interest (Example on page 575 of text): The data provided below is from a study to assess the ability to complete a task within a specified time pertaining to a complex programming problem,
Los Angeles Southwest College - STAT - 701
Statistics 701 Extra Sum of Squares Data Set: Alcohol from Wine (X) and Heart Attack Deaths (Y)Results of Fitting a Simple Linear Regression Model Regression AnalysisThe regression equation is HrtAttRate = 261 - 23.0 AlcFrmWine Predictor Constant
Los Angeles Southwest College - STAT - 701
Stat 701 Single-Factor Analysis of VarianceData Set: Iris Color and Critical Flicker Frequency (CFF)Dotplots of CFF by IrisColo(group means are indicated by lines)30 29 28CFF27 26 25 24BrownBoxplots of CFF by IrisColo(means are indicated