Anderson D.R., Sweeney D.J., Williams T.A. Essentials of Statistics for Business and Economics
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Index 675
Complement of events, probability and,
164–165, 186
Completely randomized design, 415–416
analysis of variance, 420–428
analysis of variance results, 442
ANOVA table, 424–425
Computers
analysis of variance results, 425–426
simple linear regression, 523–524
simple random sample generation,
268–269
statistical analysis and, 17
Conditional probability, 171–175
formula, 186
multiplication law, 174–175
Confidence coefficient
population mean, known, 309–310
simple linear regression, 
1
confidence interval,
510–511
Confidence interval
estimated regression equation, 517–521
formula, 536
Minitab applications, population proportions,
477–478
multiple regression analysis, 576–577
population mean, known, 309–310
population proportion, 325–327
sample size determination, 321–323
simple linear regression, 
1
, 510–511
StatTools applications, 480–482
Confidence level, population mean, known,
309–310
Contingency table, test of independence,
463–467, 472
Continuous random variables, 196
Convenience sampling, 296–297
Correlation coefficient, 120–122
Excel applications, 145
formulas, 133–134
Minitab applications, 143
simple linear regression, 502–503
StatTools applications, 147
Counting rules
combinations, 154
formulas, 185
permutations, 154–155
probability and, 151–155
Covariance, 116–120
Excel applications, 145
formulas, 133
Minitab applications, 143
StatTools applications, 147
Critical value
F distribution, population variance, 424
one-tailed tests, population mean known,
356–358
two-tailed tests, population mean known,
360–363
Cross-sectional data, 7–8
Crosstabulation
applications, 61–64
descriptive statistics, 54–60
Simpson’s paradox, 57–58
test of independence, 463–467
Cumulative distributions, quantitative data, 44
Customer opinion questionnaire, 12
D
Data acquisition and collection
analysis of variance, 416–417
errors in, 13
Data components
categorical and quantitative data, 7
cross-sectional and time series data, 7–8
grouped data, 126–127
scales of measurement, 6–7
statistics, 5–9
Data mining, 17–18
Data set, 5
Data sources, 10–13
Data warehousing, 17–18
Degree of belief, 156
Degrees of freedom
between-treatments estimates, population
variance, 421–422
chi-square distribution, 458–460
F distribution, population variance, 424
population mean, unknown, 313–318, 432
population means,
1
and
2
unknown,
402–403, 432
simple linear regression, 
1
confidence interval,
510–511
Dependent variable
defined, 485
regression model, 506–508
residual plot against, 531
Descriptive statistics, 13–15
business applications, 32–33
categorical data, 33–36
crosstabulations, 54–60
Excel applications, 143–146
exploratory data analysis, 49–54
Minitab applications, 141–143
numerical measures, 87–147
quantitative data, 33, 39–45
scatter diagram, 58–60
StatTools applications, 146–147
Deviation about the mean, 98. See also Standard
deviation
Difference data, matched samples, 410–411
Discrete probability distributions, 197–200
Excel, 230–231
Minitab, 230
Discrete random variables, 195
expected value, 202–203
formulas, 226
variance, 203–204
Discrete uniform probability function, 226
Distance intervals, Poisson probability
distribution, 220
Distribution, shape of, 103–104
Dot plot, quantitative data, 41–42
Dummy variable, 580–583
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676 Index
E
Economics, statistical applications in, 2–4
Elements, defined, 5
Empirical rule, 106–107
Equality
F distribution, population variance, 424
k population means, observational study,
427–428, 433
Errors
in data acquisition, 13
type I/type II errors, hypothesis tests, 349–351
Error term
multiple regression models, 567–568
regression model, 506–508
Estimated multiple regression equation, 554–555,
576–577, 588
Estimated regression equation
defined, 486–487
estimation and prediction, 517–521
Excel applications, 548–550
formulas, 535–536
Ethical guidelines, statistical practices, 18–19
Events
complement of, 164–165
independent events, 174
probability and, 160–162
Excel (Microsoft)
analysis of variance results, 425–426
descriptive statistics in, 143–146
discrete probability distributions, 230–231
goodness of fit and independence tests, 479–480
hypothesis testing, 386–390, 404–405
inferences about two populations, 442–443
interval estimations, 339–341
multiple regression analysis, 599–600
population proportions, hypothesis testing, 386–390
random sampling applications, 302
regression analysis applications, 548–550
known applications, 339–340, 386–388
unknown applications, 340, 388
test of independence, 466–467
Expected frequencies, test of independence,
463–467, 472
Expected value, 202–203
binomial probability distribution, 214–215
formulas, 226–227
sample distribution of p, 289–290, 299
sample distribution of x, 279–280, 299
Experimental design
analysis of variance and, 414–419
data collection, 416–417
Experimental outcomes, formulas, 226
Experimental statistical studies, 11–13
Experimental units, 415
Experiments
binomial probability distribution, 207–215
probability and, 150–155
Exploratory data analysis
box plot, 111–112
five-number summary, 110–111
stem-and-leaf display, 49–54
F
Factor, 415
Factorial combinations, counting rule, 154
Finance, statistical applications, 4
Finite population correction factor, 280–281
Fisher, Ronald Aylmer, 415
Five-number summary, 110–111
Food and Drug Administration (FDA) (U.S.), 393
Food Lion, 305
Frame, defined, 267
Frequency distribution
categorical data, 33–34
crosstabulation, 56–60
cumulative, 44
grouped data, 127–128
quantitative data, 39–41
F test
multiple regression analysis, 569–571, 588
residual analysis validation, 528–531
simple linear regression, 511–513
G
Galton, Francis (Sir), 485
Gauss, Carl Friedrich, 490
Goodness of fit test
Excel applications, 479–480
Minitab applications, 478–479
multinomial population proportion, 457–460, 472
regression model, 506–508
Gosset, William Sealy, 312
Government agencies, as data source, 11
Graphical data summary, 61–65. See also
Descriptive statistics
binomial probability function, 213–215
discrete probability distribution, 199–200
Grouped data, 126–127
formulas, 134
H
Histograms
population mean, unknown, 317–318
quantitative data, 42–43
relative frequency, simple random samples,
278–279
skewness of distribution, 104
variability measurements, 96–97
Hypergeometric probability distribution, 221–223
formula, 227
Hypergeometric probability function, 222–223
formula, 227
Hypothesis tests
alternative hypothesis, 346–349
analysis of variance, 417–419
completely randomized design, 420–428
equality of k population means, observational
study, 427–428
Excel applications, 386–390
experimental design, 416–417
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Index 677
Hypothesis tests (continued)
F distribution, population variance, 423–424
inferential difference,
1
and
2
, 397–398,
403–405, 432
interval estimation, 362–363
matched samples, 410–411
Minitab applications, 385–386, 477–478
null hypothesis, 346–349
population means,
1
and
2
known, 397–398
population means,
1
and
2
unknown, 403–405
population mean known, 352–363
population mean unknown, 367–370
population proportion, 373–375, 452–453
proportions of multinomial population,
456–460, 472
StatTools applications, 391, 445, 482
type I and type II errors, 349–351
I
Independence, test of, 463–467, 472, 478–479
Excel applications, 479–480
Minitab applications, 478–479
StatTools, 482
Independent events
conditional probability, 174
multiplication law, 186
Independent random sample
population means,
1
and
2
known,
394–398, 432
population means,
1
and
2
unknown,
402–403, 432
Independent sample design, population means,
409–411
Independent variables
defined, 485
multiple coefficient of determination, 565
multiple regression analysis, 558–559, 568–573,
578–583
regression model, 506–508
residual plot against, 529–531
Indicator variable, multiple regression analysis,
580–583
Individual value of y
formula, 536
prediction interval, 519–521
Inferences
difference between two population proportions,
450–453
Excel applications, 442–443
Minitab applications, 440–441
population means, matched samples, 409–411
population means,
1
and
2
known, 394–398
population means,
1
and
2
unknown,
401–405
StatTools applications, 444–446
Internal company records, as data source, 10
International Paper, 553
Interquartile range, 97–98
formula, 133
Intersection of A and B, 166–168
Interval estimation
computer-based ANOVA, 426
defined, 305
difference between two populations, 450–452, 471
estimated regression equation, 517–518
Excel applications, 339–341
hypothesis tests and, 362–363
Minitab applications, 338
multiple regression analysis, 576–577
population mean, sample size, 322–323
population mean, known, 306–310
population mean, unknown, 313–316
population means,
1
and
2
known, 394–398, 432
population means,
1
and
2
unknown,
401–403, 432
population proportion, 325–327, 331
sample size for, 331
StatTools applications, 341–343, 445
summary, 318–319
Interval scale, 6
ith residual
formula, 536
residual analysis validation, 527–531
simple linear regression, 499–503
J
John Morrell & Company, 345
Joint probability, 172
Judgment sampling, 297
K
k levels, categorical variables, multiple regression
analysis, 582–583
L
Least squares criterion
multiple regression analysis, 555–559, 588
simple linear regression, 488–492, 535
Length intervals, Poisson probability distribution, 220
Levels of significance, type I/type II errors,
hypothesis tests, 350–351
Linear approximation, nonlinear relationships,
513–514
Linear regression. See Simple linear regression
Location measures, 88–93
mean, 88–89
median, 89–90
mode, 90
percentiles, 91–92
quartiles, 92–93
Lower class limit, frequency distribution, 40–41
Lower tail test, population mean known, 357–358
M
Marginal probability, 172–173
Margin of error
confidence interval, estimated regression
equation, 518–521
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678 Index
Margin of error (continued)
defined, 305–306
population mean, sample size, 321–323
population mean, known, 306–310
population mean, unknown, 313–316
population proportion, 289–293, 324–327
prediction interval, 519–521
simple linear regression, 
1
confidence interval,
510–511
Marketing, statistical applications, 4
Matched samples
Excel applications, 443
population means, 409–411, 441
StatTools applications, 446
Meadwestvaco Corporation, 266
Mean
Excel applications, 144
numerical measures, 88–89
population mean, 89, 133
sample mean formula, 133
weighted mean, 125–126
z-scores, 105
Mean square due to error (MSE)
computer-based ANOVA, 426
F distribution, population variance, 423–424
formula, 535, 588
F test, simple linear regression, 512–513
multiple regression analysis, 569–571
significance test, simple linear regression,
508–509
within-treatments estimates, population variance,
422, 433
Mean square due to treatments (MSTR)
between-treatments estimates, population vari-
ance, 421–422, 433
F distribution, population variance, 423–424
Mean square regression (MSR)
formula, 536, 588
F test, simple linear regression, 511–513
multiple regression analysis, 569–571
Mean value, confidence interval, estimated
regression equation, 518–521
Measurement, scales of, 6–7
Media, statistics in, 2–3
Median
50th percentile as, 92
Excel applications, 144
location measurement, 89–90
Microsoft Excel. See Excel (Microsoft)
Minimum sample size, population mean, 322–323
Minitab
analysis of variance results, 425–426, 442
binomial probability experiments, 215
box plots, 112, 143
confidence interval, population mean,
unknown, 315–316
covariance and correlation, 143
descriptive statistics, 141–143
discrete probability distributions, 230
goodness of fit and independence testing,
478–479
hypothesis testing, 385–386, 404–405
interval estimates using, 338–339
matched samples, population means, 441
multiple regression analysis, 557–559, 570–571,
579–583, 599
population proportion inferences, 477–478
random sampling applications, 301
regression analysis applications, 547–548
simple linear regression applications, 523–524
test of independence, 466–467
two-population inferences, 440–441
Mode
Excel applications, 144
location measurement, 90
Multicollinearity, multiple regression analysis,
572–573
Multimodal data, 90
Multinomial population, proportions of, hypothesis
testing, 456–460
Multiple coefficient of determination,
564–565, 588
Multiple regression
categorical independent variables, 578–583
coefficient interpretations, 558
defined, 554
estimated regression equation, 554–555,
576–577, 588
Excel applications, 599–600
formulas, 587–588
F test, 569–571
least squares method, 555–559
Minitab applications, 557–559, 570–571,
579–583, 599
model, 554–555, 567–568
multicollinearity, 572–573
multiple coefficient of determination, 564–565
regression equation, 554–555
StatTools applications, 600–601
test for significance, 568–573
t test, 571–572
Multiple-step experiments, probability, 151–152
Multiplication law
formula, 186
independent events, 174–175
Mutually exclusive events, probability and, 168
N
95% confidence interval, multiple regression
analysis, 576–577
Nominal scale, 6
Nonlinear relationship, linear approximation,
513–514
Nonprobability sampling, 296–297
Normal probability distribution. See Probability;
Probability distribution
Null hypothesis
analysis of variance, 417–419
defined, 345–349
equality of k population means, observational
study, 427–428
experimental design, 416–417
one-tailed tests, population mean known, 353–358
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Index 679
Null hypothesis (continued)
population mean,
1
and
2
, 397–398
population proportion, 452–453
simple linear regression, t test, 509–510
summary of forms for, 348–349
test of independence, 463–467
Numerical measures
association between two variables, 116–122
Chebyshev's theorem, 105–106
distribution shape, 103–104
empirical rule, 106–107
exploratory data analysis, 110–112
grouped data, 126–128
of location, 88–93
outlier detection, 107
variability, 96–100
weighted mean, 125–126
z-scores, 104–105
O
Observation
defined, 6
independent, analysis of variance, 417
Observational statistical studies, 11–13
equality of k population means, 427–428
experimental design, 415
Observed frequencies, test of independence,
463–467
Oceanwide Seafood, 149
Ogive graph, quantitative data, 44–45
One-tailed tests
critical value approach, 356–358
goodness of fit test, 458–460
population mean known, 352–358
population mean unknown, 367–368
p-value approach, 354–356
test of independence, 463–467
Ordinal scale, 6
Outlier detection, 107
Overall sample mean
analysis of variance, 418–419, 433
completely randomized design, analysis of
variance, 420–428
P
Parameters
multiple regression analysis, 581–583
population characteristics, 268
Pareto, Vilfredo, 35
Pareto chart, 35
Partitioning, analysis of variance table, 425, 433
Pearson, Karl, 485
Pearson product moment, correlation coefficient,
120–121, 133–134
Percent frequency distribution
audit time data, 40–41
categorical data, 34
crosstabulation, 56–60
cumulative, 44
quantitative data, 41
Percentile, 91–92
Perfect positive linear relationship, scatter diagram, 121
Permutations, counting rule, 154–155, 185
Pie charts, categorical data, 35–36
Planning value
population proportion, 326–327
population standard deviation, 322–323
Point estimator/point estimation
difference between two populations, 450–452, 471
estimated regression equation, 517
margin of error, 305–306
numerical measures, 88
population mean, unknown, 315–316, 431
population means,
1
and
2
known, 395–398, 431
sampling procedures, 273–275
simple linear regression, 
1
confidence interval,
510–511
Poisson, Siméon, 218
Poisson distribution, 218–220
Poisson probability function, 218–220
formula, 227
Pooled estimates/pooled estimator
analysis of variance, 419
population proportion, 452–453, 472
Pooled StDev, computer-based ANOVA, 426
Population
covariance, 117–118, 133
infinite sample population, 270–271
normal distribution, 281
parameters, numerical measures, 88
proportion, 324–327, 331
sampled population, 267
statistical inference, 15
without normal distribution, 281–283
Population mean(s)
basic properties, 284–285
equality of, (k population means), 427–428
Excel applications, 339–341, 442–443
formula for, 133–134
inferential difference,
1
and
2
, 394–398
location measures, 89
matched samples, inferences about, 409–411
Minitab applications, 338–339, 385–386, 440–441
sample size, 321–323
known, 306–310, 331, 352–363, 385–388,
394–398
unknown, 312–319, 331, 367–370, 385–386,
388, 391, 401–405
StatTools applications, 341–343, 391, 444–446
Population proportion
difference between two populations, inferences
about, 450–453
Excel applications, 340–341, 388–390
hypothesis tests, 373–375
Minitab applications, 338–339, 386, 477–478
multinomial population, hypothesis testing,
456–460
sample size formula, 331
StatTools applications, 343, 391, 480–482
Population variance, 98–99
between-treatments estimates, 421–422
formulas, 133–134
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680 Index
Population variance (continued)
F test, 423–424
within-treatments estimates, 422
Posterior probability, 178–183
Prediction interval
estimated regression equation, 517
formula, 536
multiple regression analysis, 576–577
Prior probability, 178–183
Probability
addition law, 165–168
assignment of, 155–157
Bayes’theorem, 178–182
complement of events, 164–165
conditional probability, 171–175
counting rules, combinations, and permutations,
151–155
defined, 150
events and, 160–162
experiments, 150–151
independent events, 174
multiplication law, 174–175
relative frequency and, 157–158
sample distribution of p, 292–293
sample mean, population, 285–286
tabular approach, 182
Probability density function, 197–200
Probability distribution
binomial, 207–215
discrete probability distributions, 197–200
hypergeometric probability, 221–223
Poisson probability, 218–220
population mean, unknown, 312–318
Probability sampling, 296–297
Production, statistical applications, 4
p-values
chi-square distribution, goodness-of-fit test,
458–460
F distribution, population variance, 423–424
one-tailed tests, population mean known,
354–356
population mean,
1
and
2
, 398, 404–405
population proportion, hypothesis tests,
374–375, 453
simple random samples, 278–279
two-tailed tests, population mean known,
359–363
Q
Qualitative variables, 568–573
Quantitative data, 7. See also Categorical data
cumulative distribution, 44
descriptive statistics, 33, 39–45
dot plot, 41–42
frequency distribution, 39–41
histogram, 42–43
ogive, 44–45
relative frequence/percent frequency
distribution, 41
Quantitative variable, 7
Quartiles, 92–93
R
Random experiments, probability, 158
Randomization, experimental design, 415
Random numbers, simple random sample, 268–269
Random samples
Excel applications, 302
infinite population, 270–271
Minitab applications, 301
simple, 268–270
StatTools applications, 302–303
stratified random sampling, 295
Random variables, 194–196
standard deviation, 203–204, 266
Range, 97
Ratio scale, 6–7
Regression analysis. See also Multiple regression
analysis; Simple linear regression
computer solutions, 523–524
Excel applications, 548–550
limitations of, 513–514
Minitab applications, 547–548
model assumptions, 506–508
residual analysis validation, 527–531
StatTools, 550–551
Regression coefficients, multiple regression
analysis, 558–559
Regression equation
estimated, 486–487, 517–521
formulas, 535–536
multiple regression analysis, 554–555, 568–571,
576–577
simple linear regression, 485–486
Regression model
assumptions, 506–508
multiple regression analysis, 554–555,
567–568
simple linear regression, 485–486
Rejection rule
F distribution, population variance, 424
F test, simple linear regression, 512–513
multiple regression analysis, 570–572
Relative frequency distribution
audit time data, 40–41
categorical data, 34
crosstabulation, 56–60
cumulative, 44
formula, 66
probability assignment, 156–158
quantitative data, 41
Replication, experimental design, 416
Research hypothesis, 346–349
Residual analysis, model assumptions, 527–531
Residual plot
dependent variable, 531
independent variable x, 529–531
Response variable, 415, 417
S
Sample correlation coefficient, 120–122
formula, 535
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Index 681
Sample correlation coefficient (continued)
formulas, 133–134
multiple regression analysis, 573
Sample covariance, 116–120
Sample mean, 88–89
analysis of variance, 418–419, 432
completely randomized design, analysis of
variance, 420–428
experimental design, 416–417
formulas, 133–134
grouped data, 126–127
population mean, known, 306–310
probability, simple random population sample,
285–286
Sample points
events as, 160–162
probability experiments, 150–151
Sample population, 267
Sample/sampling
business applications, 266–267
cluster sampling, 295–296
convenience sampling, 296–297
correlation coefficient, 122
covariance, 116–118, 133
distribution, simple linear regression, t test, 509–510
distribution of p, 289–293, 324–327
distribution of x, 278–286, 358–363
distributions, 276–277, 417–419, 423–424
finite population, 268–269
infinite population, 270–271
judgment sampling, 297
mean, formulas, 133–134
point estimation, 273–275
selection, 268–271
simple random sample, 268–270
statistical inference, 15
stratified random sampling, 295
systematic sampling, 296
with/without replacement, 269–270
Sample size
determination, 321–323
interval estimate, population mean, 331
matched samples, 410–411
population mean, unknown, 315–316
population proportion, 326–327
sampling distribution of x, 285–286
StatTools applications, 343
Sample space, probability experiments, 150
Sample statistics, numerical measures, 88
Sample survey, 15
Sample variance, 98–99
completely randomized design, analysis of
variance, 420–428, 432
formulas, 133–134
grouped data, 126–127
Scales of measurement, 6–7
Scatter diagrams
correlation coefficient, 121
covariance measurements, 118–120
descriptive statistics, 58–60
multiple regression analysis, 556–559, 582–583
simple linear regression, 488–492
known
Excel applications, 339–340, 386–388
formula, 331
hypothesis tests, 352–363
inferential difference,
1
and
2
known, 394–398
Minitab applications, 338, 385
population mean, 306–310, 331, 352–363,
385–388, 394–498
two-tailed test, 358–363
Sigma squared (
2
) estimation
formula, 535
simple linear regression, 508–509
unknown, 331
Excel applications, 340, 388
hypothesis tests, 367–370
margin of error and interval estimate, 313–316
Minitab applications, 338, 385–386
one-tailed tests, 367–368
population mean, 312–319, 331, 367–370,
385–386, 388, 391, 401–405
sample size, 316–318
StatTools applications, 341–342, 391
two-tailed tests, 368–370
Significance test
multiple regression analysis, 568–573
simple linear regression, 508–514
Simple linear regression
ANOVA table, 512–513
coefficient of determination, 499–503
computer solution, 523–524
confidence for 
1
, 510–511
confidence interval, mean value of y, 518–519
correlation coefficient, 502–503
defined, 485
estimated regression equation, 486–487, 517–521
estimation process, 487
Excel applications, 548–550
formulas, 535–536
F test, 511–512
interval estimation, 517
least squares method, 488–492
Minitab applications, 547–548
model, 485–487, 506–508, 535
point estimation, 517
prediction interval, individual value of y, 519–521
regression equation, 485–486
regression model, 485–486
residual analysis, 527–531
residual plot against x, 529–531
residual plot against y, 531
sigma squared (
2
) estimation, 508–509
significance testing, 508–514
StatTools, 550–551
t test, 509
Simple random sample, 268–270
completely randomized design, analysis of
variance, 420–428
population mean, known, 306–310
sample size, 285–286
sampling distribution of x, 283–285
Simpson’s paradox, crosstabulation, 57–58
Single-factor experiment, 415
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682 Index
Skewness levels
distribution shape, 103–104
histogram, 43
Small Fry Design, 87
Squared deviation, 98–99
Standard deviation
discrete random variable variance, 204
Excel applications, 144
formula, 133
sample distribution of p, 290, 299
sample distribution of x, 280–281, 299
simple linear regression, formula, 536
simple linear regression, t test, 509–510
variability measurement, 100
z-scores, 105
Standard error
difference between two populations,
450–452, 471
of estimate, formula, 536
of estimate, significance test, simple linear
regression, 508–509
of mean, 133, 281, 290–291
point estimator,
1
–
2
known, 395–398, 432
of proportion, 290
Standardized value, z-scores, 104–105
Statistical inference, 15–16
Statistical modeling, data mining, 18
Statistical studies, as data source, 11–13
Statistics
business/economics applications, 2–4
computers and, 17
data components, 5–9
data mining, 17–18
data sources, 10–13
defined, 3
descriptive statistics, 13–15
ethical practice guidelines, 18–19
StatTools
analysis of variance results, 425–426, 446–447
descriptive statistics applications, 146–147
hypothesis testing, 391, 445
Interval estimate applications, 341–343
interval estimation, 445
matched samples, 446
multiple regression analysis, 600–601
population inferences, 444–446
population proportion applications, 480–482
random sampling applications, 302–303
regression analysis applications, 550–551
test of independence with, 482
Stem-and-leaf display
applications, 53–54
exploratory data analysis, 49–54
methods, 53
Stratified random sampling, 295
Subjective method of probability assignment,
156–158
Summation sign, mean, 88
Sum of squares due to error (SSE)
formula, 535
multiple regression analysis, 564–565, 569–571
significance test, simple linear regression, 508–509
simple linear regression, 499–503
within-treatments estimates, population variance,
422, 433
Sum of squares due to regression (SSR)
formula, 535
multiple regression analysis, 564–565, 569–571
simple linear regression, 500–503
Sum of squares due to treatments (SSTR),
between-treatments estimates, population
variance, 421–422, 433
Sum of squares of deviations, simple linear
regression, 489–492
Systematic sampling, 296
T
Tabular data summary, 61–65. See also Descriptive
statistics
Bayes’theorem, 182
binomial probabilities, 213–215
Tail distribution, population mean, unknown,
313–318
Test statistic
equality of k population means, observational
study, 427–428, 433
F distribution, population variance, 423–424
F test, 512–513, 588
goodness of fit test, 458–460, 472
hypothesis tests,
1
and
2
, 397–398, 403–405, 432
hypothesis tests, population mean unknown,
379, 403–405
matched samples, 410–411
multiple regression analysis, 570–571
one-tailed tests, population mean known,
353–358
population mean known, 397–398
population mean unknown, 403–405
population proportion, hypothesis tests, 374–375,
379, 452–453
test of independence, 463–467
Time intervals, Poisson probability distribution,
218–220
Time series data, 7–9
Total sum of squares (SST)
ANOVA table, 424–425, 433
formula, 535
multiple regression analysis, 564–565
simple linear regression, 500–503
Treatments, defined, 415
Tree diagrams
Bayes’theorem example, 180–182
binomial probability distribution, 210–215
multiple-step experiments, 152–155
Trendlines, 58–60
Trimmed mean, 93
t test
formula, 536
matched samples, 410–411
multiple regression analysis, 571–572, 588
population mean, unknown, 313–318, 402–403
population mean known, 397–398
residual analysis validation, 528–531
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Index 683
t test (continued)
unknown, two-tailed tests, 369–370
simple linear regression, 509–510
Two-tailed test
population mean,
1
and
2
, 398
population mean known, 358–363
population mean unknown, 368–370
Type I/type II errors, hypothesis tests, 349–351
U
Union of A and B, defined, 165–166
United Way, 449
Upper class limit
cumulative distributions, 44
frequency distribution, 40–41
Upper tail test
F distribution, population variance, 424
goodness of fit test, 458–460
population mean known, 357–358
test of independence, 463–467
V
Variability measures, 96–100
coefficient of variation, 100
interquartile range, 97–98
range, 97
standard deviation, 100
variance, 98–100
Variables
categorical, 7
defined, 5
dependent variable, 485, 506–508, 531
independent variables, 485, 506–508, 529–531,
558–559, 565, 568–573, 578–583
indicator variable, 580–583
quantitative, 7
Variance, 98–100
binomial probability distribution, 214–215, 227
discrete random variables, 203–204, 226
Excel applications, 144
Venn diagram
addition law, 165–166
complement of events, 164–165
conditional probability, 173–175
W
Weighted mean, 125–126
formula, 134
Within-treatments estimates
analysis of variance, 419
population variance, 422
Z
z-scores, 104–105
formula, 133
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.