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MGMT 276: Statistical Inference in Management Spring , 2013

MGMT 276: Statistical Inference in Management Spring , 2013. Welcome. Statistical Inference in Management. Instructor: Suzanne Delaney, Ph.D. Office: 405 “N” McClelland Hall. Phone: 621-2045. Email: delaney@u.arizona.edu. Office hours: 2:00 – 3:30 Mondays and Fridays and by appointment.

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MGMT 276: Statistical Inference in Management Spring , 2013

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  1. MGMT 276: Statistical Inference in ManagementSpring, 2013 Welcome

  2. Statistical Inference in Management Instructor:Suzanne Delaney, Ph.D. Office:405 “N” McClelland Hall Phone:621-2045 Email:delaney@u.arizona.edu Office hours:2:00 – 3:30Mondays and Fridays and by appointment

  3. Homework due – Tuesday (April 2nd) On class website: Please print and complete homework worksheet #14, 15 and 16 Hypothesis testing with t-tests A full week is allowed for this homework because it includes the design and completion of an original piece of research – please plan accordingly (Please note this worksheet accounts for three homework assignments). Please click in My last name starts with a letter somewhere between A. A – D B. E – L C. M – R D. S – Z Please double check – All cell phones other electronic devices are turned off and stowed away

  4. Please read: Chapters 10 – 12 in Lind book and Chapters 2 – 4 in Plous book: (Before the next exam – April 9th) Lind Chapter 10: One sample Tests of Hypothesis Chapter 11: Two sample Tests of Hypothesis Chapter 12: Analysis of Variance Plous Chapter 2: Cognitive Dissonance Chapter 3: Memory and Hindsight Bias Chapter 4: Context Dependence

  5. Use this as your study guide By the end of lecture today3/28/13 • Logic of hypothesis testing • Steps for hypothesis testing • Hypothesis testing with t-scores (one-sample) • Hypothesis testing with t-scores (two independent samples) • Interpreting excel output of hypothesis tests • Constructing brief, complete summary statements • Hypothesis testing with analysis of variance (ANOVA) • Interpreting excel output of hypothesis tests • Constructing brief, complete summary statements

  6. If this is less than .05 (or whatever alpha is) it is significant, and we the reject null df = (n1 – 1) + (n2 – 1) = (165 - 1) + (120 -1) = 283

  7. A survey was conducted to see whether men or women • superintendents make more money • The independent variable is ________________ • The dependent variable is _________________ • 3. Who made more money men or women? • 4. Identify the two means and the observed t score • 5. Identify the p value and state whether it is less than .05

  8. A survey was conducted to see whether men or women superintendents make more money Are both p values less than 0.05? 1.37834 E-05 Equals .00001378 4 zeros 6.8917 E-06 Equals .0000068917 5 zeros

  9. A survey was conducted to see whether men or women superintendents make more money A note on scientific notation: “E-05” means move the decimal to the left 5 places E-06” means move the decimal to the left 6 places 1.37834 E-05 Equals .00001378 4 zeros 6.8917 E-06 Equals .0000068917 5 zeros

  10. A survey was conducted to see whether men or women superintendents make more money. The independent variable is a. nominal level of measurement b. ordinal level of measurement c. interval level of measurement d. ratio level of measurement

  11. A survey was conducted to see whether men or women superintendents make more money. The dependent variable is a. nominal level of measurement b. ordinal level of measurement c. interval level of measurement d. ratio level of measurement

  12. A survey was conducted to see whether men or women superintendents make more money. The independent variable is a. continuous and qualitative b. continuous and quantitative c. discrete and qualitative d. discrete and quantitative

  13. A survey was conducted to see whether men or women superintendents make more money. The dependent variable is a. continuous and qualitative b. continuous and quantitative c. discrete and qualitative d. discrete and quantitative

  14. A survey was conducted to see whether men or women superintendents make more money. This is a a. quasi, between subject design b. quasi, within subject design c. true, between subject design d. true, within subject design

  15. A survey was conducted to see whether men or women superintendents make more money. This is a a. one-tailed test b. two-tailed test c. three-tailed test d. not enough information

  16. A survey was conducted to see whether men or women superintendents make more money. The null hypothesis is a. men make more money b. women make more money c. no difference between amount of money made d. there is a difference between the amount of money made

  17. A survey was conducted to see whether men or women • superintendents make more money. If the null hypothesis was rejected we will conclude that • a. men make more money • b. women make more money • no difference between amount of money made • d. there is a difference between the amount of money made

  18. A survey was conducted to see whether men or women • superintendents make more money. A Type I error would be • a. claiming men make more money, when they don’t • b. claiming women make more money, when they don’t • claiming no difference between amount of money made, when there is a difference • d. claiming there is a difference between the amount of money made, when there is no difference

  19. A survey was conducted to see whether men or women • superintendents make more money. A Type II error would be • a. claiming men make more money, when they don’t • b. claiming women make more money, when they don’t • claiming no difference between amount of money made, when there is a difference • d. claiming there is a difference between the amount of money made, when there is no difference

  20. An t-test was conducted, there were ___ men in the study and ___ women. a. 18; 21 b. 21; 18 c. 19; 19 d. 38; 38 Let’s try one

  21. A t-test was conducted, which of the following best describes the results: a. t(21) = 2.02; p < 0.05 b. t(21) = 2.02; n.s. c. t(37) = 5.0; p < 0.05 d. t(37) = 5.0; n.s Let’s try one

  22. A t-test was conducted, with a two tail test was there a significant difference? a. No, because 5.0 is not bigger than 6.89 b. Yes, because 5.0 is bigger than 1.68. c. Yes, because 5.0 is bigger than 1.37 d. Yes, because 5.0 is bigger than 2.02 Let’s try one

  23. Which is true a. p < 0.05 b. p < 0.01 c. p < 0.001 d. All of the above Let’s try one

  24. A survey was conducted to see whether women superintendents make more money than men. This is a a. one-tailed test b. two-tailed test c. three-tailed test d. not enough information Note the change in the problem

  25. A survey was conducted to see whether women superintendents make more money than men. A t-test was conducted, which of the following best describes the results:Note the results were in the unpredicted direction a. reject the null b. do not reject the null c. not enough information Let’s try one

  26. A survey was conducted to see whether women superintendents make more money than men. A t-test was conducted, which of the following best describes the results: Note the results were in the unpredicted direction a. t(21) = 2.02; p < 0.05 b. t(21) = 2.02; n.s. c. t(37) = 5.0; p < 0.05 d. t(37) = 5.0; n.s Let’s try one

  27. Study Type 1: Confidence Intervals Comparing Two Means? Use a t-test Study Type 2: t-test We are looking to compare two means http://www.youtube.com/watch?v=n4WQhJHGQB4

  28. We are looking to compare two means Study Type 2: t-test Study Type 3: One-way Analysis of Variance (ANOVA) Comparing more than two means

  29. Study Type 3: One-way ANOVA Single Independent Variable comparing more than twogroups Single Dependent Variable (numerical/continuous) Used to test the effect of the IV on the DV Ian was interested in the effect of incentives for girl scouts on the number of cookies sold. He randomly assigned girl scouts into one of three groups. The three groups were given one of three incentives and looked to see who sold more cookies. The 3 incentives were 1) Trip to Hawaii, 2) New Bike or 3) Nothing. This is an example of a true experiment How could we make this a quasi-experiment? Independent Variable: Type of incentive Levels of Independent Variable: None, Bike, Trip to Hawaii Dependent Variable: Number of cookies sold Levels of Dependent Variable: 1, 2, 3 up to max sold Between participant design Causal relationship: Incentive had an effect – it increased sales

  30. Study Type 3: One-way ANOVA Single Independent Variable comparing more than two groups Single Dependent Variable (numerical/continuous) Used to test the effect of the IV on the DV Ian was interested in the effect of incentives for girl scouts on the number of cookies sold. He randomly assigned girl scouts into one of three groups. The three groups were given one of three incentives and looked to see who sold more cookies. The 3 incentives were 1) Trip to Hawaii, 2) New Bike or 3) Nothing. This is an example of a true experiment Dependent variable is always quantitative Sales per Girl scout Sales per Girl scout New Bike None Trip Hawaii New Bike None Trip Hawaii In an ANOVA, independent variable is qualitative (& more than two groups)

  31. One-way ANOVA versus Chi Square Be careful you are not designing a Chi Square If this is just frequency you may have a problem This is an Chi Square Total Number of Boxes Sold Sales per Girl scout This is an ANOVA New Bike None Trip Hawaii New Bike None Trip Hawaii These are just frequencies These are just frequencies These are just frequencies These are means These are means These are means

  32. Writing Assignment • To prepare for our ANOVA Project - Due April 11th • There are five parts • 1. A one page report of your design (includes all of the information from the writing assignment) • Describe your experiment: what is your question / what is your prediction? • State your Independent Variable (IV), how many levels there are, and the operational definition • State your Dependent Variable (DV), and operational definition • How many participants did you measure, and how did you recruit (sample) them • Was this a between or within participant design (why?) • 2. Gather the data • Try to get at least 10 people (or data points) per level • If you are working with other students in the class you should have 10 data points per level for each member of your group • 3. Input data into Excel (hand in data) • 4. Complete ANOVA analysis hand in ANOVA table • 5. Statement of results and include a graph of your means

  33. Thank you! See you next time!!

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