ANEXO C. ****** Method 1 (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S S C A L E (A L P H A)

Size: px
Start display at page:

Download "ANEXO C. ****** Method 1 (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S S C A L E (A L P H A)"

Transcription

1 ANEXO C Alfa del factor de motivación general ****** Method (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S S C A L E (A L P H A) Item total Statistics Scale Scale Corrected Mean Variance Item Alpha if Item if Item if Item Deleted Deleted Correlation Deleted PE P4I P5I P6I P7E P8I P0I PE PE PI Analysis of Variance Source of Variation Sum of Sq. DF Mean Square F Prob. Between People Within People Between Measures Residual Nonadditivity Balance Grand Mean Tukey estimate of power to which observations must be raised to achieve additivity =.4770 Reliability Coefficients N of Cases = 8.0 N of Items = 0 Alpha =.074

2 Alfa del factor de sin motivación ****** Method (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S S C A L E (A L P H A) Item total Statistics Scale Scale Corrected Mean Variance Item Alpha if Item if Item if Item Deleted Deleted Correlation Deleted PSM PE PE P4SM Analysis of Variance Source of Variation Sum of Sq. DF Mean Square F Prob. Between People Within People Between Measures Residual Nonadditivity Balance Grand Mean 4.88 Tukey estimate of power to which observations must be raised to achieve additivity =.477 Reliability Coefficients N of Cases =.0 N of Items = 4 Alpha =.607

3 Alfa del factor de satisfacción general ****** Method (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S S C A L E (A L P H A) Item total Statistics Scale Scale Corrected Mean Variance Item Alpha if Item if Item if Item Deleted Deleted Correlation Deleted P5E P6I P7E P8I PE P0I PI P4I P5E P7E PE Analysis of Variance Source of Variation Sum of Sq. DF Mean Square F Prob. Between People Within People Between Measures Residual Nonadditivity Balance Grand Mean 4.07 Tukey estimate of power to which observations must be raised to achieve additivity =.4706 Reliability Coefficients N of Cases = 4.0 N of Items = Alpha =.886

4 Alfa del factor de intenciones de rotación de personal ****** Method (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S S C A L E (A L P H A) Item total Statistics Scale Scale Corrected Mean Variance Item Alpha if Item if Item if Item Deleted Deleted Correlation Deleted P P P Analysis of Variance Source of Variation Sum of Sq. DF Mean Square F Prob. Between People Within People Between Measures Residual Nonadditivity Balance Grand Mean Tukey estimate of power to which observations must be raised to achieve additivity =.86 Reliability Coefficients N of Cases =.0 N of Items = Alpha =.8786

5 Análisis descriptivo del factor de motivación general Descriptive Statistics F_M N (listwise) N Range Minimum Maximum Mean Std. Deviation Variance Análisis descriptivo del factor sin motivación Descriptive Statistics F_SM N (listwise) N Range Minimum Maximum Mean Std. Deviation Variance Análisis descriptivo del factor de satisfacción general Descriptive Statistics F_S N (listwise N Range Minimum Maximum Mean Std. Deviation Variance Análisis descriptivo del factor de intenciones de rotación Descriptive Statistics F_IR N (listwise N Range Minimum Maximum Mean Std. Deviation Variance

6 Análisis descriptivo de los datos generales Ocupación o puesto P ocupación o puesto Frequency

7 4 5 Antigüedad en la empresa P4 antigüedad en la empresa Frequency Género P6 género Frequency Escolaridad P5 escolaridad Frequency

8 Edad P7 edad Frequency Situación laboral P8 situación laboral Frequency Horario de trabajo P tipo de horario Frequency

Regression ( Kemampuan Individu, Lingkungan kerja dan Motivasi)

Regression ( Kemampuan Individu, Lingkungan kerja dan Motivasi) Regression (, Lingkungan kerja dan ) Descriptive Statistics Mean Std. Deviation N 3.87.333 32 3.47.672 32 3.78.585 32 s Pearson Sig. (-tailed) N Kemampuan Lingkungan Individu Kerja.000.432.49.432.000.3.49.3.000..000.000.000..000.000.000.

More information

T. Mark Beasley One-Way Repeated Measures ANOVA handout

T. Mark Beasley One-Way Repeated Measures ANOVA handout T. Mark Beasley One-Way Repeated Measures ANOVA handout Profile Analysis Example In the One-Way Repeated Measures ANOVA, two factors represent separate sources of variance. Their interaction presents an

More information

Item-Total Statistics. Corrected Item- Cronbach's Item Deleted. Total

Item-Total Statistics. Corrected Item- Cronbach's Item Deleted. Total 45 Lampiran 3 : Uji Validitas dan Reliabilitas Reliability Case Processing Summary N % Valid 75 00.0 Cases Excluded a 0.0 Total 75 00.0 a. Listwise deletion based on all variables in the procedure. Reliability

More information

Sociology 593 Exam 1 February 17, 1995

Sociology 593 Exam 1 February 17, 1995 Sociology 593 Exam 1 February 17, 1995 I. True-False. (25 points) Indicate whether the following statements are true or false. If false, briefly explain why. 1. A researcher regressed Y on. When he plotted

More information

Confidence Interval for the mean response

Confidence Interval for the mean response Week 3: Prediction and Confidence Intervals at specified x. Testing lack of fit with replicates at some x's. Inference for the correlation. Introduction to regression with several explanatory variables.

More information

1. How will an increase in the sample size affect the width of the confidence interval?

1. How will an increase in the sample size affect the width of the confidence interval? Study Guide Concept Questions 1. How will an increase in the sample size affect the width of the confidence interval? 2. How will an increase in the sample size affect the power of a statistical test?

More information

T-test: means of Spock's judge versus all other judges 1 12:10 Wednesday, January 5, judge1 N Mean Std Dev Std Err Minimum Maximum

T-test: means of Spock's judge versus all other judges 1 12:10 Wednesday, January 5, judge1 N Mean Std Dev Std Err Minimum Maximum T-test: means of Spock's judge versus all other judges 1 The TTEST Procedure Variable: pcwomen judge1 N Mean Std Dev Std Err Minimum Maximum OTHER 37 29.4919 7.4308 1.2216 16.5000 48.9000 SPOCKS 9 14.6222

More information

MANOVA is an extension of the univariate ANOVA as it involves more than one Dependent Variable (DV). The following are assumptions for using MANOVA:

MANOVA is an extension of the univariate ANOVA as it involves more than one Dependent Variable (DV). The following are assumptions for using MANOVA: MULTIVARIATE ANALYSIS OF VARIANCE MANOVA is an extension of the univariate ANOVA as it involves more than one Dependent Variable (DV). The following are assumptions for using MANOVA: 1. Cell sizes : o

More information

- Prefix 'audi', 'photo' and 'phobia' - What's striped and bouncy? A zebra on a trampoline!

- Prefix 'audi', 'photo' and 'phobia' - What's striped and bouncy? A zebra on a trampoline! - Pf '', '' '' - Nm: Ws 11 D: W's s y? A m! A m f s s f ws. Ts ws. T ws y ( ss), y ( w) y (fm ). W y f, w. s m y m y m w q y q s q m w s k s w q w s y m s m m m y s s y y www.s..k s.s 2013 s www.sss.m

More information

FACTORIAL DESIGNS and NESTED DESIGNS

FACTORIAL DESIGNS and NESTED DESIGNS Experimental Design and Statistical Methods Workshop FACTORIAL DESIGNS and NESTED DESIGNS Jesús Piedrafita Arilla jesus.piedrafita@uab.cat Departament de Ciència Animal i dels Aliments Items Factorial

More information

Saya, selaku Ketua Paguyuban Lansia Gereja Katolik Kelahiran Santa. Perawan Maria Surabaya, menyatakan bahwa mahasiswa bernama Dewi Setiawati

Saya, selaku Ketua Paguyuban Lansia Gereja Katolik Kelahiran Santa. Perawan Maria Surabaya, menyatakan bahwa mahasiswa bernama Dewi Setiawati LAMP IRAN SURA T KETERANGAN Saya, selaku Ketua Paguyuban Lansia Gereja Katolik Kelahiran Santa Perawan Maria Surabaya, menyatakan bahwa mahasiswa bernama Dewi Setiawati benar-benar telah melakukan pengambilan

More information

Formula for the t-test

Formula for the t-test Formula for the t-test: How the t-test Relates to the Distribution of the Data for the Groups Formula for the t-test: Formula for the Standard Error of the Difference Between the Means Formula for the

More information

Workshop 7.4a: Single factor ANOVA

Workshop 7.4a: Single factor ANOVA -1- Workshop 7.4a: Single factor ANOVA Murray Logan November 23, 2016 Table of contents 1 Revision 1 2 Anova Parameterization 2 3 Partitioning of variance (ANOVA) 10 4 Worked Examples 13 1. Revision 1.1.

More information

Introduction to Analysis of Variance (ANOVA) Part 2

Introduction to Analysis of Variance (ANOVA) Part 2 Introduction to Analysis of Variance (ANOVA) Part 2 Single factor Serpulid recruitment and biofilms Effect of biofilm type on number of recruiting serpulid worms in Port Phillip Bay Response variable:

More information

Review of CLDP 944: Multilevel Models for Longitudinal Data

Review of CLDP 944: Multilevel Models for Longitudinal Data Review of CLDP 944: Multilevel Models for Longitudinal Data Topics: Review of general MLM concepts and terminology Model comparisons and significance testing Fixed and random effects of time Significance

More information

sociology 362 regression

sociology 362 regression sociology 36 regression Regression is a means of modeling how the conditional distribution of a response variable (say, Y) varies for different values of one or more independent explanatory variables (say,

More information

STAT 3900/4950 MIDTERM TWO Name: Spring, 2015 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis

STAT 3900/4950 MIDTERM TWO Name: Spring, 2015 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis STAT 3900/4950 MIDTERM TWO Name: Spring, 205 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis Instructions: You may use your books, notes, and SPSS/SAS. NO

More information

1 Descriptive statistics. 2 Scores and probability distributions. 3 Hypothesis testing and one-sample t-test. 4 More on t-tests

1 Descriptive statistics. 2 Scores and probability distributions. 3 Hypothesis testing and one-sample t-test. 4 More on t-tests Overall Overview INFOWO Statistics lecture S3: Hypothesis testing Peter de Waal Department of Information and Computing Sciences Faculty of Science, Universiteit Utrecht 1 Descriptive statistics 2 Scores

More information

Stat 401B Exam 2 Fall 2015

Stat 401B Exam 2 Fall 2015 Stat 401B Exam Fall 015 I have neither given nor received unauthorized assistance on this exam. Name Signed Date Name Printed ATTENTION! Incorrect numerical answers unaccompanied by supporting reasoning

More information

One-Way ANOVA Source Table J - 1 SS B / J - 1 MS B /MS W. Pairwise Post-Hoc Comparisons of Means

One-Way ANOVA Source Table J - 1 SS B / J - 1 MS B /MS W. Pairwise Post-Hoc Comparisons of Means One-Way ANOVA Source Table ANOVA MODEL: ij = µ* + α j + ε ij H 0 : µ 1 = µ =... = µ j or H 0 : Σα j = 0 Source Sum of Squares df Mean Squares F Between Groups n j ( j - * ) J - 1 SS B / J - 1 MS B /MS

More information

Analysis of Covariance

Analysis of Covariance Analysis of Covariance Using categorical and continuous predictor variables Example An experiment is set up to look at the effects of watering on Oak Seedling establishment Three levels of watering: (no

More information

sociology 362 regression

sociology 362 regression sociology 36 regression Regression is a means of studying how the conditional distribution of a response variable (say, Y) varies for different values of one or more independent explanatory variables (say,

More information

Correlations. Notes. Output Created Comments 04-OCT :34:52

Correlations. Notes. Output Created Comments 04-OCT :34:52 Correlations Output Created Comments Input Missing Value Handling Syntax Resources Notes Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor

More information

Multiple Regression: Example

Multiple Regression: Example Multiple Regression: Example Cobb-Douglas Production Function The Cobb-Douglas production function for observed economic data i = 1,..., n may be expressed as where O i is output l i is labour input c

More information

1. What does the alternate hypothesis ask for a one-way between-subjects analysis of variance?

1. What does the alternate hypothesis ask for a one-way between-subjects analysis of variance? 1. What does the alternate hypothesis ask for a one-way between-subjects analysis of variance? 2. What is the difference between between-group variability and within-group variability? 3. What does between-group

More information

SMA 6304 / MIT / MIT Manufacturing Systems. Lecture 10: Data and Regression Analysis. Lecturer: Prof. Duane S. Boning

SMA 6304 / MIT / MIT Manufacturing Systems. Lecture 10: Data and Regression Analysis. Lecturer: Prof. Duane S. Boning SMA 6304 / MIT 2.853 / MIT 2.854 Manufacturing Systems Lecture 10: Data and Regression Analysis Lecturer: Prof. Duane S. Boning 1 Agenda 1. Comparison of Treatments (One Variable) Analysis of Variance

More information

ANOVA: Analysis of Variation

ANOVA: Analysis of Variation ANOVA: Analysis of Variation The basic ANOVA situation Two variables: 1 Categorical, 1 Quantitative Main Question: Do the (means of) the quantitative variables depend on which group (given by categorical

More information

Introduction to Within-Person Analysis and RM ANOVA

Introduction to Within-Person Analysis and RM ANOVA Introduction to Within-Person Analysis and RM ANOVA Today s Class: From between-person to within-person ANOVAs for longitudinal data Variance model comparisons using 2 LL CLP 944: Lecture 3 1 The Two Sides

More information

ANOVA: Comparing More Than Two Means

ANOVA: Comparing More Than Two Means ANOVA: Comparing More Than Two Means Chapter 11 Cathy Poliak, Ph.D. cathy@math.uh.edu Office Fleming 11c Department of Mathematics University of Houston Lecture 25-3339 Cathy Poliak, Ph.D. cathy@math.uh.edu

More information

data proc sort proc corr run proc reg run proc glm run proc glm run proc glm run proc reg CONMAIN CONINT run proc reg DUMMAIN DUMINT run proc reg

data proc sort proc corr run proc reg run proc glm run proc glm run proc glm run proc reg CONMAIN CONINT run proc reg DUMMAIN DUMINT run proc reg data one; input id Y group X; I1=0;I2=0;I3=0;if group=1 then I1=1;if group=2 then I2=1;if group=3 then I3=1; IINT1=I1*X;IINT2=I2*X;IINT3=I3*X; *************************************************************************;

More information

SCHOOL OF MATHEMATICS AND STATISTICS

SCHOOL OF MATHEMATICS AND STATISTICS SHOOL OF MATHEMATIS AND STATISTIS Linear Models Autumn Semester 2015 16 2 hours Marks will be awarded for your best three answers. RESTRITED OPEN BOOK EXAMINATION andidates may bring to the examination

More information

CHAPTER 5 FUNCTIONAL FORMS OF REGRESSION MODELS

CHAPTER 5 FUNCTIONAL FORMS OF REGRESSION MODELS CHAPTER 5 FUNCTIONAL FORMS OF REGRESSION MODELS QUESTIONS 5.1. (a) In a log-log model the dependent and all explanatory variables are in the logarithmic form. (b) In the log-lin model the dependent variable

More information

Regression: Main Ideas Setting: Quantitative outcome with a quantitative explanatory variable. Example, cont.

Regression: Main Ideas Setting: Quantitative outcome with a quantitative explanatory variable. Example, cont. TCELL 9/4/205 36-309/749 Experimental Design for Behavioral and Social Sciences Simple Regression Example Male black wheatear birds carry stones to the nest as a form of sexual display. Soler et al. wanted

More information

PLS205 Winter Homework Topic 8

PLS205 Winter Homework Topic 8 PLS205 Winter 2015 Homework Topic 8 Due TUESDAY, February 10, at the beginning of discussion. Answer all parts of the questions completely, and clearly document the procedures used in each exercise. To

More information

2-way analysis of variance

2-way analysis of variance 2-way analysis of variance We may be considering the effect of two factors (A and B) on our response variable, for instance fertilizer and variety on maize yield; or therapy and sex on cholesterol level.

More information

An Introduction to Multilevel Models. PSYC 943 (930): Fundamentals of Multivariate Modeling Lecture 25: December 7, 2012

An Introduction to Multilevel Models. PSYC 943 (930): Fundamentals of Multivariate Modeling Lecture 25: December 7, 2012 An Introduction to Multilevel Models PSYC 943 (930): Fundamentals of Multivariate Modeling Lecture 25: December 7, 2012 Today s Class Concepts in Longitudinal Modeling Between-Person vs. +Within-Person

More information

ANEXO 1 MODELO CON LM1 NOMINAL DESESTACIONALIZADO. Statistic

ANEXO 1 MODELO CON LM1 NOMINAL DESESTACIONALIZADO. Statistic ANEXO MODELO CON LM NOMINAL DESESTACIONALIZADO.. Pruebas de Raiz Unitaria Null Hypothesis: D (LM) has a unit root Exogenous: Constant, Linear Trend Lag Length: 2 (Automatic based on AIC, MAXLAG=2) t ElliottRothenbergStock

More information

ANOVA continued. Chapter 10

ANOVA continued. Chapter 10 ANOVA continued Chapter 10 Zettergren (003) School adjustment in adolescence for previously rejected, average, and popular children. Effect of peer reputation on academic performance and school adjustment

More information

STATISTICAL MODELLING PRACTICAL IX SOLUTIONS

STATISTICAL MODELLING PRACTICAL IX SOLUTIONS IX-1. STATISTICAL MODELLING PRACTICAL IX SOLUTIONS IX.1 An experiment was designed to evaluate alternative processes for pigment dispersion in aqueous media. Six solutions of each of two specific dispersion

More information

Inter Item Correlation Matrix (R )

Inter Item Correlation Matrix (R ) 7 1. I have the ability to influence my child s well-being. 2. Whether my child avoids injury is just a matter of luck. 3. Luck plays a big part in determining how healthy my child is. 4. I can do a lot

More information

36-309/749 Experimental Design for Behavioral and Social Sciences. Sep. 22, 2015 Lecture 4: Linear Regression

36-309/749 Experimental Design for Behavioral and Social Sciences. Sep. 22, 2015 Lecture 4: Linear Regression 36-309/749 Experimental Design for Behavioral and Social Sciences Sep. 22, 2015 Lecture 4: Linear Regression TCELL Simple Regression Example Male black wheatear birds carry stones to the nest as a form

More information

Simultaneous Equations with Error Components. Mike Bronner Marko Ledic Anja Breitwieser

Simultaneous Equations with Error Components. Mike Bronner Marko Ledic Anja Breitwieser Simultaneous Equations with Error Components Mike Bronner Marko Ledic Anja Breitwieser PRESENTATION OUTLINE Part I: - Simultaneous equation models: overview - Empirical example Part II: - Hausman and Taylor

More information

Lecture#12. Instrumental variables regression Causal parameters III

Lecture#12. Instrumental variables regression Causal parameters III Lecture#12 Instrumental variables regression Causal parameters III 1 Demand experiment, market data analysis & simultaneous causality 2 Simultaneous causality Your task is to estimate the demand function

More information

Sociology 593 Exam 1 Answer Key February 17, 1995

Sociology 593 Exam 1 Answer Key February 17, 1995 Sociology 593 Exam 1 Answer Key February 17, 1995 I. True-False. (5 points) Indicate whether the following statements are true or false. If false, briefly explain why. 1. A researcher regressed Y on. When

More information

STATISTICS 110/201 PRACTICE FINAL EXAM

STATISTICS 110/201 PRACTICE FINAL EXAM STATISTICS 110/201 PRACTICE FINAL EXAM Questions 1 to 5: There is a downloadable Stata package that produces sequential sums of squares for regression. In other words, the SS is built up as each variable

More information

Multiple Comparisons

Multiple Comparisons Multiple Comparisons Error Rates, A Priori Tests, and Post-Hoc Tests Multiple Comparisons: A Rationale Multiple comparison tests function to tease apart differences between the groups within our IV when

More information

STA 303H1F: Two-way Analysis of Variance Practice Problems

STA 303H1F: Two-way Analysis of Variance Practice Problems STA 303H1F: Two-way Analysis of Variance Practice Problems 1. In the Pygmalion example from lecture, why are the average scores of the platoon used as the response variable, rather than the scores of the

More information

Chapter 5 Introduction to Factorial Designs Solutions

Chapter 5 Introduction to Factorial Designs Solutions Solutions from Montgomery, D. C. (1) Design and Analysis of Experiments, Wiley, NY Chapter 5 Introduction to Factorial Designs Solutions 5.1. The following output was obtained from a computer program that

More information

ANOVA continued. Chapter 10

ANOVA continued. Chapter 10 ANOVA continued Chapter 10 Zettergren (003) School adjustment in adolescence for previously rejected, average, and popular children. Effect of peer reputation on academic performance and school adjustment

More information

Section Least Squares Regression

Section Least Squares Regression Section 2.3 - Least Squares Regression Statistics 104 Autumn 2004 Copyright c 2004 by Mark E. Irwin Regression Correlation gives us a strength of a linear relationship is, but it doesn t tell us what it

More information

Multiple Predictor Variables: ANOVA

Multiple Predictor Variables: ANOVA Multiple Predictor Variables: ANOVA 1/32 Linear Models with Many Predictors Multiple regression has many predictors BUT - so did 1-way ANOVA if treatments had 2 levels What if there are multiple treatment

More information

Variance Decomposition and Goodness of Fit

Variance Decomposition and Goodness of Fit Variance Decomposition and Goodness of Fit 1. Example: Monthly Earnings and Years of Education In this tutorial, we will focus on an example that explores the relationship between total monthly earnings

More information

1. If X has density. cx 3 e x ), 0 x < 0, otherwise. Find the value of c that makes f a probability density. f(x) =

1. If X has density. cx 3 e x ), 0 x < 0, otherwise. Find the value of c that makes f a probability density. f(x) = 1. If X has density f(x) = { cx 3 e x ), 0 x < 0, otherwise. Find the value of c that makes f a probability density. 2. Let X have density f(x) = { xe x, 0 < x < 0, otherwise. (a) Find P (X > 2). (b) Find

More information

Introduction to Analysis of Variance. Chapter 11

Introduction to Analysis of Variance. Chapter 11 Introduction to Analysis of Variance Chapter 11 Review t-tests Single-sample t-test Independent samples t-test Related or paired-samples t-test s m M t ) ( 1 1 ) ( m m s M M t M D D D s M t n s s M 1 )

More information

The Distribution of F

The Distribution of F The Distribution of F It can be shown that F = SS Treat/(t 1) SS E /(N t) F t 1,N t,λ a noncentral F-distribution with t 1 and N t degrees of freedom and noncentrality parameter λ = t i=1 n i(µ i µ) 2

More information

22s:152 Applied Linear Regression. Take random samples from each of m populations.

22s:152 Applied Linear Regression. Take random samples from each of m populations. 22s:152 Applied Linear Regression Chapter 8: ANOVA NOTE: We will meet in the lab on Monday October 10. One-way ANOVA Focuses on testing for differences among group means. Take random samples from each

More information

Amount of Weight Gained. Regained 5 lb or Less Regained More Than 5 lb Total. In Person Online Newsletter

Amount of Weight Gained. Regained 5 lb or Less Regained More Than 5 lb Total. In Person Online Newsletter This is an open book test. You are allowed your textbook, notes and a calculator. Other books, laptops, or messaging devices are not permitted. Give complete solutions. Be clear about the order of logic

More information

A Re-Introduction to General Linear Models (GLM)

A Re-Introduction to General Linear Models (GLM) A Re-Introduction to General Linear Models (GLM) Today s Class: You do know the GLM Estimation (where the numbers in the output come from): From least squares to restricted maximum likelihood (REML) Reviewing

More information

General Linear Model (Chapter 4)

General Linear Model (Chapter 4) General Linear Model (Chapter 4) Outcome variable is considered continuous Simple linear regression Scatterplots OLS is BLUE under basic assumptions MSE estimates residual variance testing regression coefficients

More information

MODELS WITHOUT AN INTERCEPT

MODELS WITHOUT AN INTERCEPT Consider the balanced two factor design MODELS WITHOUT AN INTERCEPT Factor A 3 levels, indexed j 0, 1, 2; Factor B 5 levels, indexed l 0, 1, 2, 3, 4; n jl 4 replicate observations for each factor level

More information

Descriptive Statistics

Descriptive Statistics *following creates z scores for the ydacl statedp traitdp and rads vars. *specifically adding the /SAVE subcommand to descriptives will create z. *scores for whatever variables are in the command. DESCRIPTIVES

More information

This gives us an upper and lower bound that capture our population mean.

This gives us an upper and lower bound that capture our population mean. Confidence Intervals Critical Values Practice Problems 1 Estimation 1.1 Confidence Intervals Definition 1.1 Margin of error. The margin of error of a distribution is the amount of error we predict when

More information

22s:152 Applied Linear Regression. There are a couple commonly used models for a one-way ANOVA with m groups. Chapter 8: ANOVA

22s:152 Applied Linear Regression. There are a couple commonly used models for a one-way ANOVA with m groups. Chapter 8: ANOVA 22s:152 Applied Linear Regression Chapter 8: ANOVA NOTE: We will meet in the lab on Monday October 10. One-way ANOVA Focuses on testing for differences among group means. Take random samples from each

More information

NC Births, ANOVA & F-tests

NC Births, ANOVA & F-tests Math 158, Spring 2018 Jo Hardin Multiple Regression II R code Decomposition of Sums of Squares (and F-tests) NC Births, ANOVA & F-tests A description of the data is given at http://pages.pomona.edu/~jsh04747/courses/math58/

More information

STA 303H1F: Two-way Analysis of Variance Practice Problems

STA 303H1F: Two-way Analysis of Variance Practice Problems STA 303H1F: Two-way Analysis of Variance Practice Problems 1. In the Pygmalion example from lecture, why are the average scores of the platoon used as the response variable, rather than the scores of the

More information

Stat 412/512 TWO WAY ANOVA. Charlotte Wickham. stat512.cwick.co.nz. Feb

Stat 412/512 TWO WAY ANOVA. Charlotte Wickham. stat512.cwick.co.nz. Feb Stat 42/52 TWO WAY ANOVA Feb 6 25 Charlotte Wickham stat52.cwick.co.nz Roadmap DONE: Understand what a multiple regression model is. Know how to do inference on single and multiple parameters. Some extra

More information

Multiple Regression Introduction to Statistics Using R (Psychology 9041B)

Multiple Regression Introduction to Statistics Using R (Psychology 9041B) Multiple Regression Introduction to Statistics Using R (Psychology 9041B) Paul Gribble Winter, 2016 1 Correlation, Regression & Multiple Regression 1.1 Bivariate correlation The Pearson product-moment

More information

Clase: Regresión Parte I

Clase: Regresión Parte I Clase: Regresión Parte I Índice: Introducción 1. Qué es Econometría? 2. Pasos en el Análisis Empírico 3. Estructura de una base de datos para análisis econométrico 4. Causalidad: breve introducción Modelo

More information

Review of Multiple Regression

Review of Multiple Regression Ronald H. Heck 1 Let s begin with a little review of multiple regression this week. Linear models [e.g., correlation, t-tests, analysis of variance (ANOVA), multiple regression, path analysis, multivariate

More information

a) Prepare a normal probability plot of the effects. Which effects seem active?

a) Prepare a normal probability plot of the effects. Which effects seem active? Problema 8.6: R.D. Snee ( Experimenting with a large number of variables, in experiments in Industry: Design, Analysis and Interpretation of Results, by R. D. Snee, L.B. Hare, and J. B. Trout, Editors,

More information

5. Let W follow a normal distribution with mean of μ and the variance of 1. Then, the pdf of W is

5. Let W follow a normal distribution with mean of μ and the variance of 1. Then, the pdf of W is Practice Final Exam Last Name:, First Name:. Please write LEGIBLY. Answer all questions on this exam in the space provided (you may use the back of any page if you need more space). Show all work but do

More information

Categorical and Zero Inflated Growth Models

Categorical and Zero Inflated Growth Models Categorical and Zero Inflated Growth Models Alan C. Acock* Summer, 2009 *Alan C. Acock, Department of Human Development and Family Sciences, Oregon State University, Corvallis OR 97331 (alan.acock@oregonstate.edu).

More information

COMPARING SEVERAL MEANS: ANOVA

COMPARING SEVERAL MEANS: ANOVA LAST UPDATED: November 15, 2012 COMPARING SEVERAL MEANS: ANOVA Objectives 2 Basic principles of ANOVA Equations underlying one-way ANOVA Doing a one-way ANOVA in R Following up an ANOVA: Planned contrasts/comparisons

More information

TESTING AND MEASUREMENT IN PSYCHOLOGY. Merve Denizci Nazlıgül, M.S.

TESTING AND MEASUREMENT IN PSYCHOLOGY. Merve Denizci Nazlıgül, M.S. TESTING AND MEASUREMENT IN PSYCHOLOGY Merve Denizci Nazlıgül, M.S. PREPARING THE DATA FOR ANALYSIS 1. ACCURACY OF DATA FILE 1st step SPSS FREQUENCIES For continuous variables, are all the values within

More information

Problem Set 10: Panel Data

Problem Set 10: Panel Data Problem Set 10: Panel Data 1. Read in the data set, e11panel1.dta from the course website. This contains data on a sample or 1252 men and women who were asked about their hourly wage in two years, 2005

More information

STA 302 H1F / 1001 HF Fall 2007 Test 1 October 24, 2007

STA 302 H1F / 1001 HF Fall 2007 Test 1 October 24, 2007 STA 302 H1F / 1001 HF Fall 2007 Test 1 October 24, 2007 LAST NAME: SOLUTIONS FIRST NAME: STUDENT NUMBER: ENROLLED IN: (circle one) STA 302 STA 1001 INSTRUCTIONS: Time: 90 minutes Aids allowed: calculator.

More information

Sociology Exam 1 Answer Key Revised February 26, 2007

Sociology Exam 1 Answer Key Revised February 26, 2007 Sociology 63993 Exam 1 Answer Key Revised February 26, 2007 I. True-False. (20 points) Indicate whether the following statements are true or false. If false, briefly explain why. 1. An outlier on Y will

More information

N J SS W /df W N - 1

N J SS W /df W N - 1 One-Way ANOVA Source Table ANOVA MODEL: ij = µ* + α j + ε ij H 0 : µ = µ =... = µ j or H 0 : Σα j = 0 Source Sum of Squares df Mean Squares F J Between Groups nj( j * ) J - SS B /(J ) MS B /MS W = ( N

More information

Solutions: Monday, October 22

Solutions: Monday, October 22 Amherst College Department of Economics Economics 360 Fall 2012 1. Focus on the following agricultural data: Solutions: Monday, October 22 Agricultural Production Data: Cross section agricultural data

More information

Comparing Several Means

Comparing Several Means Comparing Several Means Some slides from R. Pruim STA303/STA1002: Methods of Data Analysis II, Summer 2016 Michael Guerzhoy The Dating World of Swordtail Fish In some species of swordtail fish, males develop

More information

A Re-Introduction to General Linear Models

A Re-Introduction to General Linear Models A Re-Introduction to General Linear Models Today s Class: Big picture overview Why we are using restricted maximum likelihood within MIXED instead of least squares within GLM Linear model interpretation

More information

ECON3150/4150 Spring 2016

ECON3150/4150 Spring 2016 ECON3150/4150 Spring 2016 Lecture 4 - The linear regression model Siv-Elisabeth Skjelbred University of Oslo Last updated: January 26, 2016 1 / 49 Overview These lecture slides covers: The linear regression

More information

Statistical analysis and ARIMA model

Statistical analysis and ARIMA model 2018; 4(4): 23-30 ISSN Print: 2394-7500 ISSN Online: 2394-5869 Impact Factor: 5.2 IJAR 2018; 4(4): 23-30 www.allresearchjournal.com Received: 11-02-2018 Accepted: 15-03-2018 Panchal Bhavini V Research

More information

1 The basics of panel data

1 The basics of panel data Introductory Applied Econometrics EEP/IAS 118 Spring 2015 Related materials: Steven Buck Notes to accompany fixed effects material 4-16-14 ˆ Wooldridge 5e, Ch. 1.3: The Structure of Economic Data ˆ Wooldridge

More information

ANCOVA. Lecture 9 Andrew Ainsworth

ANCOVA. Lecture 9 Andrew Ainsworth ANCOVA Lecture 9 Andrew Ainsworth What is ANCOVA? Analysis of covariance an extension of ANOVA in which main effects and interactions are assessed on DV scores after the DV has been adjusted for by the

More information

Problem Set #3-Key. wage Coef. Std. Err. t P> t [95% Conf. Interval]

Problem Set #3-Key. wage Coef. Std. Err. t P> t [95% Conf. Interval] Problem Set #3-Key Sonoma State University Economics 317- Introduction to Econometrics Dr. Cuellar 1. Use the data set Wage1.dta to answer the following questions. a. For the regression model Wage i =

More information

CAMPBELL COLLABORATION

CAMPBELL COLLABORATION CAMPBELL COLLABORATION Random and Mixed-effects Modeling C Training Materials 1 Overview Effect-size estimates Random-effects model Mixed model C Training Materials Effect sizes Suppose we have computed

More information

ANOVA continued. Chapter 11

ANOVA continued. Chapter 11 ANOVA continued Chapter 11 Zettergren (003) School adjustment in adolescence for previously rejected, average, and popular children. Effect of peer reputation on academic performance and school adjustment

More information

Practice 2SLS with Artificial Data Part 1

Practice 2SLS with Artificial Data Part 1 Practice 2SLS with Artificial Data Part 1 Yona Rubinstein July 2016 Yona Rubinstein (LSE) Practice 2SLS with Artificial Data Part 1 07/16 1 / 16 Practice with Artificial Data In this note we use artificial

More information

Density Temp vs Ratio. temp

Density Temp vs Ratio. temp Temp Ratio Density 0.00 0.02 0.04 0.06 0.08 0.10 0.12 Density 0.0 0.2 0.4 0.6 0.8 1.0 1. (a) 170 175 180 185 temp 1.0 1.5 2.0 2.5 3.0 ratio The histogram shows that the temperature measures have two peaks,

More information

One-way analysis of variance

One-way analysis of variance Analysis of variance From R.R. Sokal and F.J. Rohlf, Biometry, 2nd Edition (1981): A knowledge of analysis of variance is indispensable to any modern biologist and, after you have mastered it, you will

More information

Booklet of Code and Output for STAC32 Final Exam

Booklet of Code and Output for STAC32 Final Exam Booklet of Code and Output for STAC32 Final Exam December 8, 2014 List of Figures in this document by page: List of Figures 1 Popcorn data............................. 2 2 MDs by city, with normal quantile

More information

Variance Decomposition in Regression James M. Murray, Ph.D. University of Wisconsin - La Crosse Updated: October 04, 2017

Variance Decomposition in Regression James M. Murray, Ph.D. University of Wisconsin - La Crosse Updated: October 04, 2017 Variance Decomposition in Regression James M. Murray, Ph.D. University of Wisconsin - La Crosse Updated: October 04, 2017 PDF file location: http://www.murraylax.org/rtutorials/regression_anovatable.pdf

More information

Applied Regression Modeling: A Business Approach Chapter 3: Multiple Linear Regression Sections

Applied Regression Modeling: A Business Approach Chapter 3: Multiple Linear Regression Sections Applied Regression Modeling: A Business Approach Chapter 3: Multiple Linear Regression Sections 3.4 3.6 by Iain Pardoe 3.4 Model assumptions 2 Regression model assumptions.............................................

More information

ESP 178 Applied Research Methods. 2/23: Quantitative Analysis

ESP 178 Applied Research Methods. 2/23: Quantitative Analysis ESP 178 Applied Research Methods 2/23: Quantitative Analysis Data Preparation Data coding create codebook that defines each variable, its response scale, how it was coded Data entry for mail surveys and

More information

Booklet of Code and Output for STAC32 Final Exam

Booklet of Code and Output for STAC32 Final Exam Booklet of Code and Output for STAC32 Final Exam December 12, 2015 List of Figures in this document by page: List of Figures 1 Time in days for students of different majors to find full-time employment..............................

More information

Comparing Several Means: ANOVA

Comparing Several Means: ANOVA Comparing Several Means: ANOVA Understand the basic principles of ANOVA Why it is done? What it tells us? Theory of one way independent ANOVA Following up an ANOVA: Planned contrasts/comparisons Choosing

More information

Stat 529 (Winter 2011) A simple linear regression (SLR) case study. Mammals brain weights and body weights

Stat 529 (Winter 2011) A simple linear regression (SLR) case study. Mammals brain weights and body weights Stat 529 (Winter 2011) A simple linear regression (SLR) case study Reading: Sections 8.1 8.4, 8.6, 8.7 Mammals brain weights and body weights Questions of interest Scatterplots of the data Log transforming

More information

Table 1: Fish Biomass data set on 26 streams

Table 1: Fish Biomass data set on 26 streams Math 221: Multiple Regression S. K. Hyde Chapter 27 (Moore, 5th Ed.) The following data set contains observations on the fish biomass of 26 streams. The potential regressors from which we wish to explain

More information

3 Variables: Cyberloafing Conscientiousness Age

3 Variables: Cyberloafing Conscientiousness Age title 'Cyberloafing, Mike Sage'; run; PROC CORR data=sage; var Cyberloafing Conscientiousness Age; run; quit; The CORR Procedure 3 Variables: Cyberloafing Conscientiousness Age Simple Statistics Variable

More information