- a
- Air freshener project
- screening experiment plan 24–37
- statistical analyses plan 37–55
- Akaike Information Criteria (AIC) 283
- Alternative hypothesis 18
- Analysis Of VAriance (ANOVA) 45–46
- b
- Bar Charts 266–267
- Binary logistic model 291, 292
- Binary logistic regression model 277–284, 288–289
- Blocking 26, 28–29
- Box‐Behnken design 139, 147–150
- Boxplots 12–13
- c
- Central composite designs (CCDs) 137, 142–144
- Central tendency 41
- fragrances 96
- measures –8
- Chi‐square test 269–270
- Coded coefficients 160–161
- Coded units 51
- Coefficient of determination 239
- Coefficient of variation 11
- Condom project
- formulations 73
- means between two groups compare 81–85
- two proportions test 85–92
- two‐sample hypothesis test 73
- variability between two groups compare 74–81
- variable settings 73
- Confidence interval (CI) 15–17, 231–232
- Confidence level 17
- Consumer voice 257
- design of experiments‐top score project 284–291
- “Top‐Two Box” project 259–284
- Contingency table. See Cross tabulations
- Contour plots 165–166, 209–210, 212, 232, 234
- Correlation analysis 218–219
- Correlation coefficients 215–224
- Cross tabulations 264–265
- d
- Dependent variables 234
- Descriptive analysis –5
- Design of experiments(DOE)‐top score project
- binary logistic regression model 288–289
- binary response variable stratifying by formulations 286–288
- factorial design 284–285
- reduce the model 290–291
- statistical analyses 285–291
- variables setting 285
- Deviations 10
- e
- Experiments 24–25
- Explanatory variables 234
- Extreme vertices design
- with lower and upper limits for components 187–195
- with linear constraints for components 195–198
- f
- Face‐centered design 139
- Factorial designs 26–27
- basic principles of 28–30
- Factors 24–25
- Fixed batch factor
- data collection worksheet 244–245
- predict response values 249–250
- shelf life estimate 245–249
- Fragrance Project 58
- aims 93
- “Appropriateness” 94–97
- “Difference:A_B” 98–100
- paired t‐test 100–103
- variables setting 93
- Frequency distributions
- g
- Gastroesophageal reflux disease (GERD) 213
- correlation coefficients 215–224
- dyspepsia 215
- heartburn 215
- multicenter randomized pilot study 215
- multiple linear regression 224–226, 232–243
- predict response values 231–232
- quantitative variables evaluation 215–243
- reduce the model 226–231
- regurgitation 215
- scatterplot 215–224
- scores 215
- General factorial design 110
- GERD. See Gastroesophageal reflux disease (GERD)
- Goodness of fit 239–240
- Graphic techniques 165, 209
- h
- Heartburn
- contour plots 234
- prediction for 232
- Histograms 13
- Hypothesis tests 18–19
- i
- Independent 28
- Independent predictors 234
- Inferential analysis –5, 13
- Inferential problems 14–15
- Intercept 51
- Interquartile range (IQR)
- m
- Mean
- Median
- Mixture designs
- components 167
- designed factor level combinations 199
- experimental design 167–199
- linear constraints 195–198
- lower and upper limits for components 187–195
- lower limits for components 175–178
- mixture‐amount experiment 181–183
- mixture‐process variable experiment 178–181
- response optimization 207–209
- response surface and locate the optimum 209–212
- response variables 200–202
- second‐order model 202–207
- simple mixture experiment 167–175, 183–187
- statistical analyses 199–200
- variables setting 200
- Mixture experiment 167, 170–173
- Mixture models 199
- Mix‐Up Project
- components 167
- designed factor level combinations 199
- experimental design 167–199
- linear constraints 195–198
- lower and upper limits for components 187–195
- lower limits for components 175–178
- mixture‐amount experiment 181–183
- mixture‐process variable experiment 178–181
- response optimization 207–209
- response surface and locate the optimum 209–212
- response variables 200–202
- second‐order model 202–207
- simple mixture experiment 167–175, 183–187
- statistical analyses 199–200
- variables setting 200
- Multi‐factor ANOVA 45
- Multiple linear regression model 224–226, 243
- n
- Non‐central tendency 41
- Normal distribution 54, 241
- Normal probability plots 242
- Null hypothesis 18
- Numeric variable
- o
- Observations 28
- Odds ratios 279–283
- One‐way ANOVA 45
- Optimal design 192, 193
- Overlaid plots 165–166, 209–210, 232
- p
- Paired t‐test 100–103
- Pareto chart 43
- Pearson correlation. See Correlation coefficients
- Pie Charts 266–267
- Point estimate 15–16
- Polymer Project
- Box‐Behnken Design 147–150
- central composite designs 139–144
- experimental design 139–150
- face‐centered CCD 145–147
- product's elasticity 138
- reduce the model 157–161
- response optimization 162–164
- response surface and locate the optimum 165–166
- response variables 150, 151–153
- second‐order model 138, 153–157
- statistical analysis 150
- variables setting 151
- Population parameters estimation 15–17
- Prediction interval (PI) 232
- Product validation 213
- p‐value 19–20, 44, 159
- q
- Quantiles
- Quantitative variable , 80, 83
- Quartiles
- r
- Random
- Random batch factor
- data collection worksheet 250–251
- shelf life estimate 252–256
- stability studies 250
- Randomization 28, 54
- Randomized complete block design (RCBD) 28–29
- Random samples 54
- Range
- Regression analysis 218–219
- Regression equation 229–230
- Regression model 51, 234–235
- Regurgitation
- Replication 29–30
- Residuals 239
- analysis 54–55, 240–242
- vs. fitted values plots 242
- vs. order plots 241
- Response surface designs
- Box‐Behnken Design 147–150
- central composite designs 139–144
- experimental design 139–150
- face‐centered CCD 145–147
- product's elasticity 138
- reduce the model 157–161
- response optimization 162–164
- response surface and locate the optimum 165–166
- response variables 150, 151–153
- second‐order model 138, 153–157
- statistical analysis 150
- variables setting 151
- Response surface model 110, 114–118
- Response variables 24–25, 111–114, 234
- R‐squared 49–50
- R‐squared (R‐sq, R2) 49–50, 121, 160, 205, 228
- s
- Sample
- Scatterplot 215–224
- Scatterplot of thickness
- Screening experiment 24–37
- alternately, choose the desired fractional design 32–33
- assign the designed factor level combinations to the experimental units 33–37
- collect data for the response variable 33–37
- create a full factorial design 30–32
- Shapes of data distributions –6
- for quantitative variables –7
- Simple linear regression models 236–239
- Simplex centroid design 174
- create 167–175
- mixture‐amount experiment 181–183
- mixture‐process variable experiment 178–181
- simple mixture experiment with lower limits for components 175–178
- Simplex lattice design 167
- simple mixture experiment 183–187
- Spearman rank correlation 274–276
- Stain Removal Project 58
- aims 104
- general factorial experiment 104–109
- reduce the model 118–125
- response optimization 125–130
- response surface and locate the optimum 130–136
- response surface model 114–118
- response variables 111–114
- statistical analysis 110–111
- Standard deviation 10–11
- Statistical analyses 37–55
- Statistical unit
- Statistical variables –3
- Surface plots 165–166, 209–210, 232, 234
- t
- Throat Care project
- aims 59
- mean to a specified value compare 60–66
- one‐sample hypothesis test 59–60
- proportion to a specified value compare 67–72
- variable settings 60
- “Top‐Two Box” project
- binary logistic regression model 277–284
- χ2 test 267–270
- satisfaction scores by product 260–267
- variable “Satisfaction” 271–277
- variables setting 259
- Two‐level factorial designs 25, 27
- Two proportions test 91–92
- Two‐sample inferential problems 79–80
- Two‐sample t‐test 83–84
- Two variances test 80–81
- Two‐way table. See Cross tabulations
- v
- Variability , 41
- Variance 10–11
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