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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