Lab: Comprehensive Review Correlation & Regression 1-Sample t-test Dependent t-test 1-way ANOVA 2-way ANOVA.

Slides:



Advertisements
Similar presentations
Significance Testing.  A statistical method that uses sample data to evaluate a hypothesis about a population  1. State a hypothesis  2. Use the hypothesis.
Advertisements

CAAP Fall Report On Freshmen/Sophomores OIRA February 2007.
13- 1 Chapter Thirteen McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.
Learning Objectives Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.
Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Bivariate Correlation and Regression CHAPTER Thirteen.
Dr. Sinn, PSYC301, The joy of 1-way ANOVA1 Unit 3 Outline Day 1: Introduce F Return Tests (20) Power (20) Matching variance with data, ranking Fs (20)
PY 427 Statistics 1Fall 2006 Kin Ching Kong, Ph.D Lecture 9 Chicago School of Professional Psychology.
PSY 307 – Statistics for the Behavioral Sciences
4.1 All rights reserved by Dr.Bill Wan Sing Hung - HKBU Lecture #4 Studenmund (2006): Chapter 5 Review of hypothesis testing Confidence Interval and estimation.
Does SiZe Matter ? An analysis of Orientation Group Size and Cumulative GPA.
Analysis of Differential Expression T-test ANOVA Non-parametric methods Correlation Regression.
Jeopardy! One-Way ANOVA Correlation & Regression Plots.
Bivariate & Multivariate Regression correlation vs. prediction research prediction and relationship strength interpreting regression formulas process of.
Introduction to Probability and Statistics Linear Regression and Correlation.
UNDERSTANDING RESEARCH RESULTS: STATISTICAL INFERENCE © 2012 The McGraw-Hill Companies, Inc.
Program Review  Health Profession Advising  Key Communities  Orientation and Transition Programs  Outreach and Support  Undeclared Advising.
Correlation 1. Correlation - degree to which variables are associated or covary. (Changes in the value of one tends to be associated with changes in the.
Chapter 10r Linear Regression Revisited. Correlation A numerical measure of the direction and strength of a linear association. –Like standard deviation.
Choosing the correct analysis. Some research questions How many times each semester do Penn State students go “home”? What percentage of Penn State students.
Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Spring 2015 Room 150 Harvill.
Week 9: Chapter 15, 17 (and 16) Association Between Variables Measured at the Interval-Ratio Level The Procedure in Steps.
Independent t-tests.  Use when:  You are examining differences between groups  Each participant is tested once  Comparing two groups only.
3nd meeting: Multilevel modeling: introducing level 1 (individual) and level 2 (contextual) variables + interactions Subjects for today:  Intra Class.
Statistical Analyses & Threats to Validity
Hypothesis Testing in SPSS Using the T Distribution
LEARNING PROGRAMME Hypothesis testing Intermediate Training in Quantitative Analysis Bangkok November 2007.
Factorial Design Two Way ANOVAs
Lecturer’s desk INTEGRATED LEARNING CENTER ILC 120 Screen Row A Row B Row C Row D Row E Row F Row G Row.
Lecturer’s desk INTEGRATED LEARNING CENTER ILC 120 Screen Row A Row B Row C Row D Row E Row F Row G Row.
C ORRELATION B ONUS W ORK Completion = up to 20 points toward Exams.
Inferential Statistics 2 Maarten Buis January 11, 2006.
MULTIPLE REGRESSION Using more than one variable to predict another.
T-TEST Statistics The t test is used to compare to groups to answer the differential research questions. Its values determines the difference by comparing.
Simple Linear Regression One reason for assessing correlation is to identify a variable that could be used to predict another variable If that is your.
Data Analysis (continued). Analyzing the Results of Research Investigations Two basic ways of describing the results Two basic ways of describing the.
Lecturer’s desk INTEGRATED LEARNING CENTER ILC 120 Screen Row A Row B Row C Row D Row E Row F Row G Row.
Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Spring 2015 Room 150 Harvill.
Investigating the Relationship between Scores
1 Self-Regulation and Ability Predictors of Academic Success during College Anastasia Kitsantas, Faye Huie, and Adam Winsler George Mason University.
Lecture 9 TWO GROUP MEANS TESTS EPSY 640 Texas A&M University.
MGS3100_04.ppt/Sep 29, 2015/Page 1 Georgia State University - Confidential MGS 3100 Business Analysis Regression Sep 29 and 30, 2015.
The Role of Health Education and Hardiness in Perceived Wellness Rozan Khalil Psychology and Social Behavior University of California, Irvine.
SW388R6 Data Analysis and Computers I Slide 1 Multiple Regression Key Points about Multiple Regression Sample Homework Problem Solving the Problem with.
Lecturer’s desk INTEGRATED LEARNING CENTER ILC 120 Screen Row A Row B Row C Row D Row E Row F Row G Row.
Simple Linear Regression ANOVA for regression (10.2)
Lab 9: Two Group Comparisons. Today’s Activities - Evaluating and interpreting differences across groups – Effect sizes Gender differences examples Class.
Hypothesis Testing. Why do we need it? – simply, we are looking for something – a statistical measure - that will allow us to conclude there is truly.
Descriptive Statistics
Regression Analysis: Part 2 Inference Dummies / Interactions Multicollinearity / Heteroscedasticity Residual Analysis / Outliers.
Final Test Information The final test is Monday, April 13 at 8:30 am The final test is Monday, April 13 at 8:30 am GRH102: Last name begins with A - I.
Regression Analysis. 1. To comprehend the nature of correlation analysis. 2. To understand bivariate regression analysis. 3. To become aware of the coefficient.
Grade Point Average, among working and non-working students Group 4 ●Bre Patroske ●Marcello Gill ●Nga Wargin.
Difference Between Means Test (“t” statistic) Analysis of Variance (F statistic)
Data Processing/Statistical Analysis Calculating Reaction Rates: This is a slope - just like in math class! rate = y = y 2 -y 1 Graphing: At least two.
Center for Institutional Effectiveness LaMont Rouse, Ph.D. Fall 2015.
The Effect of Social Media Use on Narcissistic Behavior By Mariel Meskunas.
Dominion Training Survey BADM 621 October 19,2005 Shawn Miller Kylee Fink Soumya Prasad Thomas O’Neill Casey Brown.
Topics, Summer 2008 Day 1. Introduction Day 2. Samples and populations Day 3. Evaluating relationships Scatterplots and correlation Day 4. Regression and.
Beginners statistics Assoc Prof Terry Haines. 5 simple steps 1.Understand the type of measurement you are dealing with 2.Understand the type of question.
©2013, The McGraw-Hill Companies, Inc. All Rights Reserved Chapter 3 Investigating the Relationship of Scores.
Just one quick favor… Please use your phone or laptop Please take just a minute to complete Course Evaluations online….. Check your for a link or.
MGS4020_Minitab.ppt/Jul 14, 2011/Page 1 Georgia State University - Confidential MGS 4020 Business Intelligence Regression Analysis By Using Minitab Jul.
CHAPTER 15: THE NUTS AND BOLTS OF USING STATISTICS.
Multiple Regression: I
Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Fall 2016 Room 150 Harvill Building 10: :50 Mondays, Wednesdays.
CAAP Fall Report On Freshmen/Sophomores
Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Fall 2016 Room 150 Harvill Building 10: :50 Mondays, Wednesdays.
Practice As part of a program to reducing smoking, a national organization ran an advertising campaign to convince people to quit or reduce their smoking.
Experimental research methods.
MGS 3100 Business Analysis Regression Feb 18, 2016
Presentation transcript:

Lab: Comprehensive Review Correlation & Regression 1-Sample t-test Dependent t-test 1-way ANOVA 2-way ANOVA

#1 Ecological Awareness: a. Ho: ρ = 0 b. r(8) = -.758, p ≤.05 c. The hypothesis was supported. Ecological awareness correlates negatively with miles driven per week, r(8) = -.758, p ≤.05. Ecological awareness accounts for about 58% of the variance in miles driven, r 2 =.575. d1. The chance of obtaining the result by chance: p=.011 d2. You’re 57.5% more accurate in predicting miles driven.

(#1 cont.) d3. y’ = bx + a y’ = (15) = d4.

#2 Goal Setting #2 Goal setting: a. μ control = μ education = μ education + contracting = μ ed. + contract + feedback b. F(3,16) = , p≤.05 μ ed. + contract + feedback < μ ed. + contract < μ control = μ education c. The hypothesis was supported. Participants receiving feedback set their thermostats lower (M=67.6) than participants who only signed contracts (M=70.6), who in turn set their thermostats lower than participants who received only education (M=75.0) or no intervention (M=74.8), F(3,16) = , p≤.05. The intervention accounted for a large amount of variance in the thermostat setting, η 2 =.7067.

(#2 Cont.) d. Goal- setting without feedback still works, but not as well.

#3 Caffeine addiction a. Ho: μ caffeine = 50 b. t(8) = , n.s. c. The hypothesis was not supported. The average amount of caffeine (M = 37.78) did not differ significantly from the promised amount (μ =50), t(8) = , n.s. d. The level of caffeine is not terribly consistent, ŝ =

#4. Clustering freshmen courses a. μ D = 0 b. t(14) = 2.210, p≤.05 c. The hypothesis was supported. The level of social integration was significantly higher for those in clustered courses (M=27.00) than those not (M=21.67), t(14) = 2.210, p≤.05. The effect of the clustering on social integration was moderate, d =.57.

(#4 cont.) e. Yes, I think freshmen who take clustered courses might become more socially integrated. Other programs that increase familiar within a small group might also work. For example, students could be in the same group for their summer orientation and for their freshman seminar course in the Fall. d.

#5 Homework Input

#5 Homework Output

#5 (cont) a. μ low consc. = μ high consc.. μ no grading = μ spot checking = μ full grading no interaction b. F(1,18) = , p≤.05 μ low consc. < μ high consc. F(2,18) = , p≤.05 μ no grd < μ spot grd < μ full grd F(2,18) = , p≤.05

(#5 cont.) c. The hypotheses were supported. Students low in conscientiousness received lower grades (M=63.25) than those high in conscientiousness (M=83.83), F(1,18) = , p≤.05. Grading homework produced higher grades (M=79.75) than spot checking homework (M=72.62), which in turn produced higher grades than no grading (M=68.25), F(2,18) = , p≤.05. The interaction was also significant, F(2,18) = , p≤.05. Grading improves grades for low-conscientious students, but high-conscientious students do well regardless. Conscientious accounts for the most variance in grades, η 2 =.6194, though both the effects for homework, η 2 =.1313, and the interaction, η 2 =.1818, were practically significant.

(#5 cont.) e. If I were teaching, I would grade homework. d.