The inductive effect in nitridosilicates and oxysilicates and its effects on 5d energy levels of Ce 3+

Size: px
Start display at page:

Download "The inductive effect in nitridosilicates and oxysilicates and its effects on 5d energy levels of Ce 3+"

Transcription

1 The inductive effect in nitridosilicates and oxysilicates and its effects on 5d energy levels of Ce 3+ Yuwei Kong, Zhen Song, Shuxin Wang, Zhiguo Xia and Quanlin Liu* The Beijing Municipal Key Laboratory of New Energy Materials and Technologies, School of Materials Sciences and Engineering, University of Science and Technology Beijing, Beijing , China Supporting information All the following results of statistical analysis of established models is calculated and output by SPSS software. Table S1 The statistical analytical results of equation (6) (a) /Removed a 1 µ χ b. Enter (b) Summary b R R Std. Error of the Estimate a b. Dependent Variable: WeightedAverageBondLength Sum of s df Mean F Sig. 1 Regression b Residual Total (d) a 1 (Constant) S1

2 µ χ Table S2 The statistical analytical results of equation (7) (a) /Removed a 1 WeightedAverag ebondlength b Removed Method. Enter (b) Summary b R R Std. Error of the Estimate a a. Predictors: (Constant), WeightedAverageBondLength Sum of s df Mean F Sig. 1 Regression b Residual Total b. Predictors: (Constant), WeightedAverageBondLength (d) a 95.0% Confidence Interval B Std. Error Beta t Sig. Lower Upper 1 (Constant) WeightedAverageBondLength Table S3 The statistical analytical results of equation (9) (a) /Removed a S2

3 1 µ χ b. Enter (b) Summary b R R Std. Error of the Estimate a Sum of s df Mean F Sig. 1 Regression b Residual Total (d) a 1 (Constant) µ χ Table S4 The statistical analytical results of equation (11) (a) /Removed a 1 µ χ b. Enter (b) Summary b Std. Error of the R R Estimate a S3

4 b. Dependent Variable: WeightedAverageBondLength Sum of s df Mean F Sig. 1 Regression b Residual Total (d) a 1 (Constant) µ χ Table S5 The statistical analytical results of equation (12) (a) /Removed a 1 WeightedAvera gebondlength b Removed. Enter Method (b) Summary b R R Std. Error of the Estimate a a. Predictors: (Constant), WeightedAverageBondLength Sum of s df Mean F Sig. 1 Regression b Residual Total S4

5 b. Predictors: (Constant), WeightedAverageBondLength (d) a 95.0% Confidence Interval B Std. Error Beta t Sig. Lower Upper 1 (Constant) WeightedAverageBondLength Table S6 The statistical analytical results of equation (13) (a) /Removed a 1 µ χ b. Enter (b) Summary b R R Std. Error of the Estimate a Sum of s df Mean F Sig. 1 Regression b Residual Total (d) a 1 (Constant) S5

6 µ χ S6

Multiple Regression. More Hypothesis Testing. More Hypothesis Testing The big question: What we really want to know: What we actually know: We know:

Multiple Regression. More Hypothesis Testing. More Hypothesis Testing The big question: What we really want to know: What we actually know: We know: Multiple Regression Ψ320 Ainsworth More Hypothesis Testing What we really want to know: Is the relationship in the population we have selected between X & Y strong enough that we can use the relationship

More information

Regression. Notes. Page 1. Output Created Comments 25-JAN :29:55

Regression. Notes. Page 1. Output Created Comments 25-JAN :29:55 REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) BCOV R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT favorability /METHOD=ENTER Zcontemp ZAnxious6 zallcontact. Regression Notes Output

More information

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

Interactions between Binary & Quantitative Predictors

Interactions between Binary & Quantitative Predictors Interactions between Binary & Quantitative Predictors The purpose of the study was to examine the possible joint effects of the difficulty of the practice task and the amount of practice, upon the performance

More information

Multivariate Correlational Analysis: An Introduction

Multivariate Correlational Analysis: An Introduction Assignment. Multivariate Correlational Analysis: An Introduction Mertler & Vanetta, Chapter 7 Kachigan, Chapter 4, pps 180-193 Terms you should know. Multiple Regression Linear Equations Least Squares

More information

Simple Linear Regression

Simple Linear Regression Simple Linear Regression 1 Correlation indicates the magnitude and direction of the linear relationship between two variables. Linear Regression: variable Y (criterion) is predicted by variable X (predictor)

More information

Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed)

Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed) Institutional Assessment Report Texas Southern University College of Pharmacy and Health Sciences "An Analysis of 2013 NAPLEX, P4-Comp. Exams and P3 courses The following analysis illustrates relationships

More information

*************NO YOGA!!!!!!!************************************.

*************NO YOGA!!!!!!!************************************. *************NO YOGA!!!!!!!************************************. temporary. select if human gt 1 and Q_TotalDuration gt 239 and subjectnum ne 672 and subj ectnum ne 115 and subjectnum ne 104 and subjectnum

More information

SPSS Output. ANOVA a b Residual Coefficients a Standardized Coefficients

SPSS Output. ANOVA a b Residual Coefficients a Standardized Coefficients SPSS Output Homework 1-1e ANOVA a Sum of Squares df Mean Square F Sig. 1 Regression 351.056 1 351.056 11.295.002 b Residual 932.412 30 31.080 Total 1283.469 31 a. Dependent Variable: Sexual Harassment

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

Levene's Test of Equality of Error Variances a

Levene's Test of Equality of Error Variances a BUTTERFAT DATA: INTERACTION MODEL Levene's Test of Equality of Error Variances a Dependent Variable: Butterfat (%) F df1 df2 Sig. 2.711 9 90.008 Tests the null hypothesis that the error variance of the

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

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

A discussion on multiple regression models

A discussion on multiple regression models A discussion on multiple regression models In our previous discussion of simple linear regression, we focused on a model in which one independent or explanatory variable X was used to predict the value

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

x3,..., Multiple Regression β q α, β 1, β 2, β 3,..., β q in the model can all be estimated by least square estimators

x3,..., Multiple Regression β q α, β 1, β 2, β 3,..., β q in the model can all be estimated by least square estimators Multiple Regression Relating a response (dependent, input) y to a set of explanatory (independent, output, predictor) variables x, x 2, x 3,, x q. A technique for modeling the relationship between variables.

More information

: The model hypothesizes a relationship between the variables. The simplest probabilistic model: or.

: The model hypothesizes a relationship between the variables. The simplest probabilistic model: or. Chapter Simple Linear Regression : comparing means across groups : presenting relationships among numeric variables. Probabilistic Model : The model hypothesizes an relationship between the variables.

More information

Univariate analysis. Simple and Multiple Regression. Univariate analysis. Simple Regression How best to summarise the data?

Univariate analysis. Simple and Multiple Regression. Univariate analysis. Simple Regression How best to summarise the data? Univariate analysis Example - linear regression equation: y = ax + c Least squares criteria ( yobs ycalc ) = yobs ( ax + c) = minimum Simple and + = xa xc xy xa + nc = y Solve for a and c Univariate analysis

More information

Advanced Quantitative Data Analysis

Advanced Quantitative Data Analysis Chapter 24 Advanced Quantitative Data Analysis Daniel Muijs Doing Regression Analysis in SPSS When we want to do regression analysis in SPSS, we have to go through the following steps: 1 As usual, we choose

More information

TOPIC 9 SIMPLE REGRESSION & CORRELATION

TOPIC 9 SIMPLE REGRESSION & CORRELATION TOPIC 9 SIMPLE REGRESSION & CORRELATION Basic Linear Relationships Mathematical representation: Y = a + bx X is the independent variable [the variable whose value we can choose, or the input variable].

More information

Example. Multiple Regression. Review of ANOVA & Simple Regression /749 Experimental Design for Behavioral and Social Sciences

Example. Multiple Regression. Review of ANOVA & Simple Regression /749 Experimental Design for Behavioral and Social Sciences 36-309/749 Experimental Design for Behavioral and Social Sciences Sep. 29, 2015 Lecture 5: Multiple Regression Review of ANOVA & Simple Regression Both Quantitative outcome Independent, Gaussian errors

More information

Bivariate Regression Analysis. The most useful means of discerning causality and significance of variables

Bivariate Regression Analysis. The most useful means of discerning causality and significance of variables Bivariate Regression Analysis The most useful means of discerning causality and significance of variables Purpose of Regression Analysis Test causal hypotheses Make predictions from samples of data Derive

More information

SPSS LAB FILE 1

SPSS LAB FILE  1 SPSS LAB FILE www.mcdtu.wordpress.com 1 www.mcdtu.wordpress.com 2 www.mcdtu.wordpress.com 3 OBJECTIVE 1: Transporation of Data Set to SPSS Editor INPUTS: Files: group1.xlsx, group1.txt PROCEDURE FOLLOWED:

More information

Chapter 4 Regression with Categorical Predictor Variables Page 1. Overview of regression with categorical predictors

Chapter 4 Regression with Categorical Predictor Variables Page 1. Overview of regression with categorical predictors Chapter 4 Regression with Categorical Predictor Variables Page. Overview of regression with categorical predictors 4-. Dummy coding 4-3 4-5 A. Karpinski Regression with Categorical Predictor Variables.

More information

WORKSHOP 3 Measuring Association

WORKSHOP 3 Measuring Association WORKSHOP 3 Measuring Association Concepts Analysing Categorical Data o Testing of Proportions o Contingency Tables & Tests o Odds Ratios Linear Association Measures o Correlation o Simple Linear Regression

More information

Chapter 9 - Correlation and Regression

Chapter 9 - Correlation and Regression Chapter 9 - Correlation and Regression 9. Scatter diagram of percentage of LBW infants (Y) and high-risk fertility rate (X ) in Vermont Health Planning Districts. 9.3 Correlation between percentage of

More information

The 1905 Einstein equation in a general mathematical analysis model of Quasars

The 1905 Einstein equation in a general mathematical analysis model of Quasars DePaul University From the SelectedWorks of Byron E. Bell Spring May 3, 2010 The 1905 Einstein equation in a general mathematical analysis model of Quasars Byron E. Bell Available at: https://works.bepress.com/byron_bell/2/

More information

Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons

Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons 1. Suppose we wish to assess the impact of five treatments on an outcome Y. How would these five treatments

More information

Scenario 5: Internet Usage Solution. θ j

Scenario 5: Internet Usage Solution. θ j Scenario : Internet Usage Solution Some more information would be interesting about the study in order to know if we can generalize possible findings. For example: Does each data point consist of the total

More information

Practical Biostatistics

Practical Biostatistics Practical Biostatistics Clinical Epidemiology, Biostatistics and Bioinformatics AMC Multivariable regression Day 5 Recap Describing association: Correlation Parametric technique: Pearson (PMCC) Non-parametric:

More information

FIN822 project 2 Project 2 contains part I and part II. (Due on November 10, 2008)

FIN822 project 2 Project 2 contains part I and part II. (Due on November 10, 2008) FIN822 project 2 Project 2 contains part I and part II. (Due on November 10, 2008) Part I Logit Model in Bankruptcy Prediction You do not believe in Altman and you decide to estimate the bankruptcy prediction

More information

( ), which of the coefficients would end

( ), which of the coefficients would end Discussion Sheet 29.7.9 Qualitative Variables We have devoted most of our attention in multiple regression to quantitative or numerical variables. MR models can become more useful and complex when we consider

More information

Multiple OLS Regression

Multiple OLS Regression Multiple OLS Regression Ronet Bachman, Ph.D. Presented by Justice Research and Statistics Association 12/8/2016 Justice Research and Statistics Association 720 7 th Street, NW, Third Floor Washington,

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

4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES

4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES 4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES FOR SINGLE FACTOR BETWEEN-S DESIGNS Planned or A Priori Comparisons We previously showed various ways to test all possible pairwise comparisons for

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

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

Ch 2: Simple Linear Regression

Ch 2: Simple Linear Regression Ch 2: Simple Linear Regression 1. Simple Linear Regression Model A simple regression model with a single regressor x is y = β 0 + β 1 x + ɛ, where we assume that the error ɛ is independent random component

More information

Lecture 18: Simple Linear Regression

Lecture 18: Simple Linear Regression Lecture 18: Simple Linear Regression BIOS 553 Department of Biostatistics University of Michigan Fall 2004 The Correlation Coefficient: r The correlation coefficient (r) is a number that measures the strength

More information

Model Building Chap 5 p251

Model Building Chap 5 p251 Model Building Chap 5 p251 Models with one qualitative variable, 5.7 p277 Example 4 Colours : Blue, Green, Lemon Yellow and white Row Blue Green Lemon Insects trapped 1 0 0 1 45 2 0 0 1 59 3 0 0 1 48 4

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

Stevens 2. Aufl. S Multivariate Tests c

Stevens 2. Aufl. S Multivariate Tests c Stevens 2. Aufl. S. 200 General Linear Model Between-Subjects Factors 1,00 2,00 3,00 N 11 11 11 Effect a. Exact statistic Pillai's Trace Wilks' Lambda Hotelling's Trace Roy's Largest Root Pillai's Trace

More information

IT 403 Practice Problems (2-2) Answers

IT 403 Practice Problems (2-2) Answers IT 403 Practice Problems (2-2) Answers #1. Which of the following is correct with respect to the correlation coefficient (r) and the slope of the leastsquares regression line (Choose one)? a. They will

More information

Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D.

Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D. Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D. Curve Fitting Mediation analysis Moderation Analysis 1 Curve Fitting The investigation of non-linear functions using

More information

EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS

EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS The data used in this example describe teacher and student behavior in 8 classrooms. The variables are: Y percentage of interventions

More information

Correlation and simple linear regression S5

Correlation and simple linear regression S5 Basic medical statistics for clinical and eperimental research Correlation and simple linear regression S5 Katarzyna Jóźwiak k.jozwiak@nki.nl November 15, 2017 1/41 Introduction Eample: Brain size and

More information

( ) *

( ) * : * ** : 189.. (r.m.moghadam@gmail.com) ( ) * ** (kemami@hotmail.com) 1391/1/20 : 1390/6/27 :.189-216 1391 . ( ) ( ).... :. 1391 4 190 : 191...... (1)... . (2).. 43 41 34 1379. :. (3). 38/7 28 50 15 8206

More information

Topic 1. Definitions

Topic 1. Definitions S Topic. Definitions. Scalar A scalar is a number. 2. Vector A vector is a column of numbers. 3. Linear combination A scalar times a vector plus a scalar times a vector, plus a scalar times a vector...

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

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

STATISTICS. Multiple regression

STATISTICS. Multiple regression STATISTICS Multiple regression Problem : Explain the price of a ski pass. 2 3 4 Model (Constant) nb pistes SPSS results Unstandardized Coefficients a. Dependent Variable: prix forfait jour Coefficients

More information

Interactions among Categorical Predictors

Interactions among Categorical Predictors Interactions among Categorical Predictors Today s Class: Reviewing significance tests Manual contrasts for categorical predictors Program-created contrasts for categorical predictors SPLH 861: Lecture

More information

SPSS Guide For MMI 409

SPSS Guide For MMI 409 SPSS Guide For MMI 409 by John Wong March 2012 Preface Hopefully, this document can provide some guidance to MMI 409 students on how to use SPSS to solve many of the problems covered in the D Agostino

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

McGill University. Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II. Final Examination

McGill University. Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II. Final Examination McGill University Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II Final Examination Date: 20th April 2009 Time: 9am-2pm Examiner: Dr David A Stephens Associate Examiner: Dr Russell Steele Please

More information

Multiple linear regression S6

Multiple linear regression S6 Basic medical statistics for clinical and experimental research Multiple linear regression S6 Katarzyna Jóźwiak k.jozwiak@nki.nl November 15, 2017 1/42 Introduction Two main motivations for doing multiple

More information

Chapter Goals. To understand the methods for displaying and describing relationship among variables. Formulate Theories.

Chapter Goals. To understand the methods for displaying and describing relationship among variables. Formulate Theories. Chapter Goals To understand the methods for displaying and describing relationship among variables. Formulate Theories Interpret Results/Make Decisions Collect Data Summarize Results Chapter 7: Is There

More information

Introduction to Regression

Introduction to Regression Regression Introduction to Regression If two variables covary, we should be able to predict the value of one variable from another. Correlation only tells us how much two variables covary. In regression,

More information

Unit 6 - Introduction to linear regression

Unit 6 - Introduction to linear regression Unit 6 - Introduction to linear regression Suggested reading: OpenIntro Statistics, Chapter 7 Suggested exercises: Part 1 - Relationship between two numerical variables: 7.7, 7.9, 7.11, 7.13, 7.15, 7.25,

More information

MATH ASSIGNMENT 2: SOLUTIONS

MATH ASSIGNMENT 2: SOLUTIONS MATH 204 - ASSIGNMENT 2: SOLUTIONS (a) Fitting the simple linear regression model to each of the variables in turn yields the following results: we look at t-tests for the individual coefficients, and

More information

UNIVERSITY OF MASSACHUSETTS Department of Mathematics and Statistics Basic Exam - Applied Statistics January, 2018

UNIVERSITY OF MASSACHUSETTS Department of Mathematics and Statistics Basic Exam - Applied Statistics January, 2018 UNIVERSITY OF MASSACHUSETTS Department of Mathematics and Statistics Basic Exam - Applied Statistics January, 2018 Work all problems. 60 points needed to pass at the Masters level, 75 to pass at the PhD

More information

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #6

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #6 STA 8 Applied Linear Models: Regression Analysis Spring 011 Solution for Homework #6 6. a) = 11 1 31 41 51 1 3 4 5 11 1 31 41 51 β = β1 β β 3 b) = 1 1 1 1 1 11 1 31 41 51 1 3 4 5 β = β 0 β1 β 6.15 a) Stem-and-leaf

More information

y ˆ i = ˆ " T u i ( i th fitted value or i th fit)

y ˆ i = ˆ  T u i ( i th fitted value or i th fit) 1 2 INFERENCE FOR MULTIPLE LINEAR REGRESSION Recall Terminology: p predictors x 1, x 2,, x p Some might be indicator variables for categorical variables) k-1 non-constant terms u 1, u 2,, u k-1 Each u

More information

Regression Analysis and Forecasting Prof. Shalabh Department of Mathematics and Statistics Indian Institute of Technology-Kanpur

Regression Analysis and Forecasting Prof. Shalabh Department of Mathematics and Statistics Indian Institute of Technology-Kanpur Regression Analysis and Forecasting Prof. Shalabh Department of Mathematics and Statistics Indian Institute of Technology-Kanpur Lecture 10 Software Implementation in Simple Linear Regression Model using

More information

Multiple Regression Analysis

Multiple Regression Analysis Multiple Regression Analysis Where as simple linear regression has 2 variables (1 dependent, 1 independent): y ˆ = a + bx Multiple linear regression has >2 variables (1 dependent, many independent): ˆ

More information

MATH 644: Regression Analysis Methods

MATH 644: Regression Analysis Methods MATH 644: Regression Analysis Methods FINAL EXAM Fall, 2012 INSTRUCTIONS TO STUDENTS: 1. This test contains SIX questions. It comprises ELEVEN printed pages. 2. Answer ALL questions for a total of 100

More information

1 Correlation and Inference from Regression

1 Correlation and Inference from Regression 1 Correlation and Inference from Regression Reading: Kennedy (1998) A Guide to Econometrics, Chapters 4 and 6 Maddala, G.S. (1992) Introduction to Econometrics p. 170-177 Moore and McCabe, chapter 12 is

More information

ECONOMETRIC ANALYSIS OF THE COMPANY ON STOCK EXCHANGE

ECONOMETRIC ANALYSIS OF THE COMPANY ON STOCK EXCHANGE ECONOMETRIC ANALYSIS OF THE COMPANY ON STOCK EXCHANGE Macovei Anamaria Geanina Ştefan cel Mare of University Suceava,Faculty of Economics and Public Administration street Universității, no. 3, city Suceava,

More information

The independent-means t-test:

The independent-means t-test: The independent-means t-test: Answers the question: is there a "real" difference between the two conditions in my experiment? Or is the difference due to chance? Previous lecture: (a) Dependent-means t-test:

More information

ST430 Exam 1 with Answers

ST430 Exam 1 with Answers ST430 Exam 1 with Answers Date: October 5, 2015 Name: Guideline: You may use one-page (front and back of a standard A4 paper) of notes. No laptop or textook are permitted but you may use a calculator.

More information

Example: 1982 State SAT Scores (First year state by state data available)

Example: 1982 State SAT Scores (First year state by state data available) Lecture 11 Review Section 3.5 from last Monday (on board) Overview of today s example (on board) Section 3.6, Continued: Nested F tests, review on board first Section 3.4: Interaction for quantitative

More information

Ordinary Least Squares Regression Explained: Vartanian

Ordinary Least Squares Regression Explained: Vartanian Ordinary Least Squares Regression Eplained: Vartanian When to Use Ordinary Least Squares Regression Analysis A. Variable types. When you have an interval/ratio scale dependent variable.. When your independent

More information

Lampiran 1. Hasil Determinasi Tanaman Umbi Singkong

Lampiran 1. Hasil Determinasi Tanaman Umbi Singkong Lampiran 1. Hasil Determinasi Tanaman Umbi Singkong 1 Lampiram 1. (Lanjutan) 2 Lampiran 1. (Lanjutan) 3 Lampiran 2. Certificate Of Analisis Dimenhidrinat 4 Lampiran 2. (Lanjutan) 5 Lampiran 2. (Lanjutan)

More information

9. Linear Regression and Correlation

9. Linear Regression and Correlation 9. Linear Regression and Correlation Data: y a quantitative response variable x a quantitative explanatory variable (Chap. 8: Recall that both variables were categorical) For example, y = annual income,

More information

Multiple linear regression

Multiple linear regression Multiple linear regression Course MF 930: Introduction to statistics June 0 Tron Anders Moger Department of biostatistics, IMB University of Oslo Aims for this lecture: Continue where we left off. Repeat

More information

Y (Nominal/Categorical) 1. Metric (interval/ratio) data for 2+ IVs, and categorical (nominal) data for a single DV

Y (Nominal/Categorical) 1. Metric (interval/ratio) data for 2+ IVs, and categorical (nominal) data for a single DV 1 Neuendorf Discriminant Analysis The Model X1 X2 X3 X4 DF2 DF3 DF1 Y (Nominal/Categorical) Assumptions: 1. Metric (interval/ratio) data for 2+ IVs, and categorical (nominal) data for a single DV 2. Linearity--in

More information

QUANTITATIVE STATISTICAL METHODS: REGRESSION AND FORECASTING JOHANNES LEDOLTER VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS ADMINISTRATION SPRING 2013

QUANTITATIVE STATISTICAL METHODS: REGRESSION AND FORECASTING JOHANNES LEDOLTER VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS ADMINISTRATION SPRING 2013 QUANTITATIVE STATISTICAL METHODS: REGRESSION AND FORECASTING JOHANNES LEDOLTER VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS ADMINISTRATION SPRING 3 Introduction Objectives of course: Regression and Forecasting

More information

Multiple Regression and Model Building Lecture 20 1 May 2006 R. Ryznar

Multiple Regression and Model Building Lecture 20 1 May 2006 R. Ryznar Multiple Regression and Model Building 11.220 Lecture 20 1 May 2006 R. Ryznar Building Models: Making Sure the Assumptions Hold 1. There is a linear relationship between the explanatory (independent) variable(s)

More information

Trends in Human Development Index of European Union

Trends in Human Development Index of European Union Trends in Human Development Index of European Union Department of Statistics, Hacettepe University, Beytepe, Ankara, Turkey spxl@hacettepe.edu.tr, deryacal@hacettepe.edu.tr Abstract: The Human Development

More information

One-Way ANOVA. Some examples of when ANOVA would be appropriate include:

One-Way ANOVA. Some examples of when ANOVA would be appropriate include: One-Way ANOVA 1. Purpose Analysis of variance (ANOVA) is used when one wishes to determine whether two or more groups (e.g., classes A, B, and C) differ on some outcome of interest (e.g., an achievement

More information

Parametric Test. Multiple Linear Regression Spatial Application I: State Homicide Rates Equations taken from Zar, 1984.

Parametric Test. Multiple Linear Regression Spatial Application I: State Homicide Rates Equations taken from Zar, 1984. Multiple Linear Regression Spatial Application I: State Homicide Rates Equations taken from Zar, 984. y ˆ = a + b x + b 2 x 2K + b n x n where n is the number of variables Example: In an earlier bivariate

More information

Determination of Gas Well Productivity by Logging Parameters

Determination of Gas Well Productivity by Logging Parameters Earth Science Research; Vol. 6, No. ; 017 ISSN 197-054 E-ISSN 197-0550 Published by Canadian Center of Science and Education Determination of Gas Well Productivity by Logging Parameters Weijun Hao 1, Zhihong

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

PSY 216. Assignment 9 Answers. Under what circumstances is a t statistic used instead of a z-score for a hypothesis test

PSY 216. Assignment 9 Answers. Under what circumstances is a t statistic used instead of a z-score for a hypothesis test PSY 216 Assignment 9 Answers 1. Problem 1 from the text Under what circumstances is a t statistic used instead of a z-score for a hypothesis test The t statistic should be used when the population standard

More information

4/22/2010. Test 3 Review ANOVA

4/22/2010. Test 3 Review ANOVA Test 3 Review ANOVA 1 School recruiter wants to examine if there are difference between students at different class ranks in their reported intensity of school spirit. What is the factor? How many levels

More information

" M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2

 M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2 Notation and Equations for Final Exam Symbol Definition X The variable we measure in a scientific study n The size of the sample N The size of the population M The mean of the sample µ The mean of the

More information

Area Classification of Surrounding Parking Facility Based on Land Use Functionality

Area Classification of Surrounding Parking Facility Based on Land Use Functionality Open Journal of Applied Sciences, 0,, 80-85 Published Online July 0 in SciRes. http://www.scirp.org/journal/ojapps http://dx.doi.org/0.4/ojapps.0.709 Area Classification of Surrounding Parking Facility

More information

Two-Way ANOVA. Chapter 15

Two-Way ANOVA. Chapter 15 Two-Way ANOVA Chapter 15 Interaction Defined An interaction is present when the effects of one IV depend upon a second IV Interaction effect : The effect of each IV across the levels of the other IV When

More information

Tables Table A Table B Table C Table D Table E 675

Tables Table A Table B Table C Table D Table E 675 BMTables.indd Page 675 11/15/11 4:25:16 PM user-s163 Tables Table A Standard Normal Probabilities Table B Random Digits Table C t Distribution Critical Values Table D Chi-square Distribution Critical Values

More information

Sociology 593 Exam 2 Answer Key March 28, 2002

Sociology 593 Exam 2 Answer Key March 28, 2002 Sociology 59 Exam Answer Key March 8, 00 I. True-False. (0 points) Indicate whether the following statements are true or false. If false, briefly explain why.. A variable is called CATHOLIC. This probably

More information

Extensions of One-Way ANOVA.

Extensions of One-Way ANOVA. Extensions of One-Way ANOVA http://www.pelagicos.net/classes_biometry_fa18.htm What do I want You to Know What are two main limitations of ANOVA? What two approaches can follow a significant ANOVA? How

More information

Class Notes: Week 8. Probit versus Logit Link Functions and Count Data

Class Notes: Week 8. Probit versus Logit Link Functions and Count Data Ronald Heck Class Notes: Week 8 1 Class Notes: Week 8 Probit versus Logit Link Functions and Count Data This week we ll take up a couple of issues. The first is working with a probit link function. While

More information

Intro to Linear Regression

Intro to Linear Regression Intro to Linear Regression Introduction to Regression Regression is a statistical procedure for modeling the relationship among variables to predict the value of a dependent variable from one or more predictor

More information

Linear Regression Model. Badr Missaoui

Linear Regression Model. Badr Missaoui Linear Regression Model Badr Missaoui Introduction What is this course about? It is a course on applied statistics. It comprises 2 hours lectures each week and 1 hour lab sessions/tutorials. We will focus

More information

Three Factor Completely Randomized Design with One Continuous Factor: Using SPSS GLM UNIVARIATE R. C. Gardner Department of Psychology

Three Factor Completely Randomized Design with One Continuous Factor: Using SPSS GLM UNIVARIATE R. C. Gardner Department of Psychology Data_Analysis.calm Three Factor Completely Randomized Design with One Continuous Factor: Using SPSS GLM UNIVARIATE R. C. Gardner Department of Psychology This article considers a three factor completely

More information

Multiple Regression. Peerapat Wongchaiwat, Ph.D.

Multiple Regression. Peerapat Wongchaiwat, Ph.D. Peerapat Wongchaiwat, Ph.D. wongchaiwat@hotmail.com The Multiple Regression Model Examine the linear relationship between 1 dependent (Y) & 2 or more independent variables (X i ) Multiple Regression Model

More information

Intro to Linear Regression

Intro to Linear Regression Intro to Linear Regression Introduction to Regression Regression is a statistical procedure for modeling the relationship among variables to predict the value of a dependent variable from one or more predictor

More information

Extensions of One-Way ANOVA.

Extensions of One-Way ANOVA. Extensions of One-Way ANOVA http://www.pelagicos.net/classes_biometry_fa17.htm What do I want You to Know What are two main limitations of ANOVA? What two approaches can follow a significant ANOVA? How

More information

Unit 6 - Simple linear regression

Unit 6 - Simple linear regression Sta 101: Data Analysis and Statistical Inference Dr. Çetinkaya-Rundel Unit 6 - Simple linear regression LO 1. Define the explanatory variable as the independent variable (predictor), and the response variable

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