Search Results - "leaf"

Mixed models theory and applications with R / by Demidenko, Eugene, 1948-

Table of Contents: “…11.1 Mixed effects for clustered data 21.2 ANOVA, variance components, and the mixed model 41.3 Other special cases of the mixed effects model 61.4 A compromise between Bayesian and frequentist approaches 71.5 Penalized likelihood and mixed effects 91.6 Healthy Akaike information criterion 111.7 Penalized smoothing 131.8 Penalized polynomial fitting 161.9 Restraining parameters, or what to eat 181.10 Ill-posed problems, Tikhonov regularization, and mixed effects 201.11 Computerized tomography and linear image reconstruction 231.12 GLMM for PET 261.13 Maple shape leaf analysis 291.14 DNA Western blot analysis 311.15 Where does the wind blow? …”
An electronic book accessible through the World Wide Web; click to view

Mastering Lean Six Sigma advanced black belt concepts / by Taghizadegan, Salman, 1957-

Table of Contents: “…Analysis of concepts and strategies: advanced statistical analysis, achieving ultimate performance scientifically -- 9.1 Descriptive statistics -- 9.1.1 Descriptive statistics techniques and graphing: stem and leaf -- 9.1.2 Histogram -- 9.1.3 Measure of center tendency -- 9.1.4 Measures of variability -- 9.2 Descriptive measures -- 9.2.1 Measurement system analysis -- 9.2.2 Accuracy/bias -- 9.2.3 Stability (consistency) -- 9.2.4 Linearity -- 9.2.5 Gage repeatability and reproducibility (or Gage R&R) -- 9.2.6 Measurement system components -- 9.3 Probability distributions and concepts -- 9.3.1 Definition, experiment, outcome, and sample space -- 9.3.2 Probability of event (EI) as relative frequency -- 9.3.3 Marginal and conditional probabilities -- 9.3.4 The rules of probability (union of events) -- 9.3.5 The rules of probability (intersection of events) -- 9.4 Discrete random variables: probability distribution -- 9.4.1 Binomial probability distribution -- 9.4.2 Poisson probability distribution -- 9.4.3 The hypergeometric probability distribution -- 9.5 Continuous random variables probability distributions -- 9.5.1 Normal probability distribution -- 9.5.2 t-distribution -- 9.5.3 Normality test -- 9.5.4 Exponential distribution -- 9.5.5 Reliability engineering -- 9.6 Inferential statistics and sampling distribution -- 9.6.1 Random sampling and the distribution of the sample mean -- 9.6.2 Central limit theorem (CLT) -- 9.6.3 Confidence interval for the mean [mu] of normal population ([theta] is known) -- 9.6.4 Confidence interval for the mean [mu] of normal -- Population (([theta] is unknown) -- 9.6.5 Selecting the necessary sample size -- 9.7 Hypothesis testing, inferences procedures, and proportions testing -- 9.7.1 Hypothesis testing for the mean [mu] and variance ([theta]2) of the population -- 9.7.2 P-value application -- 9.7.3 Hypothesis testing using p-value approach (using equal mean) -- 9.7.4 Hypothesis testing on the mean [mu] of a normal population for small sample -- 9.7.5 Inference procedures for two populations: applying the concepts -- 9.7.6 Comparing two normal population means ([mu]1 - [mu]2) using two small, independent samples: apply the mechanics -- 9.7.7 Comparing the variance of two normal populations ([theta]12-[theta]22) using independent samples-f test (small sample size): apply the mechanics -- 9.7.8 Estimation and testing for population proportions -- 9.7.9 Confidence interval for a population proportion: large sample -- 9.7.10 Hypothesis testing for a population proportion -- 9.7.11 Comparing population proportion: two large independent samples -- 9.8 Advanced analysis of variance (ANOVA) -- 9.8.1 One-way analysis of variance -- 9.8.2 Randomized block design and analysis of variance -- 9.8.3 Two-way analysis of variance -- 9.9 Linear regression analysis -- 9.9.1 Scatter plots and correlation analysis -- 9.9.2 Simple linear regression model and analysis -- 9.9.3 Linear regression model -- 9.9.4 Least square criteria -- 9.9.5 Inferences on the slope [beta]1, concept: t-test -- 9.9.6 Confidence interval for B1 slope -- 9.9.7 Prediction by regression analysis: confidence interval for an individual y, given x -- 9.10 Multiple regression analysis -- 9.10.1 Multiple linear regression model building -- 9.10.2 Hypothesis testing and confidence interval -- 9.10.3 Polynomial and nonlinear regression model building -- 9.11 Tollgate review and deliverables for analysis phase -- 9.11.1 Analysis phase deliverables and checklist --…”
An electronic book accessible through the World Wide Web; click to view

Mastering Lean Six Sigma advanced black belt concepts / by Taghizadegan, Salman, 1957-

Table of Contents: “…Analysis of concepts and strategies: advanced statistical analysis, achieving ultimate performance scientifically -- 9.1 Descriptive statistics -- 9.1.1 Descriptive statistics techniques and graphing: stem and leaf -- 9.1.2 Histogram -- 9.1.3 Measure of center tendency -- 9.1.4 Measures of variability -- 9.2 Descriptive measures -- 9.2.1 Measurement system analysis -- 9.2.2 Accuracy/bias -- 9.2.3 Stability (consistency) -- 9.2.4 Linearity -- 9.2.5 Gage repeatability and reproducibility (or Gage R&R) -- 9.2.6 Measurement system components -- 9.3 Probability distributions and concepts -- 9.3.1 Definition, experiment, outcome, and sample space -- 9.3.2 Probability of event (EI) as relative frequency -- 9.3.3 Marginal and conditional probabilities -- 9.3.4 The rules of probability (union of events) -- 9.3.5 The rules of probability (intersection of events) -- 9.4 Discrete random variables: probability distribution -- 9.4.1 Binomial probability distribution -- 9.4.2 Poisson probability distribution -- 9.4.3 The hypergeometric probability distribution -- 9.5 Continuous random variables probability distributions -- 9.5.1 Normal probability distribution -- 9.5.2 t-distribution -- 9.5.3 Normality test -- 9.5.4 Exponential distribution -- 9.5.5 Reliability engineering -- 9.6 Inferential statistics and sampling distribution -- 9.6.1 Random sampling and the distribution of the sample mean -- 9.6.2 Central limit theorem (CLT) -- 9.6.3 Confidence interval for the mean [mu] of normal population ([theta] is known) -- 9.6.4 Confidence interval for the mean [mu] of normal -- Population (([theta] is unknown) -- 9.6.5 Selecting the necessary sample size -- 9.7 Hypothesis testing, inferences procedures, and proportions testing -- 9.7.1 Hypothesis testing for the mean [mu] and variance ([theta]2) of the population -- 9.7.2 P-value application -- 9.7.3 Hypothesis testing using p-value approach (using equal mean) -- 9.7.4 Hypothesis testing on the mean [mu] of a normal population for small sample -- 9.7.5 Inference procedures for two populations: applying the concepts -- 9.7.6 Comparing two normal population means ([mu]1 - [mu]2) using two small, independent samples: apply the mechanics -- 9.7.7 Comparing the variance of two normal populations ([theta]12-[theta]22) using independent samples-f test (small sample size): apply the mechanics -- 9.7.8 Estimation and testing for population proportions -- 9.7.9 Confidence interval for a population proportion: large sample -- 9.7.10 Hypothesis testing for a population proportion -- 9.7.11 Comparing population proportion: two large independent samples -- 9.8 Advanced analysis of variance (ANOVA) -- 9.8.1 One-way analysis of variance -- 9.8.2 Randomized block design and analysis of variance -- 9.8.3 Two-way analysis of variance -- 9.9 Linear regression analysis -- 9.9.1 Scatter plots and correlation analysis -- 9.9.2 Simple linear regression model and analysis -- 9.9.3 Linear regression model -- 9.9.4 Least square criteria -- 9.9.5 Inferences on the slope [beta]1, concept: t-test -- 9.9.6 Confidence interval for B1 slope -- 9.9.7 Prediction by regression analysis: confidence interval for an individual y, given x -- 9.10 Multiple regression analysis -- 9.10.1 Multiple linear regression model building -- 9.10.2 Hypothesis testing and confidence interval -- 9.10.3 Polynomial and nonlinear regression model building -- 9.11 Tollgate review and deliverables for analysis phase -- 9.11.1 Analysis phase deliverables and checklist --…”
An electronic book accessible through the World Wide Web; click to view