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Book sectio n 09/07 Topic coverage (page) Introduction 1 No lab 09/12 Sample survey and inference about populations 3.1.1 Sample space and relationships among events 4.1 Definition of probability 4.2 Population, sample, random sample (113) Sample space, event, complement, union, intersection, dis mutually exclusive events (149-157) Three statements defining probability (160 Results following definition (163) Definition of probability from a sample sp equally likely outcomes (167) 09/14 Counting rules useful in probability 4.3 Product rule, permutation (168-172) Combination (172-173) 2 Additional topics covered in the lab (not in class) Time order plot (55-59) Stem and leaf displays The frequency distribution (9-10) Lab 1 Numerical and graphical displays of data (part 1) 1.2 1.3.1 1.3.2 2.1 2.2 Histogram (16) Check sheets and summary tables Bar chart (11) Pie chart Pareto diagram (12-14) The contingency table (53-54) The side-by-side bar chart (53-54) Misleading displays of data From 09/14 to 09/19 Measures of central tendency: mean, med weighted average (25) Videos/quiz of the month 1.4.1 1.4.5 Measures of position: quartile, percentiles Measures of variation: range, IQR, s2, s (2 Sampling with replacement, sampling wit replacement (120-125) Conditional probability and independence 3.2.1 4.4 Definition of conditional probability (176 Independence (184-185) 09/19 Complementary events (188) 3 Rules of probability Additive rule (188-189) 4.5 Multiplicative rule (189) 09/21 No class – Engineering Expo Lab 2 Numerical and graphical displays of data (part 2) Additional topics covered in the lab (neither in class nor in t Measures of central tendency: midrange, midhinge 5-numbe summary Bloxplot (25) Measures of shape: skewness, ku Random variable (214) 4 09/26 Random variables and their probability distribution Discrete random variable (215) 5.1 Probability mass function (215) Cumulative distribution function (217) 6 Expected values of random variables Expected value (22 5.2 Variance (221) E(aX+b) = aE(X) + V(aX+b) = a2V(X) The Bernoulli distribution 5.3 Bernoulli distribution (230) Continuous r.v. (26 Continuous random variable Probability density 6.1 Distribution functi Expected values (2 09/28 Expected values of continuous r.v. Variance (273) 6.2 E(aX+b) = aE(X) + V(aX+b) = a2V(X) Additional topics covered in the lab Lab 3 Discrete probability distributions Binomial distributi Hypergeometric di Normal distributio Standard normal di 10/03 The normal distribution Empirical rule (292 6.6 Q-Q plot (299) Normal probability Statistic (366) 5 The sampling distributions Estimator, point es 8.1, 9, 9.1 Commonly used pa (428) 10/05 Sampling distribut The sampling 8.2 distribution of X (large Sampling distribut Central Limit theor Use of CLT (378) sample) Lab 4 10/10 Continuous distribution and the standard normal distribution The sampling distribution of the sample proportion (large sample) Normal approxima 8.4 Use of CLT for pro The sampling distribution of X Confidence intervals: the simple sample case 6 8.3 9.2 t distribution (382) Definition (435) CI for large sample 10/12 Confidence interval for a mean: General distribution Margin of error (43 9.2.1 Interpretation (438 Calculate sample s Confidence interval for a mean: Normal distribution 7 CI for small sample size - Lab 5 Normal probability plot and sampling distributions 10/17 Test 1 2 unkn CI with normal app Confidence interval for a proportion 9.2.3 Confidence interval for the variance 9.2.4 Calculate sample s 10/19 Lab 6 7 9.2.2 Confidence, prediction and tolerance intervals CI for the variance Additional topics covered in the lab ( intervals (474-475) Tolerance int P-value (505) From 10/19 to 10/24 Videos/quiz of the month Testing with the p- 10.2 Interpretation as th Hypothesis (495) Null hypothesis (49 Alternative hypoth Test statistic (495) Terminology of hypothesis testing Rejection region (4 10.1 Critical value (495 Two-tailed test (49 10/24 One tailed test (497 8 Type I error, level Type II error (499) P-value (505) Hypothesis testing: The single sample case Testing with the p- 10.2 Interpretation as th Wrap up of cases: small sample - 2 unknown Testing a mean: Normal distribution case Hypothesis testing 10/26 Typical hypotheses Testing for proportion: Large sample case Lab 7 10.2.3 Confidence intervals and hypothesis testing Hypothesis testing (516) Procedure (523) Testing with large Testing the difference between two means 525) 10.3.1 10/31 Enumeration of cas known, 2 unknown and assumed eq different (526, 529) Sampling distribut 9 Testing the ratio of variances: Normal distribution case 11/02 Lab 8 11/07 Testing the difference between means for paired samples 10.3.4 Test of variances r Difference between the paired test 10.3.3 Importance Calculation (532, 5 Hypothesis testing II Testing equality among binomial parameters 10.4.2 A review of design of experiments 12.1 Test (543, 544, 545) What is design of e Why randomizatio Why is it used? ANOVA technique 12.2 Why a t-test is not (679) 10 A completely rand Data layout (686, 6 Data plot 11/09 ANOVA for the Complete randomized design (CRD) 12.3 Sources of variatio Sums of squares (6 Degrees of freedom Mean squares (683 F-test and assumpt Final table (684) Lab 9 Hypothesis testing III From 11/9 to 11/14 Video/quiz of the month (with electronic quiz) 11/14 Test 2 8 Tests for identifyin Tukey-Kramer (50 Definition (709) Importance (709) 11 A randomized bloc 11/16 Randomized block design (RBD) 12.6 Hypotheses (710) ANOVA table for Interpretation What happens if th Lab 10 Complete randomized designs Definition (724) Importance (723, 7 A two-factor factor Factorial designs 12 Hypotheses (726) 11/21 ANOVA table for Interpretation Interaction and ma 15th Annual Lip-Sync / 3rd Annual Dance-Sync Contest (8:00 PM - 11:00 PM; Ro No Lab 11/28 Scatterplots: Graphical analysis of association Correlation: estimating strength of a linear relation Scatterplot (64) 2.3 Scatterplot matrix 2.4, 11.3.3 Correlation coefficient (72, 73, 58 Simple linear regre Least Squares coef 13 11/30 Standard error of th Regression: modeling linear relationships Residual analysis ( t-test for the slope Using the model fo Additional topics covered in the lab ( efficiency Lab 11 Blocked and factorial designs 12/05 Transformations 12/07 FINAL EXAM – FIRST PART 12/09 FINAL EXAM – SECOND PART DUE. DROPBOX CLOSES AT 12:00 m (N 2.5, 2.7 Transformations (95-103) Additional topics covered in the lab ( F-test for the slope 14 ANOVA table for regression coefficient Lab 12 Regression Correlation in term Confidence interva Prediction interval 595) 9 LAB ROOM ASSIGNMENTS Laboratory sessions will be held in the following rooms. An announcement on the course web page and/or during lecture will be made for special lab projects that may take place in locations other than those listed below. Section Lab Group 1: (44710) Lab Group 2: (44711) Lab Group 3: (44709) Lab Group 4: (44700) Lab Group 5: (44696) Lab Group 6: (46627) Lab Group 7: (44712) Lab Group 8: (44713) Lab Group 9: (44701) Day/Time M 6:30 pm - 9:20 pm T 12:30 am - 3:20 pm T 3:30 pm - 6:20 pm T 6:30 pm - 9:20 pm W 3:30 pm - 6:20 pm W 6:30 pm - 9:20 pm R 12:30 am - 3:20 pm R 3:30 pm - 6:20 pm R 6:30 pm - 9:20 pm Room CEAS C-229 CEAS C-229 CEAS C-229 CEAS C-227 CEAS C208 CEAS C-227 CEAS C-229 CEAS C-229 CEAS C-226 Instructor Ms. Kimberly Harms Mr. Milton Soto Ms. Megan Kuk Mr. Ryan Walsh Ms. Yuwen Gu Ms. Michelle Valente Mr. Matthew Bracey Ms. Fehime Utkan Ms. Fehime Utkan