TAILIEUCHUNG - Ebook Fundamental statistics for the behavioral sciences (7th edition): Part 2

(BQ) Part 2 book "Fundamental statistics for the behavioral sciences" has contents: Choosing the appropriate analysis, nonparametric and distribution free statistical tests, repeated measures analysis of variance, factorial analysis of variance, one way analysis of variance,.and other contents. | 13 Hypothesis Tests Applied to Means: Two Related Samples Concepts that you will need to remember from previous chapters t distribution: Sampling distribution of the t statistic when the null hypothesis is true. Often called the “central t distribution“ Standard error: The standard deviation of the sampling distribution of a statistic Degrees of freedom: An adjusted value of the sample size, often N 2 1 or N 2 2 Null hypothesis: The hypothesis to be tested by a statistical test: H0 Research hypothesis: The hypothesis that the study is designed to test: H1 m, s, X, s: Mean and standard deviation of a population or a sample, respectively 335 336 Chapter 13 Hypothesis Tests Applied to Means: Two Related Samples T his chapter will move from the one-sample case to the two-sample case, but here we will assume that the two samples of data were provided by the same participants. At first glance it looks as if this situation creates problems, but it turns out that it is very easy to get around the issues that arise. We will also consider the question of when we would, and would not, want to use related samples. In Chapter 12 we considered the situation in which we had one sample mean 1X 2. We wanted to test to see if it was reasonable to believe that we would obtain such a mean if we had been sampling from a population with some specified population mean (which we denote m0). Another way of phrasing this is to say that we were testing to determine if the mean of the population from which we sampled (call it m1) could be equal to some particular value given by the null hypothesis 1m0 2 . In this chapter we will move away from the case in which we perform a test on the mean of a single sample of data. Instead we will consider the case in which we have two related samples and we wish to perform a test on the difference between their two means. (The same analyses apply to what are variously called repeated measures, matched samples, paired samples, correlated

• Tài chính doanh nghiệp
• Fundamental statistics
• Behavioral sciences
• The appropriate analysis
• Distribution free statistical tests
• Repeated measures analysis
• Factorial analysis
• Analysis of variance
• The behavioral sciences
• Basic concepts
• Displaying data
• Central tendency
• The normal distribution
• Sampling distributions
• Introduction to fluid mechanics
• Fundamental concepts
• Fluid statistics
• Differential analysis
• Incompressible inviscid flow
• Dimensional analysis and similitud
• Internal incompressible viscous flow
• Profit variance analysis
• Variance analysis
• Cost accounting
• Fundamentals of cost accounting
• Cost analysis
• Cost management systems
• Lecture Basic Statistics
• Bài giảng Thống kê cơ bản
• Repeated measures analysis of variance
• Individual differences
• The denominator of the F‑ratio
• Two factor analysis of variance
• Two factor designs
• Factorial designs
• Fruit yield
• Genetic analysis of variance
• Sponge gourd
• Sponge gourd [Luffa cylindrica (Roem ) L ]
• Analysis of variance fruit yield
• Management control systems
• Statistical techniques
• Statistical techniques in business
• Statistical techniques in economics
• Descriptive statistics
• F distribution
• Statistics for business
• Describing data
• Equal variances
• Basic statistics
• Confidence interval
• Mean performance
• Zea mays L
• Twelve characters studied in maize
• Maize (Zea mays L )
• Diallel analysis
• Combining ability effects
• GCA variance
• SCA variance
• Over environments
• Experimentwise α level
• Measuring effect size
• The scheffe test
• Nuclear engineering and technology
• Analysis of inconsistent source sampling
• Monte carlo weight window variance reduction methods
• Moderate levels of splitting and roulettin
• The consistent adjoint driven importance sampling
• Genetic variance
• Lady finger
• Genetic parameters
• Recessive alleles
• Abelmoschus esculentus L
• Genetic advance
• Genotypic coefficient of variation
• Genotypic variance
• Phenotypic coefficient of variation
• phenotypic variance
• Line × tester
• Yield contributing traits in tomato
• Heterotic expression studies
• BMC Genomics
• Genetic connectedness
• Prediction error of variance
• Variance of unit effect estimates
• Whole genome prediction
• Agricultural species
• Differentially expressed genes
• Between study variance
• Random effects model
• Meta analysis
• Alzheimer’s microarray datasets
• Agricultural science
• Separation of genetic variance components
• Eight faba bean genotypes
• Combining ability
• Twenty eight crosses
• Variance component analysis
• Functional genomics
• Enrichment or depletion analysis
• Regulatory genome
• Splice sites
• DNA methylated regions
• Half diallel
• Yield component
• Half diallel analysis in cowpea
• Analysis of variance for diallel analysis
• Components traits in cowpea
• BMC Bioinformatics
• Mass spectrometry
• Validation biomarkers
• Technical variability
• Experimental design
• Nonparametric statistics
• Additional topics
• Regression analysis
• Multiple variable regression analysis
• Two variable regression analysis
• Quantitative Investment Analysis
• Investment process
• Quantitative methods
• Cash flow
• Mean variance analysis
• Efficient portfolios