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William G. Zikmund, Barry J. Babin, Jon C. Carr, Mitch Griffin

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- 1. Business Research Methods William G. Zikmund Chapter 21: Univariate Statistics
- 2. Univariate Statistics • Test of statistical significance • Hypothesis testing one variable at a time
- 3. Hypothesis • Unproven proposition • Supposition that tentatively explains certain facts or phenomena • Assumption about nature of the world
- 4. Hypothesis • An unproven proposition or supposition that tentatively explains certain facts or phenomena – Null hypothesis – Alternative hypothesis
- 5. Null Hypothesis • Statement about the status quo • No difference
- 6. Alternative Hypothesis • Statement that indicates the opposite of the null hypothesis
- 7. Significance Level • Critical probability in choosing between the null hypothesis and the alternative hypothesis
- 8. Significance Level • Critical Probability • Confidence Level • Alpha • Probability Level selected is typically .05 or .01 • Too low to warrant support for the null hypothesis
- 9. 0.3: oH The null hypothesis that the mean is equal to 3.0:
- 10. 0.3:1 H The alternative hypothesis that the mean does not equal to 3.0:
- 11. A Sampling Distribution 3.0 x
- 12. 3.0 x a.025 a.025 A Sampling Distribution
- 13. LOWER LIMIT UPPER LIMIT 3.0 A Sampling Distribution
- 14. Critical values of Critical value - upper limit n S ZZSX or 225 5.1 96.10.3
- 15. 1.096.10.3 196.0.3 196.3 Critical values of
- 16. Critical value - lower limit n S ZZSX -or- 225 5.1 96.1-0.3 Critical values of
- 17. 1.096.10.3 196.0.3 804.2 Critical values of
- 18. Region of Rejection LOWER LIMIT UPPER LIMIT 3.0
- 19. Hypothesis Test 3.0 2.804 3.1963.0 3.78
- 20. Accept null Reject null Null is true Null is false Correct- no error Type I error Type II error Correct- no error Type I and Type II Errors
- 21. Type I and Type II Errors in Hypothesis Testing State of Null Hypothesis Decision in the Population Accept Ho Reject Ho Ho is true Correct--no error Type I error Ho is false Type II error Correct--no error
- 22. Calculating Zobs xs x zobs
- 23. X obs S X Z Alternate Way of Testing the Hypothesis
- 24. X obs S Z 78.3 1. 0.378.3 1. 78.0 8.7 Alternate Way of Testing the Hypothesis
- 25. Choosing the Appropriate Statistical Technique • Type of question to be answered • Number of variables – Univariate – Bivariate – Multivariate • Scale of measurement
- 26. PARAMETRIC STATISTICS NONPARAMETRIC STATISTICS
- 27. t-Distribution • Symmetrical, bell-shaped distribution • Mean of zero and a unit standard deviation • Shape influenced by degrees of freedom
- 28. Degrees of Freedom • Abbreviated d.f. • Number of observations • Number of constraints
- 29. or Xlc StX .. n S tX lc ..limitUpper n S tX lc ..limitLower Confidence Interval Estimate Using the t-distribution
- 30. = population mean = sample mean = critical value of t at a specified confidence level = standard error of the mean = sample standard deviation = sample size ..lct X X S S n Confidence Interval Estimate Using the t-distribution
- 31. xcl stX 17 66.2 7.3 n S X Confidence Interval Estimate Using the t-distribution
- 32. 07.5 )1766.2(12.27.3limitupper
- 33. 33.2 )1766.2(12.27.3limitLower
- 34. Hypothesis Test Using the t-Distribution
- 35. Suppose that a production manager believes the average number of defective assemblies each day to be 20. The factory records the number of defective assemblies for each of the 25 days it was opened in a given month. The mean was calculated to be 22, and the standard deviation, ,to be 5. X S Univariate Hypothesis Test Utilizing the t-Distribution
- 36. 20: 20: 1 0 H H
- 37. nSSX / 25/5 1
- 38. The researcher desired a 95 percent confidence, and the significance level becomes .05.The researcher must then find the upper and lower limits of the confidence interval to determine the region of rejection. Thus, the value of t is needed. For 24 degrees of freedom (n-1, 25-1), the t-value is 2.064. Univariate Hypothesis Test Utilizing the t-Distribution
- 39. :limitLower 25/5064.220.. Xlc St 1064.220 936.17
- 40. :limitUpper 25/5064.220.. Xlc St 1064.220 064.20
- 41. X obs S X t 1 2022 1 2 2 Univariate Hypothesis Test t-Test
- 42. Testing a Hypothesis about a Distribution • Chi-Square test • Test for significance in the analysis of frequency distributions • Compare observed frequencies with expected frequencies • “Goodness of Fit”
- 43. i ii )²( ² E EO x Chi-Square Test
- 44. x² = chi-square statistics Oi = observed frequency in the ith cell Ei = expected frequency on the ith cell Chi-Square Test
- 45. n CR E ji ij Chi-Square Test Estimation for Expected Number for Each Cell
- 46. Chi-Square Test Estimation for Expected Number for Each Cell Ri = total observed frequency in the ith row Cj = total observed frequency in the jth column n = sample size
- 47. 2 2 22 1 2 112 E EO E EO X Univariate Hypothesis Test Chi-square Example
- 48. 50 5040 50 5060 22 2 X 4 Univariate Hypothesis Test Chi-square Example
- 49. Hypothesis Test of a Proportion p is the population proportion p is the sample proportion p is estimated with p
- 50. 5.:H 5.:H 1 0 p p Hypothesis Test of a Proportion
- 51. 100 4.06.0 pS 100 24. 0024. 04899.
- 52. pS p Zobs p 04899. 5.6. 04899. 1. 04.2
- 53. 0115.Sp 000133.Sp 1200 16. Sp 1200 )8)(.2(. Sp n pq Sp 20.p 200,1n Hypothesis Test of a Proportion: Another Example
- 54. 0115.Sp 000133.Sp 1200 16. Sp 1200 )8)(.2(. Sp n pq Sp 20.p 200,1n Hypothesis Test of a Proportion: Another Example
- 55. Indeed .001thebeyondtsignificantisit level..05theatrejectedbeshouldhypothesisnulltheso1.96,exceedsvalueZThe 348.4Z 0115. 05. Z 0115. 15.20. Z S p Z p p Hypothesis Test of a Proportion: Another Example

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