594 Index
Regression, multiple, 376
coeffi cients and prediction
equation, 361–362
example using, 371–385
F distribution, 353–355
multiple correlation, 359–360
output, interpreting, 358–359, 377
parameters, 353–356
prediction using, 355–356
t tests for coeffi cients, 362–363
Regression assumptions, testing
normal errors and plot, 370
observed vs. predicted values,
363–366
plotting residuals vs. predicted
values, 366–368
plotting residuals vs. predictor
variables, 368–369
Rejection region, 233, 234–235
Related, 291
Relative frequency, 183–184
Relative reference, 50
Replicates, 410
Residuals
analysis of discrimination data,
377–378
defi ned, 314
normal plot of, 378–379
predicted values and, 328
predicted values vs. plotting,
366–368
predictor variables vs. plotting,
368–369
testing for constant variance in, 332
testing for independence of,
332–335
testing for normal distribution of,
331–332
Review tab, 9
Ribbons
context-sensitive, 9
Ribbon tab, 7
types, 8–9
Robustness
defi ned, 243
t, 243–244
Row headings, 7
Runs test, 333
command, 576–577
S
Sample, 190
test commands, 569–574
Sampling command, 537–538
Sampling data commands, 556–557
Sampling distributions
creating, 206–212
defi ned, 206
standard deviation/error, 212
Saving work, 22–24
Scatter chart, 84
Scatter plots
adding moving average to,
446–447
breaking into categories, 117–120
commands, 558, 562
components of, 86–91
defi ned, 87
lagged values and, 438–440
matrix (SPLOM), creating,
343–345, 373–374, 562
regression data plotting and use
of, 320–323
variables, plotting, 120–123
S charts, 566
Scroll bars, 7
horizontal, 7
vertical, 7, 13
Seasonality
additive, 464
adjusting for, 471–473
autocorrelation function and,
470–471
boxplots and, 467–468
command, 577
example of, 464–473
line plots and, 468–470
multiplicative, 462–463
Shapes, measures of, 162–164
Sheet tabs, 7
Shewhart, Walter A., 488
Sign test, 253–255
command, 571
Signifi cance level, 233
Single-Factor command, 523
Skewness, 141, 162
negative, 141
positive, 141
Slope
correlation and, 336
defi ned, 314
Smoothing factor/constant, 449
Solver, 479–482
Somers’ D, 299, 306
Sorting data, 54–55, 71–75
custom, 307–309
Sparse cell, 299
SPC. See Statistical process control
Spearman’s rank correlation
coeffi cient, 337
Special causes, 489
SPLOM. See Scatter plots, matrix
Spreadsheets, 4
SQC. See Statistical quality control
SSE. See Error sum of squaresSST. See Sum of square for
treatment
Standard deviation/error, 161,
212, 451
control limits and, 494–495,
498–500
Standardize (data) command, 556
Standardized residual, 297
Starting
Excel, 5–6
Statistical analysis functions, list
of, 550–551, 584–586
Statistical inference
applying t test to two-sample
data, 259–264
confi dence intervals, 225–232
equality of variance, 258–259
hypothesis testing, 232–235
nonparametric test to paired data,
250–255
nonparametric test to two-sample
data, 265–267
t distribution, 240–250
two-sample t test, 255–257
Statistical process control (SPC),
488–490
Statistical quality control (SQC),
488–490
StatPlus, 2
About—command, 33, 579
ANOVA and, 395, 397
autocorrelation function and, 443
boxplots and, 172–173
checking availability of, 552
commands, 552–580
data points, identifying, 106
distribution statistics and,
162–163
exponential smoothing and, 474
frequency tables and, 134, 136–137
hidden data, 31
histograms and, 138, 143
installing fi les, 2–3
linked formulas, 32
loading, 24–28
Mann-Whitney test and, 265–266
mathematical and statistical
functions, 581–586
modules, 30–31
normal probability plot and,
201–205
Options command, 577–578
Pareto charts and, 513–516
percentiles and quartiles and,
151–154
random normal data and,
197–199
runs test and, 333