Multiple Regression using STAT 301 Spring 2004 Final Exam Grades
|
|
|
- Darlene Clarke
- 7 years ago
- Views:
Transcription
1 Frequency Frequency Frequency Multiple Regression using STAT 3 Spring 24 Exam Grades We want to figure out the best way to predict a person s final exam (not total course) grade. Possible variables to include are,, Project, Exam and Exam 2. Here is the summary of the individual variables. Do you see any repeated outlier students? Mean = Std. Dev. =.986 = Mean = Std. Dev. = = Mean = Std. Dev. = =
2 Frequency Frequency Frequency Exam Mean = Std. Dev. =.2 = 64 5 Exam Mean = 73.9 Std. Dev. = = Mean = Std. Dev. = 5.2 =
3 ow let s look at the scatterplots between final and each of the other 5 variables: Exam
4 ow let s look at the correlation between the 6 variables: Exam **. Correlation is significant at the. level (2-tailed). *. Correlation is significant at the.5 level (2-tailed). Correlations Exam.744**.387**.322*.57**.67** **.385**.574**.592**.8** **.385**.282*.268*.353** *.574**.282*.553**.66** **.592**.268*.553**.75** **.8**.353**.66**.75** From strongest to weakest, write the order that the other 5 variables are correlated to. Explanatory variable Correlation Which other 2 explanatory variables are most strongly correlated to each other? Which other 2 explanatory variables are the most weakly correlated to each other? 4
5 Let s do regression using all 5 of the variables to explain. The results are: Summary b Adjusted Std. Error of R R Square R Square the Estimate.84 a a. Predictors: (Constant),,, Exam,, b. Dependent Variable: Regression Residual Total AOVA b Sum of Squares df Mean Square F Sig a a. Predictors: (Constant),,, Exam,, b. Dependent Variable: (Constant) Exam a. Dependent Variable: Unstandardized Coe fficients a Standardized 95% Confidence Interval for B t Sig. Lower Bound Upper Bound B Std. Error Beta What is the value for R 2? What is the estimate for the standard deviation? What is the value for the AOVA F-test statistic and its P-value? Are there any individual coefficients which are not significant? If so, which ones? We delete variables one-at-a-time from the model, starting with the least significant. Which one should we delete first? 5
6 Let s do regression using the 4 more significant variables (leaving off Project) to explain. The results are: Summary b Adjusted Std. Error of R R Square R Square the Estimate.83 a a. Predictors: (Constant),,, Exam, b. Dependent Variable: Regression Residual Total AOVA b Sum of Squares df Mean Square F Sig a a. Predictors: (Constant),,, Exam, b. Dependent Variable: (Constant) Exam a. Dependent Variable: Unstandardized Coe fficients a Standardized 95% Confidence Interval for B t Sig. Lower Bound Upper Bound B Std. Error Beta What is the value for R 2? How did it change from the original? Good or bad? What is the estimate for the standard deviation? How did it change from the original? Good or bad? What is the value for the AOVA F-test statistic and its P-value? How did it change from the original? Good or bad? Are there any individual coefficients which are not significant? If so, which ones? We delete variables one-at-a-time from the model, starting with the least significant. Which one should we delete now? 6
7 Let s do regression using the 3 more significant variables (leaving off Project and now Homework) to explain. The results are: Summary b Adjusted Std. Error of R R Square R Square the Estimate.8 a a. Predictors: (Constant),,, Exam b. Dependent Variable: Regression Residual Total AOVA b Sum of Squares df Mean Square F Sig a a. Predictors: (Constant),,, Exam b. Dependent Variable: (Constant) Exam a. Dependent Variable: Unstandardized Coe fficients a Standardized B Std. Error Beta What is the value for R 2? How did it change from the model on the previous page? Good or bad? What is the estimate for the standard deviation? How did it change from the model on the previous page? Good or bad? What is the value for the AOVA F-test statistic and its P-value? How did it change from the model on the previous page? Good or bad? Are there any individual coefficients which are not significant? If so, which ones? We delete variables one-at-a-time from the model, starting with the least significant. Are there any which still need deleting? What is our multiple regression model for Exam? 95% Confidence Interval for B t Sig. Lower Bound Upper Bound Using this model, estimate your Exam score this semester. 7
8 What if we had made a mistake and had deleted Exam and Exam 2 from the model instead of Project and Lab? Our regression output would look like: Summary b Adjusted Std. Error of R R Square R Square the Estimate.662 a a. Predictors: (Constant),,, b. Dependent Variable: Regression Residual Total AOVA b Sum of Squares df Mean Square F Sig a a. Predictors: (Constant),,, b. Dependent Variable: (Constant) a. Dependent Variable: Unstandardized Coe fficients a Standardized 95% Confidence Interval for B t Sig. Lower Bound Upper Bound B Std. Error Beta What is the value for R 2? How did it change from the original? Good or bad? What is the estimate for the standard deviation? How it change from the original? Good or bad? What is the value for the AOVA F-test statistic and its P-value? How did it change from the original? Good or bad? Are there any individual coefficients which are not significant? If so, which ones? How does this model compare to the one you picked on the previous page? Justify your reasons for preferring one over the other. 8
9 General Questions about Multiple Regression State the hypotheses for the original regression AOVA model here. Why do we look at that first? What does R 2 tell us? Answer in terms of this story. What is a good R 2? Where do you find the estimate for the standard deviation? What is a good standard deviation? What do the test statistics and the P-values from the regression output for individual coefficients (t tests) tell you? Write out the hypotheses from the original model and state your conclusions. Why don t we always keep all the variables in the model? How do we know when we re done finding the best model for our data? Is it just when we have only significant coefficients, or is there more than just P-values that we have to consider? What 2 types of graphs (other than scatterplots) should we do to check our assumptions? 9
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
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
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
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
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.
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:
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) -
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
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
Two Related Samples t Test
Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person
Introduction to Analysis of Variance (ANOVA) Limitations of the t-test
Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One- Way ANOVA Limitations of the t-test Although the t-test is commonly used, it has limitations Can only
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
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
Moderator and Mediator Analysis
Moderator and Mediator Analysis Seminar General Statistics Marijtje van Duijn October 8, Overview What is moderation and mediation? What is their relation to statistical concepts? Example(s) October 8,
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
MULTIPLE REGRESSION EXAMPLE
MULTIPLE REGRESSION EXAMPLE For a sample of n = 166 college students, the following variables were measured: Y = height X 1 = mother s height ( momheight ) X 2 = father s height ( dadheight ) X 3 = 1 if
Example: Boats and Manatees
Figure 9-6 Example: Boats and Manatees Slide 1 Given the sample data in Table 9-1, find the value of the linear correlation coefficient r, then refer to Table A-6 to determine whether there is a significant
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
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
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
A full analysis example Multiple correlations Partial correlations
A full analysis example Multiple correlations Partial correlations New Dataset: Confidence This is a dataset taken of the confidence scales of 41 employees some years ago using 4 facets of confidence (Physical,
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)
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
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
False. Model 2 is not a special case of Model 1, because Model 2 includes X5, which is not part of Model 1. What she ought to do is estimate
Sociology 59 - Research Statistics I Final Exam Answer Key December 6, 00 Where appropriate, show your work - partial credit may be given. (On the other hand, don't waste a lot of time on excess verbiage.)
2013 MBA Jump Start Program. Statistics Module Part 3
2013 MBA Jump Start Program Module 1: Statistics Thomas Gilbert Part 3 Statistics Module Part 3 Hypothesis Testing (Inference) Regressions 2 1 Making an Investment Decision A researcher in your firm just
Class 19: Two Way Tables, Conditional Distributions, Chi-Square (Text: Sections 2.5; 9.1)
Spring 204 Class 9: Two Way Tables, Conditional Distributions, Chi-Square (Text: Sections 2.5; 9.) Big Picture: More than Two Samples In Chapter 7: We looked at quantitative variables and compared the
One-Way Independent ANOVA: Exam Practice Sheet
One-Way Independent AOVA: Exam Practice Sheet Questions Question 1 Students were given different drug treatments before revising for their exams. Some were given a memory drug, some a placebo drug and
Answer: C. The strength of a correlation does not change if units change by a linear transformation such as: Fahrenheit = 32 + (5/9) * Centigrade
Statistics Quiz Correlation and Regression -- ANSWERS 1. Temperature and air pollution are known to be correlated. We collect data from two laboratories, in Boston and Montreal. Boston makes their measurements
5. Correlation. Open HeightWeight.sav. Take a moment to review the data file.
5. Correlation Objectives Calculate correlations Calculate correlations for subgroups using split file Create scatterplots with lines of best fit for subgroups and multiple correlations Correlation The
DEPARTMENT OF PSYCHOLOGY UNIVERSITY OF LANCASTER MSC IN PSYCHOLOGICAL RESEARCH METHODS ANALYSING AND INTERPRETING DATA 2 PART 1 WEEK 9
DEPARTMENT OF PSYCHOLOGY UNIVERSITY OF LANCASTER MSC IN PSYCHOLOGICAL RESEARCH METHODS ANALYSING AND INTERPRETING DATA 2 PART 1 WEEK 9 Analysis of covariance and multiple regression So far in this course,
Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini
NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building
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.
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
Coefficient of Determination
Coefficient of Determination The coefficient of determination R 2 (or sometimes r 2 ) is another measure of how well the least squares equation ŷ = b 0 + b 1 x performs as a predictor of y. R 2 is computed
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
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
Correlation and Regression Analysis: SPSS
Correlation and Regression Analysis: SPSS Bivariate Analysis: Cyberloafing Predicted from Personality and Age These days many employees, during work hours, spend time on the Internet doing personal things,
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
AGE AND EMOTIONAL INTELLIGENCE
White Paper Research on Emotional Intelligence AGE AND EMOTIONAL INTELLIGENCE Lorenzo Fariselli, Massimiliano Ghini, Joshua Freedman Publication Date: May 16, 2006 Last Updated: Jan 8, 2008 Abstract: There
HYPOTHESIS TESTING WITH SPSS:
HYPOTHESIS TESTING WITH SPSS: A NON-STATISTICIAN S GUIDE & TUTORIAL by Dr. Jim Mirabella SPSS 14.0 screenshots reprinted with permission from SPSS Inc. Published June 2006 Copyright Dr. Jim Mirabella CHAPTER
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
Multiple Regression Using SPSS
Multiple Regression Using SPSS The following sections have been adapted from Field (2009) Chapter 7. These sections have been edited down considerably and I suggest (especially if you re confused) that
Chapter 10. Key Ideas Correlation, Correlation Coefficient (r),
Chapter 0 Key Ideas Correlation, Correlation Coefficient (r), Section 0-: Overview We have already explored the basics of describing single variable data sets. However, when two quantitative variables
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
How Far is too Far? Statistical Outlier Detection
How Far is too Far? Statistical Outlier Detection Steven Walfish President, Statistical Outsourcing Services steven@statisticaloutsourcingservices.com 30-325-329 Outline What is an Outlier, and Why are
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
LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE
LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre-
Linear Models in STATA and ANOVA
Session 4 Linear Models in STATA and ANOVA Page Strengths of Linear Relationships 4-2 A Note on Non-Linear Relationships 4-4 Multiple Linear Regression 4-5 Removal of Variables 4-8 Independent Samples
Relationships Between Two Variables: Scatterplots and Correlation
Relationships Between Two Variables: Scatterplots and Correlation Example: Consider the population of cars manufactured in the U.S. What is the relationship (1) between engine size and horsepower? (2)
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,
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
Chapter 7. Comparing Means in SPSS (t-tests) Compare Means analyses. Specifically, we demonstrate procedures for running Dependent-Sample (or
1 Chapter 7 Comparing Means in SPSS (t-tests) This section covers procedures for testing the differences between two means using the SPSS Compare Means analyses. Specifically, we demonstrate procedures
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
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
Independent t- Test (Comparing Two Means)
Independent t- Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent t-test when to use the independent t-test the use of SPSS to complete an independent
Multiple Regression: What Is It?
Multiple Regression Multiple Regression: What Is It? Multiple regression is a collection of techniques in which there are multiple predictors of varying kinds and a single outcome We are interested in
" 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
MULTIPLE REGRESSION ANALYSIS OF MAIN ECONOMIC INDICATORS IN TOURISM. R, analysis of variance, Student test, multivariate analysis
Journal of tourism [No. 8] MULTIPLE REGRESSION ANALYSIS OF MAIN ECONOMIC INDICATORS IN TOURISM Assistant Ph.D. Erika KULCSÁR Babeş Bolyai University of Cluj Napoca, Romania Abstract This paper analysis
Correlational Research
Correlational Research Chapter Fifteen Correlational Research Chapter Fifteen Bring folder of readings The Nature of Correlational Research Correlational Research is also known as Associational Research.
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
Chapter Four. Data Analyses and Presentation of the Findings
Chapter Four Data Analyses and Presentation of the Findings The fourth chapter represents the focal point of the research report. Previous chapters of the report have laid the groundwork for the project.
Stata Walkthrough 4: Regression, Prediction, and Forecasting
Stata Walkthrough 4: Regression, Prediction, and Forecasting Over drinks the other evening, my neighbor told me about his 25-year-old nephew, who is dating a 35-year-old woman. God, I can t see them getting
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
ch12 practice test SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.
ch12 practice test 1) The null hypothesis that x and y are is H0: = 0. 1) 2) When a two-sided significance test about a population slope has a P-value below 0.05, the 95% confidence interval for A) does
Chicago Booth BUSINESS STATISTICS 41000 Final Exam Fall 2011
Chicago Booth BUSINESS STATISTICS 41000 Final Exam Fall 2011 Name: Section: I pledge my honor that I have not violated the Honor Code Signature: This exam has 34 pages. You have 3 hours to complete this
When to use Excel. When NOT to use Excel 9/24/2014
Analyzing Quantitative Assessment Data with Excel October 2, 2014 Jeremy Penn, Ph.D. Director When to use Excel You want to quickly summarize or analyze your assessment data You want to create basic visual
Lesson 4 Measures of Central Tendency
Outline Measures of a distribution s shape -modality and skewness -the normal distribution Measures of central tendency -mean, median, and mode Skewness and Central Tendency Lesson 4 Measures of Central
Lecture 11: Chapter 5, Section 3 Relationships between Two Quantitative Variables; Correlation
Lecture 11: Chapter 5, Section 3 Relationships between Two Quantitative Variables; Correlation Display and Summarize Correlation for Direction and Strength Properties of Correlation Regression Line Cengage
Chapter 7 Section 7.1: Inference for the Mean of a Population
Chapter 7 Section 7.1: Inference for the Mean of a Population Now let s look at a similar situation Take an SRS of size n Normal Population : N(, ). Both and are unknown parameters. Unlike what we used
Economics of Strategy (ECON 4550) Maymester 2015 Applications of Regression Analysis
Economics of Strategy (ECON 4550) Maymester 015 Applications of Regression Analysis Reading: ACME Clinic (ECON 4550 Coursepak, Page 47) and Big Suzy s Snack Cakes (ECON 4550 Coursepak, Page 51) Definitions
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:
Simple Linear Regression
STAT 101 Dr. Kari Lock Morgan Simple Linear Regression SECTIONS 9.3 Confidence and prediction intervals (9.3) Conditions for inference (9.1) Want More Stats??? If you have enjoyed learning how to analyze
Correlation and Regression
Correlation and Regression Scatterplots Correlation Explanatory and response variables Simple linear regression General Principles of Data Analysis First plot the data, then add numerical summaries Look
Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.
Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing
Statistics. Measurement. Scales of Measurement 7/18/2012
Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does
ANALYSIS OF USER ACCEPTANCE OF A NETWORK MONITORING SYSTEM WITH A FOCUS ON ICT TEACHERS
ANALYSIS OF USER ACCEPTANCE OF A NETWORK MONITORING SYSTEM WITH A FOCUS ON ICT TEACHERS Siti Rahayu Abdul Aziz 1, Mohamad Ibrahim 2, and Suhaimi Sauti 3 1 Universiti Teknologi MARA, Malaysia, rahayu@fskm.uitm.edu.my
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
Effect of the learning support and the use of project management tools on project success: The case of Pakistan
Effect of the learning support and the use of project management tools on project success: The case of Pakistan Muhammad Javed 1 Atiq ur Rehman 2* M. Shahzad N.K. Lodhi 3 1. Student MSPM, SZABIST, Islamabad
One-Way Analysis of Variance
One-Way Analysis of Variance Note: Much of the math here is tedious but straightforward. We ll skim over it in class but you should be sure to ask questions if you don t understand it. I. Overview A. We
hp calculators HP 50g Trend Lines The STAT menu Trend Lines Practice predicting the future using trend lines
The STAT menu Trend Lines Practice predicting the future using trend lines The STAT menu The Statistics menu is accessed from the ORANGE shifted function of the 5 key by pressing Ù. When pressed, a CHOOSE
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,
WHAT TYPES OF ON-LINE TEACHING RESOURCES DO STUDENTS PREFER
WHAT TYPES OF ON-LINE TEACHING RESOURCES DO STUDENTS PREFER James R. Lackey PhD. Head of Computing and Information Services Faculty Support Center Oklahoma State University jlackey@okstate.edu This research
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,
Module 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
Using R for Linear Regression
Using R for Linear Regression In the following handout words and symbols in bold are R functions and words and symbols in italics are entries supplied by the user; underlined words and symbols are optional
ABSORBENCY OF PAPER TOWELS
ABSORBENCY OF PAPER TOWELS 15. Brief Version of the Case Study 15.1 Problem Formulation 15.2 Selection of Factors 15.3 Obtaining Random Samples of Paper Towels 15.4 How will the Absorbency be measured?
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
Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics
Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This
Data Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
Name: Date: Use the following to answer questions 2-3:
Name: Date: 1. A study is conducted on students taking a statistics class. Several variables are recorded in the survey. Identify each variable as categorical or quantitative. A) Type of car the student
Statistics 151 Practice Midterm 1 Mike Kowalski
Statistics 151 Practice Midterm 1 Mike Kowalski Statistics 151 Practice Midterm 1 Multiple Choice (50 minutes) Instructions: 1. This is a closed book exam. 2. You may use the STAT 151 formula sheets and
Outline: Demand Forecasting
Outline: Demand Forecasting Given the limited background from the surveys and that Chapter 7 in the book is complex, we will cover less material. The role of forecasting in the chain Characteristics of
2. Simple Linear Regression
Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according
Binary Logistic Regression
Binary Logistic Regression Main Effects Model Logistic regression will accept quantitative, binary or categorical predictors and will code the latter two in various ways. Here s a simple model including
Outline. Definitions Descriptive vs. Inferential Statistics The t-test - One-sample t-test
The t-test Outline Definitions Descriptive vs. Inferential Statistics The t-test - One-sample t-test - Dependent (related) groups t-test - Independent (unrelated) groups t-test Comparing means Correlation
SPSS Explore procedure
SPSS Explore procedure One useful function in SPSS is the Explore procedure, which will produce histograms, boxplots, stem-and-leaf plots and extensive descriptive statistics. To run the Explore procedure,
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