Analyzing the Relationship Between Two Variables

Slides:



Advertisements
Similar presentations
Selecting a Data Analysis Technique: The First Steps
Advertisements

Dr. Satyendra Singh, Department of Adminstrative Studies Welcome to the Class of Bivariate Data Analysis.
 Will help you gain knowledge in: ◦ Improving performance characteristics ◦ Reducing costs ◦ Understand regression analysis ◦ Understand relationships.
CORRELATION. Overview of Correlation u What is a Correlation? u Correlation Coefficients u Coefficient of Determination u Test for Significance u Correlation.
Wednesday AM  Presentation of yesterday’s results  Associations  Correlation  Linear regression  Applications: reliability.
FACTORIAL ANOVA Overview of Factorial ANOVA Factorial Designs Types of Effects Assumptions Analyzing the Variance Regression Equation Fixed and Random.
Association Between Two Variables Measured at the Nominal Level
Inference for Linear Regression (C27 BVD). * If we believe two variables may have a linear relationship, we may find a linear regression line to model.
SPSS Session 5: Association between Nominal Variables Using Chi-Square Statistic.
Chapter 13 Analyzing Quantitative data. LEVELS OF MEASUREMENT Nominal Measurement Ordinal Measurement Interval Measurement Ratio Measurement.
Statistics for the Social Sciences Psychology 340 Fall 2006 Putting it all together.
Chapter 14 Analyzing Quantitative Data. LEVELS OF MEASUREMENT Nominal Measurement Nominal Measurement Ordinal Measurement Ordinal Measurement Interval.
Session 7.1 Bivariate Data Analysis
Chapter 3 Summarizing Descriptive Relationships ©.
Analyzing quantitative data – section III Week 10 Lecture 1.
Measures of Association Deepak Khazanchi Chapter 18.
Statistics for the Social Sciences Psychology 340 Spring 2005 Course Review.
5-1 Two Discrete Random Variables Example Two Discrete Random Variables Figure 5-1 Joint probability distribution of X and Y in Example 5-1.
5-1 Two Discrete Random Variables Example Two Discrete Random Variables Figure 5-1 Joint probability distribution of X and Y in Example 5-1.
Correlation Question 1 This question asks you to use the Pearson correlation coefficient to measure the association between [educ4] and [empstat]. However,
Correlation Coefficient Correlation coefficient refers to the type of relationship between variables that allows one to make predications from one variable.
Analyzing Data: Bivariate Relationships Chapter 7.
LIS 570 Summarising and presenting data - Univariate analysis continued Bivariate analysis.
Significance Testing 10/15/2013. Readings Chapter 3 Proposing Explanations, Framing Hypotheses, and Making Comparisons (Pollock) (pp ) Chapter 5.
Learning Objective Chapter 14 Correlation and Regression Analysis CHAPTER fourteen Correlation and Regression Analysis Copyright © 2000 by John Wiley &
Lesson 7 Aim: How can we represent bivariate data graphically?
Cross Tabulation Statistical Analysis of Categorical Variables.
Learning Objectives Copyright © 2002 South-Western/Thomson Learning Multivariate Data Analysis CHAPTER seventeen.
Bivariate Descriptive Analysis First step in analyzing your data Three components Cross-tabulations and frequency distributions Significance testing Correlations.
Correlation Patterns.
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Inferences about the slope of the regression line Section 13.2.
Choosing a statistical What are you trying to do?.
Chapter 16 Data Analysis: Testing for Associations.
Chapter 5 – 1 Chapter 9 Organization of Information and Measurement of Relationships: A Review of Descriptive Data Analysis.
Chapter 9 Correlational Research Designs. Correlation Acceptable terminology for the pattern of data in a correlation: *Correlation between variables.
2.6 Scatter Diagrams. Scatter Diagrams A relation is a correspondence between two sets of data X is the independent variable Y is the dependent variable.
Introduction. The Role of Statistics in Science Research can be qualitative or quantitative Research can be qualitative or quantitative Where the research.
YES Youth Organization SPSS Statistical Analysis.
Chapter Thirteen Bivariate Correlation and Regression Chapter Thirteen.
1 UNIT 13: DATA ANALYSIS. 2 A. Editing, Coding and Computer Entry Editing in field i.e after completion of each interview/questionnaire. Editing again.
Y=3x+1 y 5x + 2 =13 Solution: (, ) Solve: Do you have an equation already solved for y or x?
SOCW 671 #11 Correlation and Regression. Uses of Correlation To study the strength of a relationship To study the direction of a relationship Scattergrams.
Correlations: Linear Relationships Data What kind of measures are used? interval, ratio nominal Correlation Analysis: Pearson’s r (ordinal scales use Spearman’s.
McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved. Part Four ANALYSIS AND PRESENTATION OF DATA.
Cross Tabulation with Chi Square
Inference about the slope parameter and correlation
Analysis and Interpretation: Multiple Variables Simultaneously
Summarizing Descriptive Relationships
REGRESSION G&W p
EXPLORATORY DATA ANALYSIS and DESCRIPTIVE STATISTICS
Solving Multistep Equations
Making Comparisons All hypothesis testing follows a common logic of comparison Null hypothesis and alternative hypothesis mutually exclusive exhaustive.
Statistics in SPSS Lecture 10
Chapter 10 CORRELATION.
Multiple Regression.
CHAPTER fourteen Correlation and Regression Analysis
BIVARIATE REGRESSION AND CORRELATION
Understanding Research Results: Description and Correlation
Essentials of Marketing Research William G. Zikmund
Summarising and presenting data - Bivariate analysis
Summarising and presenting data - Univariate analysis continued
Nominal/Ordinal Level Measures of Association
Nominal/Ordinal Level Measures of Association
Computing A Variable Mean
BIVARIATE ANALYSIS: Measures of Association Between Two Variables
BIVARIATE ANALYSIS: Measures of Association Between Two Variables
Distribute and combine like terms
Summarizing Descriptive Relationships
Operational Definitions,
Presentation transcript:

Analyzing the Relationship Between Two Variables Bivariate Analysis Analyzing the Relationship Between Two Variables

Types of Bivariate Analysis New Terms: Independent Variable; Dependent Variable Remember your variable types: Nominal; Ordinal; Interval To analyze the relationship between 2 variables, we will define several possibilities: Nominal-Nominal or Ordinal Nominal-Interval Interval-Interval

Bivariate Analysis, for each combination Nominal-Interval: Difference of means Nominal-Nominal: Cross Tabulation Interval-Interval: Correlation and Regression Analysis Surprise! You’ve already been doing some of this…..and have seen these procedures in Simon.

More visuals: Difference of Means

More visuals: Cross Tabulation

Nominal-Interval Bivariate Analysis: Mean Difference Does the average number of persons in a household differ by neighborhood?

Nominal-Nominal Bivariate Analysis: Cross Tabulation Occupational Distributions by Ward, 1905:

Interval –Interval Bivariate Analysis: Regression and Correlation Does the size a building determine it property value?

Interval –Interval Bivariate Analysis: Regression and Correlation Does the size a building determine it property value?