Descriptive Statistics

Size: px
Start display at page:

Download "Descriptive Statistics"

Transcription

1 Lampiran 14. Descriptive Statistics N Range Min Max. Sum Dev. Var. Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Error PBM INKUIRI KONTROL Valid N (listwise) 20

2 Nonparametric Correlations Correlations PBM KONTROL Kendall's tau_b PBM Correlation Coefficient ** Sig. (2-tailed)..003 KONTROL Correlation Coefficient.558 ** Sig. (2-tailed).003. Spearman's rho PBM Correlation Coefficient ** Sig. (2-tailed)..001 KONTROL Correlation Coefficient.676 ** Sig. (2-tailed).001. **. Correlation is significant at the 0.01 level (2-tailed). Descriptive Statistics Deviation N PBM KONTROL Correlations PBM KONTROL PBM Pearson Correlation ** Sig. (2-tailed).002 KONTROL Pearson Correlation.652 ** 1 Sig. (2-tailed).002 **. Correlation is significant at the 0.01 level (2-tailed).

3 Nonparametric Correlations Correlations KONTROL INKUIRI Kendall's tau_b KONTROL Correlation Coefficient ** Sig. (2-tailed)..004 INKUIRI Correlation Coefficient.539 ** Sig. (2-tailed).004. Spearman's rho KONTROL Correlation Coefficient ** Sig. (2-tailed)..001 INKUIRI Correlation Coefficient.667 ** Sig. (2-tailed).001. **. Correlation is significant at the 0.01 level (2-tailed). Descriptive Statistics Deviation N KONTROL INKUIRI Correlations KONTROL INKUIRI KONTROL Pearson Correlation ** Sig. (2-tailed).003 INKUIRI Pearson Correlation.621 ** 1 Sig. (2-tailed).003 **. Correlation is significant at the 0.01 level (2-tailed).

4 Nonparametric Correlations Correlations KONTROL INKUIRI Kendall's tau_b KONTROL Correlation Coefficient ** Sig. (2-tailed)..004 INKUIRI Correlation Coefficient.539 ** Sig. (2-tailed).004. Spearman's rho KONTROL Correlation Coefficient ** Sig. (2-tailed)..001 INKUIRI Correlation Coefficient.667 ** Sig. (2-tailed).001. **. Correlation is significant at the 0.01 level (2-tailed). Descriptive Statistics Deviation N KONTROL INKUIRI Correlations KONTROL INKUIRI KONTROL Pearson Correlation ** Sig. (2-tailed).003 INKUIRI Pearson Correlation.621 ** 1 Sig. (2-tailed).003 **. Correlation is significant at the 0.01 level (2-tailed).

5 Nonparametric Correlations Correlations INKUIRI PBM Kendall's tau_b INKUIRI Correlation Coefficient * Sig. (2-tailed)..020 PBM Correlation Coefficient.430 * Sig. (2-tailed).020. Spearman's rho INKUIRI Correlation Coefficient * Sig. (2-tailed)..013 PBM Correlation Coefficient.543 * Sig. (2-tailed).013. *. Correlation is significant at the 0.05 level (2-tailed). Descriptive Statistics Deviation N INKUIRI PBM

6 Correlations INKUIRI PBM INKUIRI Pearson Correlation * Sig. (2-tailed).017 PBM Pearson Correlation.529 * 1 Sig. (2-tailed).017 *. Correlation is significant at the 0.05 level (2-tailed).

7 NPar Tests Descriptive Statistics N Deviation Minimum Maximum INKUIRI PBM KONTROL One-Sample Kolmogorov-Smirnov Test INKUIRI PBM KONTROL 20 Normal Parameters a Deviation Most Extreme Differences Absolute Positive Negative Kolmogorov-Smirnov Z Asymp. Sig. (2-tailed) a. Test distribution is Normal.

8 Crosstabs PBM * KONTROL Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent % 0.0% % PBM * KONTROL Crosstabulation Count KONTROL Total PBM Total Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases a. 30 cells (100.0%) have expected count less than 5. The minimum expected count is.10.

9 Crosstabs INKUIRI * KONTROL Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent % 0.0% % INKUIRI * KONTROL Crosstabulation Count KONTROL Total INKUIRI Total Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases a. 36 cells (100.0%) have expected count less than 5. The minimum expected count is.05.

10 Crosstabs PBM * INKUIRI Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent % 0.0% % PBM * INKUIRI Crosstabulation Count INKUIRI Total PBM Total Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases a. 30 cells (100.0%) have expected count less than 5. The minimum expected count is.10.

11 Paired Samples Test Deviation Paired Differences Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2-tailed) Pair 1 PBM - Kontrol Pair 1 Inkuiri - Kontrol Paired Samples Test Deviation Paired Differences Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 PBM - Inkuiri Paired Samples Test Paired Differences Deviation Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed)

12 T-Test ( Pretes Dan Postes Kelas Inkuiri) Paired Samples Statistics N Deviation Error Pair 1 PretesINKU PostesINKU Paired Samples Correlations N Correlation Sig. Pair 1 PretesINKU & PostesINKU Paired Samples Test Paired Differences 95% Confidence Interval of the Error Difference Sig. (2- Deviation Lower Upper t df tailed) Pair 1 PretesINKU - PostesINKU

13 T-Test ( Pretes dan Postes Kelas PBM) Paired Samples Statistics N Deviation Error Pair 1 PretesPBM PostesPBM Paired Samples Correlations N Correlation Sig. Pair 1 PretesPBM & PostesPBM Paired Samples Test Paired Differences 95% Confidence Interval of the Error Difference Sig. (2- Deviation Lower Upper t df tailed) Pair 1 PretesPBM - PostesPBM

14 T-Test Kelas PBM dan Inkuiri Paired Samples Statistics N Deviation Error Pair 1 PBM Inkuiri Paired Samples Correlations N Correlation Sig. Pair 1 PBM & Inkuiri Paired Samples Test Paired Differences 95% Confidence Deviation Error Interval of the Difference t df Sig. (2-tailed) Lower Upper Pair 1 PBM - Inkuiri

15 T-Test Kelas PBM dan Kelas Kontrol Paired Samples Statistics N Deviation Error Pair 1 PBM Kontrol Paired Samples Correlations N Correlation Sig. Pair 1 PBM & Kontrol Paired Samples Test Paired Differences 95% Confidence Deviation Error Interval of the Difference t df Sig. (2-tailed) Lower Upper Pair 1 PBM - Kontrol

16 T-Test Kelas Inkuiri dan Kelas Kontrol Paired Samples Statistics N Deviation Error Pair 1 Inkuiri Kontrol Paired Samples Correlations N Correlation Sig. Pair 1 Inkuiri & Kontrol Paired Samples Test Paired Differences 95% Confidence Deviation Error Interval of the Difference t df Sig. (2-tailed) Lower Upper Pair 1 Inkuiri - Kontrol

17 Regression Descriptive Statistics Deviation N KONTROL PBM INKUIRI Correlations KONTROL PBM INKUIRI Pearson Correlation KONTROL PBM INKUIRI Sig. (1-tailed) KONTROL PBM INKUIRI N KONTROL PBM INKUIRI Variables Entered/Removed b Variables Model Variables Entered Removed Method 1 INKUIRI, PBM a. Enter a. All requested variables entered. b. Dependent Variable: KONTROL

18 Model Summary b Model R R Square Adjusted R Square Error of the Estimate a a. Predictors: (Constant), INKUIRI, PBM b. Dependent Variable: KONTROL ANOVA b Model Sum of Squares df Square F Sig. 1 Regression a Residual Total a. Predictors: (Constant), INKUIRI, PBM b. Dependent Variable: KONTROL Coefficients a Unstandardized Coefficients Standardized Coefficients Model B Error Beta t Sig. 1 (Constant) PBM INKUIRI a. Dependent Variable: KONTROL Residuals Statistics a Minimum Maximum Deviation N Predicted Value Residual Predicted Value Residual a. Dependent Variable: KONTROL

19 Charts

20

21

22

Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear.

Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear. Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear. In the main dialog box, input the dependent variable and several predictors.

More information

SPSS Guide: Regression Analysis

SPSS Guide: Regression Analysis SPSS Guide: Regression Analysis I put this together to give you a step-by-step guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar

More information

SPSS TUTORIAL & EXERCISE BOOK

SPSS TUTORIAL & EXERCISE BOOK UNIVERSITY OF MISKOLC Faculty of Economics Institute of Business Information and Methods Department of Business Statistics and Economic Forecasting PETRA PETROVICS SPSS TUTORIAL & EXERCISE BOOK FOR BUSINESS

More information

Projects Involving Statistics (& SPSS)

Projects Involving Statistics (& SPSS) Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,

More information

Chapter 13 Introduction to Linear Regression and Correlation Analysis

Chapter 13 Introduction to Linear Regression and Correlation Analysis Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing

More information

Data Analysis for Marketing Research - Using SPSS

Data Analysis for Marketing Research - Using SPSS North South University, School of Business MKT 63 Marketing Research Instructor: Mahmood Hussain, PhD Data Analysis for Marketing Research - Using SPSS Introduction In this part of the class, we will learn

More information

Univariate Regression

Univariate Regression Univariate Regression Correlation and Regression The regression line summarizes the linear relationship between 2 variables Correlation coefficient, r, measures strength of relationship: the closer r is

More information

The Dummy s Guide to Data Analysis Using SPSS

The Dummy s Guide to Data Analysis Using SPSS The Dummy s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001 Amy Gamble 4/30/01 All Rights Rerserved TABLE OF CONTENTS PAGE Helpful Hints for All Tests...1 Tests

More information

Final Exam Practice Problem Answers

Final Exam Practice Problem Answers Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal

More information

Chapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS

Chapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS Chapter Seven Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS Section : An introduction to multiple regression WHAT IS MULTIPLE REGRESSION? Multiple

More information

1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96

1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96 1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years

More information

EPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST

EPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeated-measures data if participants are assessed on two occasions or conditions

More information

business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar

business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel

More information

SPSS Tests for Versions 9 to 13

SPSS Tests for Versions 9 to 13 SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list

More information

Simple Linear Regression, Scatterplots, and Bivariate Correlation

Simple Linear Regression, Scatterplots, and Bivariate Correlation 1 Simple Linear Regression, Scatterplots, and Bivariate Correlation This section covers procedures for testing the association between two continuous variables using the SPSS Regression and Correlate analyses.

More information

Data Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.

Data Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds. Sept 03-23-05 22 2005 Data Mining for Model Creation Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.com page 1 Agenda Data Mining and Estimating Model Creation

More information

Outline. Topic 4 - Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares

Outline. Topic 4 - Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares Topic 4 - Analysis of Variance Approach to Regression Outline Partitioning sums of squares Degrees of freedom Expected mean squares General linear test - Fall 2013 R 2 and the coefficient of correlation

More information

containing Kendall correlations; and the OUTH = option will create a data set containing Hoeffding statistics.

containing Kendall correlations; and the OUTH = option will create a data set containing Hoeffding statistics. Getting Correlations Using PROC CORR Correlation analysis provides a method to measure the strength of a linear relationship between two numeric variables. PROC CORR can be used to compute Pearson product-moment

More information

Nonparametric Statistics

Nonparametric Statistics Nonparametric Statistics J. Lozano University of Goettingen Department of Genetic Epidemiology Interdisciplinary PhD Program in Applied Statistics & Empirical Methods Graduate Seminar in Applied Statistics

More information

Simple linear regression

Simple linear regression Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between

More information

Multiple Regression. Page 24

Multiple Regression. Page 24 Multiple Regression Multiple regression is an extension of simple (bi-variate) regression. The goal of multiple regression is to enable a researcher to assess the relationship between a dependent (predicted)

More information

" Y. Notation and Equations for Regression Lecture 11/4. Notation:

 Y. Notation and Equations for Regression Lecture 11/4. Notation: Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through

More information

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm

More information

Data analysis process

Data analysis process Data analysis process Data collection and preparation Collect data Prepare codebook Set up structure of data Enter data Screen data for errors Exploration of data Descriptive Statistics Graphs Analysis

More information

Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk

Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:

More information

Directions for using SPSS

Directions for using SPSS Directions for using SPSS Table of Contents Connecting and Working with Files 1. Accessing SPSS... 2 2. Transferring Files to N:\drive or your computer... 3 3. Importing Data from Another File Format...

More information

SIMPLE LINEAR CORRELATION. r can range from -1 to 1, and is independent of units of measurement. Correlation can be done on two dependent variables.

SIMPLE LINEAR CORRELATION. r can range from -1 to 1, and is independent of units of measurement. Correlation can be done on two dependent variables. SIMPLE LINEAR CORRELATION Simple linear correlation is a measure of the degree to which two variables vary together, or a measure of the intensity of the association between two variables. Correlation

More information

Introduction to Quantitative Methods

Introduction to Quantitative Methods Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................

More information

Basic Statistics and Data Analysis for Health Researchers from Foreign Countries

Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association

More information

SPSS Guide How-to, Tips, Tricks & Statistical Techniques

SPSS Guide How-to, Tips, Tricks & Statistical Techniques SPSS Guide How-to, Tips, Tricks & Statistical Techniques Support for the course Research Methodology for IB Also useful for your BSc or MSc thesis March 2014 Dr. Marijke Leliveld Jacob Wiebenga, MSc CONTENT

More information

Simple Linear Regression Inference

Simple Linear Regression Inference Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation

More information

1.1. Simple Regression in Excel (Excel 2010).

1.1. Simple Regression in Excel (Excel 2010). .. Simple Regression in Excel (Excel 200). To get the Data Analysis tool, first click on File > Options > Add-Ins > Go > Select Data Analysis Toolpack & Toolpack VBA. Data Analysis is now available under

More information

A Basic Guide to Analyzing Individual Scores Data with SPSS

A Basic Guide to Analyzing Individual Scores Data with SPSS A Basic Guide to Analyzing Individual Scores Data with SPSS Step 1. Clean the data file Open the Excel file with your data. You may get the following message: If you get this message, click yes. Delete

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

More information

Chapter 23. Inferences for Regression

Chapter 23. Inferences for Regression Chapter 23. Inferences for Regression Topics covered in this chapter: Simple Linear Regression Simple Linear Regression Example 23.1: Crying and IQ The Problem: Infants who cry easily may be more easily

More information

6 Variables: PD MF MA K IAH SBS

6 Variables: PD MF MA K IAH SBS options pageno=min nodate formdlim='-'; title 'Canonical Correlation, Journal of Interpersonal Violence, 10: 354-366.'; data SunitaPatel; infile 'C:\Users\Vati\Documents\StatData\Sunita.dat'; input Group

More information

Statistical tests for SPSS

Statistical tests for SPSS Statistical tests for SPSS Paolo Coletti A.Y. 2010/11 Free University of Bolzano Bozen Premise This book is a very quick, rough and fast description of statistical tests and their usage. It is explicitly

More information

Factor Analysis. Principal components factor analysis. Use of extracted factors in multivariate dependency models

Factor Analysis. Principal components factor analysis. Use of extracted factors in multivariate dependency models Factor Analysis Principal components factor analysis Use of extracted factors in multivariate dependency models 2 KEY CONCEPTS ***** Factor Analysis Interdependency technique Assumptions of factor analysis

More information

Student debt from higher education attendance is an increasingly troubling problem in the

Student debt from higher education attendance is an increasingly troubling problem in the Morrie Swerlick Student Debt Policy Memo 2/23/2012 Student debt from higher education attendance is an increasingly troubling problem in the United States. Due to rising costs and shrinking state expenditures,

More information

Regression Analysis: A Complete Example

Regression Analysis: A Complete Example Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc, Inc./Getty

More information

Doing Multiple Regression with SPSS. In this case, we are interested in the Analyze options so we choose that menu. If gives us a number of choices:

Doing Multiple Regression with SPSS. In this case, we are interested in the Analyze options so we choose that menu. If gives us a number of choices: Doing Multiple Regression with SPSS Multiple Regression for Data Already in Data Editor Next we want to specify a multiple regression analysis for these data. The menu bar for SPSS offers several options:

More information

Using Excel for inferential statistics

Using Excel for inferential statistics FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied

More information

Generalized Linear Models

Generalized Linear Models Generalized Linear Models We have previously worked with regression models where the response variable is quantitative and normally distributed. Now we turn our attention to two types of models where the

More information

SPSS Resources. 1. See website (readings) for SPSS tutorial & Stats handout

SPSS Resources. 1. See website (readings) for SPSS tutorial & Stats handout Analyzing Data SPSS Resources 1. See website (readings) for SPSS tutorial & Stats handout Don t have your own copy of SPSS? 1. Use the libraries to analyze your data 2. Download a trial version of SPSS

More information

DAFTAR PUSTAKA. Arifin Ali, 2002, Membaca Saham, Edisi I, Yogyakarta : Andi. Bapepam, 2004, Ringkasan Data Perusahaan, Jakarta : Bapepam

DAFTAR PUSTAKA. Arifin Ali, 2002, Membaca Saham, Edisi I, Yogyakarta : Andi. Bapepam, 2004, Ringkasan Data Perusahaan, Jakarta : Bapepam 03 DAFTAR PUSTAKA Arifin Ali, 00, Membaca Saham, Edisi I, Yogyakarta : Andi Bapepam, 004, Ringkasan Data Perusahaan, Jakarta : Bapepam Darmadji Tjiptono dan Fakhruddin M Hendy, 006, Pasar Modal di Indonesia,

More information

Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1

Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1 Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1 Calculate counts, means, and standard deviations Produce

More information

The importance of graphing the data: Anscombe s regression examples

The importance of graphing the data: Anscombe s regression examples The importance of graphing the data: Anscombe s regression examples Bruce Weaver Northern Health Research Conference Nipissing University, North Bay May 30-31, 2008 B. Weaver, NHRC 2008 1 The Objective

More information

Week TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500 6 8480

Week TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500 6 8480 1) The S & P/TSX Composite Index is based on common stock prices of a group of Canadian stocks. The weekly close level of the TSX for 6 weeks are shown: Week TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500

More information

Chapter 2 Probability Topics SPSS T tests

Chapter 2 Probability Topics SPSS T tests Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the One-Sample T test has been explained. In this handout, we also give the SPSS methods to perform

More information

11. Analysis of Case-control Studies Logistic Regression

11. Analysis of Case-control Studies Logistic Regression Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:

More information

RECRUITERS PRIORITIES IN PLACING MBA FRESHER: AN EMPIRICAL ANALYSIS

RECRUITERS PRIORITIES IN PLACING MBA FRESHER: AN EMPIRICAL ANALYSIS RECRUITERS PRIORITIES IN PLACING MBA FRESHER: AN EMPIRICAL ANALYSIS Miss Sangeeta Mohanty Assistant Professor, Academy of Business Administration, Angaragadia, Balasore, Orissa, India ABSTRACT Recruitment

More information

KSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management

KSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management KSTAT MINI-MANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To

More information

EXPLORATORY DATA ANALYSIS: GETTING TO KNOW YOUR DATA

EXPLORATORY DATA ANALYSIS: GETTING TO KNOW YOUR DATA EXPLORATORY DATA ANALYSIS: GETTING TO KNOW YOUR DATA Michael A. Walega Covance, Inc. INTRODUCTION In broad terms, Exploratory Data Analysis (EDA) can be defined as the numerical and graphical examination

More information

Student Guide to SPSS Barnard College Department of Biological Sciences

Student Guide to SPSS Barnard College Department of Biological Sciences Student Guide to SPSS Barnard College Department of Biological Sciences Dan Flynn Table of Contents Introduction... 2 Basics... 4 Starting SPSS... 4 Navigating... 4 Data Editor... 5 SPSS Viewer... 6 Getting

More information

A SUCCESFULL WAY TO SELL HANDICRAFTS IN ALBANIA

A SUCCESFULL WAY TO SELL HANDICRAFTS IN ALBANIA International Journal of Economics, Commerce and Management United Kingdom Vol. III, Issue 4, April 2015 http://ijecm.co.uk/ ISSN 2348 0386 A SUCCESFULL WAY TO SELL HANDICRAFTS IN ALBANIA EMPIRICAL EXPLORATION

More information

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and

More information

The Statistics Tutor s Quick Guide to

The Statistics Tutor s Quick Guide to statstutor community project encouraging academics to share statistics support resources All stcp resources are released under a Creative Commons licence The Statistics Tutor s Quick Guide to Stcp-marshallowen-7

More information

Chapter 7: Simple linear regression Learning Objectives

Chapter 7: Simple linear regression Learning Objectives Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) -

More information

An introduction to IBM SPSS Statistics

An introduction to IBM SPSS Statistics An introduction to IBM SPSS Statistics Contents 1 Introduction... 1 2 Entering your data... 2 3 Preparing your data for analysis... 10 4 Exploring your data: univariate analysis... 14 5 Generating descriptive

More information

An SPSS companion book. Basic Practice of Statistics

An SPSS companion book. Basic Practice of Statistics An SPSS companion book to Basic Practice of Statistics SPSS is owned by IBM. 6 th Edition. Basic Practice of Statistics 6 th Edition by David S. Moore, William I. Notz, Michael A. Flinger. Published by

More information

The correlation coefficient

The correlation coefficient The correlation coefficient Clinical Biostatistics The correlation coefficient Martin Bland Correlation coefficients are used to measure the of the relationship or association between two quantitative

More information

DATA ANALYSIS. QEM Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University

DATA ANALYSIS. QEM Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University DATA ANALYSIS QEM Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University Quantitative Research What is Statistics? Statistics (as a subject) is the science

More information

Predictability Study of ISIP Reading and STAAR Reading: Prediction Bands. March 2014

Predictability Study of ISIP Reading and STAAR Reading: Prediction Bands. March 2014 Predictability Study of ISIP Reading and STAAR Reading: Prediction Bands March 2014 Chalie Patarapichayatham 1, Ph.D. William Fahle 2, Ph.D. Tracey R. Roden 3, M.Ed. 1 Research Assistant Professor in the

More information

COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES.

COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. 277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies

More information

THE KRUSKAL WALLLIS TEST

THE KRUSKAL WALLLIS TEST THE KRUSKAL WALLLIS TEST TEODORA H. MEHOTCHEVA Wednesday, 23 rd April 08 THE KRUSKAL-WALLIS TEST: The non-parametric alternative to ANOVA: testing for difference between several independent groups 2 NON

More information

UNIVERSITY OF NAIROBI

UNIVERSITY OF NAIROBI UNIVERSITY OF NAIROBI MASTERS IN PROJECT PLANNING AND MANAGEMENT NAME: SARU CAROLYNN ELIZABETH REGISTRATION NO: L50/61646/2013 COURSE CODE: LDP 603 COURSE TITLE: RESEARCH METHODS LECTURER: GAKUU CHRISTOPHER

More information

An introduction to using Microsoft Excel for quantitative data analysis

An introduction to using Microsoft Excel for quantitative data analysis Contents An introduction to using Microsoft Excel for quantitative data analysis 1 Introduction... 1 2 Why use Excel?... 2 3 Quantitative data analysis tools in Excel... 3 4 Entering your data... 6 5 Preparing

More information

Section 3 Part 1. Relationships between two numerical variables

Section 3 Part 1. Relationships between two numerical variables Section 3 Part 1 Relationships between two numerical variables 1 Relationship between two variables The summary statistics covered in the previous lessons are appropriate for describing a single variable.

More information

DESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.

DESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,

More information

Chapter 5 Analysis of variance SPSS Analysis of variance

Chapter 5 Analysis of variance SPSS Analysis of variance Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,

More information

CHAPTER 14 ORDINAL MEASURES OF CORRELATION: SPEARMAN'S RHO AND GAMMA

CHAPTER 14 ORDINAL MEASURES OF CORRELATION: SPEARMAN'S RHO AND GAMMA CHAPTER 14 ORDINAL MEASURES OF CORRELATION: SPEARMAN'S RHO AND GAMMA Chapter 13 introduced the concept of correlation statistics and explained the use of Pearson's Correlation Coefficient when working

More information

Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables 2

Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables 2 Lesson 4 Part 1 Relationships between two numerical variables 1 Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables

More information

Chapter 13. Chi-Square. Crosstabs and Nonparametric Tests. Specifically, we demonstrate procedures for running two separate

Chapter 13. Chi-Square. Crosstabs and Nonparametric Tests. Specifically, we demonstrate procedures for running two separate 1 Chapter 13 Chi-Square This section covers the steps for running and interpreting chi-square analyses using the SPSS Crosstabs and Nonparametric Tests. Specifically, we demonstrate procedures for running

More information

The Chi-Square Test. STAT E-50 Introduction to Statistics

The Chi-Square Test. STAT E-50 Introduction to Statistics STAT -50 Introduction to Statistics The Chi-Square Test The Chi-square test is a nonparametric test that is used to compare experimental results with theoretical models. That is, we will be comparing observed

More information

Regression step-by-step using Microsoft Excel

Regression step-by-step using Microsoft Excel Step 1: Regression step-by-step using Microsoft Excel Notes prepared by Pamela Peterson Drake, James Madison University Type the data into the spreadsheet The example used throughout this How to is a regression

More information

This chapter will demonstrate how to perform multiple linear regression with IBM SPSS

This chapter will demonstrate how to perform multiple linear regression with IBM SPSS CHAPTER 7B Multiple Regression: Statistical Methods Using IBM SPSS This chapter will demonstrate how to perform multiple linear regression with IBM SPSS first using the standard method and then using the

More information

Examining a Fitted Logistic Model

Examining a Fitted Logistic Model STAT 536 Lecture 16 1 Examining a Fitted Logistic Model Deviance Test for Lack of Fit The data below describes the male birth fraction male births/total births over the years 1931 to 1990. A simple logistic

More information

Influence of Aggressivenessand Conservativenessin Investing and Financing Policies on Performance of Industrial Firms in Kenya

Influence of Aggressivenessand Conservativenessin Investing and Financing Policies on Performance of Industrial Firms in Kenya IOSR Journal of Economics and Finance (IOSR-JEF) e-issn: 2321-5933, p-issn: 2321-5925.Volume 2, Issue 5 (Jan. 2014), PP 27-32 Influence of Aggressivenessand Conservativenessin Investing and Financing Policies

More information

5. Linear Regression

5. Linear Regression 5. Linear Regression Outline.................................................................... 2 Simple linear regression 3 Linear model............................................................. 4

More information

We are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries?

We are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries? Statistics: Correlation Richard Buxton. 2008. 1 Introduction We are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries? Do

More information

SPSS-Applications (Data Analysis)

SPSS-Applications (Data Analysis) CORTEX fellows training course, University of Zurich, October 2006 Slide 1 SPSS-Applications (Data Analysis) Dr. Jürg Schwarz, juerg.schwarz@schwarzpartners.ch Program 19. October 2006: Morning Lessons

More information

Basic Statistical and Modeling Procedures Using SAS

Basic Statistical and Modeling Procedures Using SAS Basic Statistical and Modeling Procedures Using SAS One-Sample Tests The statistical procedures illustrated in this handout use two datasets. The first, Pulse, has information collected in a classroom

More information

Part 2: Analysis of Relationship Between Two Variables

Part 2: Analysis of Relationship Between Two Variables Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable

More information

STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE

STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE Perhaps Microsoft has taken pains to hide some of the most powerful tools in Excel. These add-ins tools work on top of Excel, extending its power and abilities

More information

January 26, 2009 The Faculty Center for Teaching and Learning

January 26, 2009 The Faculty Center for Teaching and Learning THE BASICS OF DATA MANAGEMENT AND ANALYSIS A USER GUIDE January 26, 2009 The Faculty Center for Teaching and Learning THE BASICS OF DATA MANAGEMENT AND ANALYSIS Table of Contents Table of Contents... i

More information

Ordinal Regression. Chapter

Ordinal Regression. Chapter Ordinal Regression Chapter 4 Many variables of interest are ordinal. That is, you can rank the values, but the real distance between categories is unknown. Diseases are graded on scales from least severe

More information

How To Find Out What Is The Recipe For A Bank Loan

How To Find Out What Is The Recipe For A Bank Loan BANK S LENDING DECISION TO THE INDUSTRIAL SECTORS Ms.Sangeeta Mohanty Assistant Professor, Academy of Business Administration, Industrial Estate (S1/25) Angaragadia, Balasore, Orissa (756001) Abstract

More information

Research Methods & Experimental Design

Research Methods & Experimental Design Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and

More information

Table of Contents. Preface

Table of Contents. Preface Table of Contents Preface Chapter 1: Introduction 1-1 Opening an SPSS Data File... 2 1-2 Viewing the SPSS Screens... 3 o Data View o Variable View o Output View 1-3 Reading Non-SPSS Files... 6 o Convert

More information

Advertising value of mobile marketing through acceptance among youth in Karachi

Advertising value of mobile marketing through acceptance among youth in Karachi MPRA Munich Personal RePEc Archive Advertising value of mobile marketing through acceptance among youth in Karachi Suleman Syed Akbar and Rehan Azam and Danish Muhammad IQRA UNIVERSITY 1. September 2012

More information

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in

More information

IBM SPSS Bootstrapping 22

IBM SPSS Bootstrapping 22 IBM SPSS Bootstrapping 22 Note Before using this information and the product it supports, read the information in Notices on page 7. Product Information This edition applies to version 22, release 0, modification

More information

Using Excel for Statistical Analysis

Using Excel for Statistical Analysis Using Excel for Statistical Analysis You don t have to have a fancy pants statistics package to do many statistical functions. Excel can perform several statistical tests and analyses. First, make sure

More information

Data driven approach in analyzing energy consumption data in buildings. Office of Environmental Sustainability Ian Tan

Data driven approach in analyzing energy consumption data in buildings. Office of Environmental Sustainability Ian Tan Data driven approach in analyzing energy consumption data in buildings Office of Environmental Sustainability Ian Tan Background Real time energy consumption data of buildings in terms of electricity (kwh)

More information

Data Mining. for Process Improvement DATA MINING. Paul Below, Quantitative Software Management, Inc. (QSM)

Data Mining. for Process Improvement DATA MINING. Paul Below, Quantitative Software Management, Inc. (QSM) Data mining techniques can be used to help thin out the forest so that we can examine the important trees. Hopefully, this article will encourage you to learn more about data mining, try some of the techniques

More information

Introduction to Regression and Data Analysis

Introduction to Regression and Data Analysis Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it

More information

Study Guide for the Final Exam

Study Guide for the Final Exam Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make

More information

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a

More information

MASTER COURSE SYLLABUS-PROTOTYPE PSYCHOLOGY 2317 STATISTICAL METHODS FOR THE BEHAVIORAL SCIENCES

MASTER COURSE SYLLABUS-PROTOTYPE PSYCHOLOGY 2317 STATISTICAL METHODS FOR THE BEHAVIORAL SCIENCES MASTER COURSE SYLLABUS-PROTOTYPE THE PSYCHOLOGY DEPARTMENT VALUES ACADEMIC FREEDOM AND THUS OFFERS THIS MASTER SYLLABUS-PROTOTYPE ONLY AS A GUIDE. THE INSTRUCTORS ARE FREE TO ADAPT THEIR COURSE SYLLABI

More information