Browse All Chapters of This Textbook
Chapter 2 - ProbabilityChapter 2.1 - Sample Spaces And EventsChapter 2.2 - Interpretations And Axioms Of ProbabilityChapter 2.3 - Addition RulesChapter 2.4 - Conditional ProbabilityChapter 2.5 - Multiplication And Total Probability RulesChapter 2.6 - IndependenceChapter 2.7 - Bayes’theoremChapter 2.8 - Random VariablesChapter 3 - Discrete Random Variables And Probability Distributions
Chapter 3.1 - Discrete Random VariablesChapter 3.2 - Probability Distributions And Probability Mass FunctionsChapter 3.3 - Cumulative Distribution FunctionsChapter 3.4 - Mean And Variance Of A Discrete Random VariableChapter 3.5 - Discrete Uniform DistributionChapter 3.6 - Binomial DistributionChapter 3.7 - Geometric And Negative Binomial DistributionsChapter 3.8 - Hypergeometric DistributionChapter 3.9 - Poisson DistributionChapter 4 - Continuous Random Variables And Probability DistributionsChapter 4.2 - Probability Distributions And Probability Density FunctionsChapter 4.3 - Cumulative Distribution FunctionsChapter 4.4 - Mean And Variance Of A Continuous Random VariableChapter 4.5 - Continuous Uniform DistributionChapter 4.6 - Normal DistributionChapter 4.7 - Normal Approximation To The Binomial And Poisson DistributionsChapter 4.8 - Exponential DistributionChapter 4.9 - Erlang And Gamma DistributionsChapter 4.10 - Weibull DistributionChapter 4.11 - Lognormal DistributionChapter 4.12 - Beta DistributionChapter 5 - Joint Probability DistributionsChapter 5.1 - Two Or More Random VariablesChapter 5.2 - Covariance And CorrelationChapter 5.3 - Common Joint DistributionsChapter 5.4 - Linear Functions Of Random VariablesChapter 5.5 - General Functions Of Random VariablesChapter 5.6 - Moment-generating FunctionsChapter 6 - Descriptive StatisticsChapter 6.1 - Numerical Summaries Of DataChapter 6.2 - Stem-and-leaf DiagramsChapter 6.3 - Frequency Distributions And HistogramsChapter 6.4 - Box PlotsChapter 6.5 - Time Sequence PlotsChapter 6.6 - Scatter DiagramsChapter 6.7 - Probability PlotsChapter 7 - Point Estimation Of Parameters And Sampling DistributionsChapter 7.2 - Sampling Distributions And The Central Limit TheoremChapter 7.3 - General Concepts Of Point EstimationChapter 7.4 - Methods Of Point EstimationChapter 8 - Statistical Intervals For A Single SampleChapter 8.1 - Confidence Interval On The Mean Of A Normal Distribution, Variance KnownChapter 8.2 - Confidence Interval On The Mean Of A Normal Distribution, Variance UnknownChapter 8.3 - Confidence Interval On The Variance And Standard Deviation Of A Normal DistributionChapter 8.4 - Large-sample Confidence Interval For A Population ProportionChapter 8.6 - Bootstrap Confidence IntervalChapter 9 - Tests Of Hypotheses For A Single SampleChapter 9.1 - Hypothesis TestingChapter 9.2 - Tests On The Mean Of A Normal Distribution, Variance KnownChapter 9.3 - Tests On The Mean Of A Normal Distribution, Variance UnknownChapter 9.4 - Tests On The Variance And Standard Deviation Of A Normal DistributionChapter 9.5 - Tests On A Population ProportionChapter 9.7 - Testing For Goodness Of FitChapter 9.8 - Contingency Table TestsChapter 9.9 - Nonparametric ProceduresChapter 9.10 - Equivalence TestingChapter 10 - Statistical Inference For Two SamplesChapter 10.1 - Inference On The Difference In Means Of Two Normal Distributions, Variances KnownChapter 10.2 - Inference On The Difference In Means Of Two Normal Distributions, Variances UnknownChapter 10.3 - A Nonparametric Test For The Difference In Two MeansChapter 10.4 - Paired T-testChapter 10.5 - Inference On The Variances Of Two Normal DistributionsChapter 10.6 - Inference On Two Population ProportionsChapter 11 - Simple Linear Regression And CorrelationChapter 11.2 - Simple Linear RegressionChapter 11.4 - Hypothesis Tests In Simple Linear RegressionChapter 11.6 - Prediction Of New ObservationsChapter 11.7 - Adequacy Of The Regression ModelChapter 11.8 - CorrelationChapter 11.9 - Regression On Transformed VariablesChapter 11.10 - Logistic RegressionChapter 12 - Multiple Linear RegressionChapter 12.1 - Multiple Linear Regression ModelChapter 12.2 - Hypothesis Tests In Multiple Linear RegressionChapter 12.4 - Prediction Of New ObservationsChapter 12.5 - Model Adequacy CheckingChapter 12.6 - Aspects Of Multiple Regression ModelingChapter 13 - Design And Analysis Of Single-factor Experiments: The Analysis Of VarianceChapter 13.2 - Completely Randomized Single-factor ExperimentChapter 13.3 - The Random-effects ModelChapter 13.4 - Randomized Complete Block DesignChapter 14 - Design Of Experiments With Several FactorsChapter 14.3 - Two-factor Factorial ExperimentsChapter 14.4 - General Factorial ExperimentsChapter 14.5 - 2k Factorial DesignsChapter 14.6 - Blocking And Confounding In The 2k DesignChapter 14.7 - Fractional Replication Of The 2k DesignChapter 14.8 - Response Surface Methods And DesignsChapter 15 - Statistical Quality ControlChapter 15.3 - Xbar And R Or S Control ChartsChapter 15.4 - Control Charts For Individual MeasurementsChapter 15.5 - Process CapabilityChapter 15.6 - Attribute Control ChartsChapter 15.7 - Control Chart PerformanceChapter 15.8 - Time-weighted ChartsChapter 15.9 - Other Spc Problem-solving Tools
Book Details
This best-selling engineering statistics text provides a practical approach that is more oriented to engineering and the chemical and physical sciences than many similar texts. It is packed with unique problem sets that reflect realistic situations engineers will encounter in their working lives. This text shows how statistics, the science of data is just as important for engineers as the mechanical, electrical, and materials sciences.
Sample Solutions for this Textbook
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Given info: There are 2% and 1% of the sample shipped in small and large packages, respectively,...Given info: The random variable X be equally likely to takes the values 18,14, and 38. Calculation:...Given info: The probability density function for the time is, f(x)=0.04 for 50<x<75....Calculation: It is given that, xyfXY(x,y)00140118101811142214 The joint probability mass function of...Calculation: The given information is that, the data represent the monthly absolute estimates of...Given info: A random sample of n transistors is taken. The transistors has a life which is...Given info: The confidence interval for μ with known standard deviation σ is x¯−zα1σn≤μ≤x¯+zα2σn...Calculation: The SE Mean from the sample standard deviation is, σN. Substitute σ=1.5 and SE...Given info: A computer output is provided for two sample T-test. Calculation: Test statistic:...
Given info: It is given that Y is the response variable and x is the predictor variable....Calculation: An incomplete regression analysis table is given. Multiple linear regression model: A...Given info: The data represents the ANOVA table with missing values. Calculation: The levels of...Calculation: The data represents the two factorial experiments to investigate the effect of pH and...Calculation: The data shows the diameter of fuse pins used in an aircraft engine taken for 25...
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