Review and prospectus
Question 1
Assume that a null hypothesis is true. Which of the following statements is true?
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A. A study with a larger sample is more likely than a smaller study to get the result that P < 0.05. B. A study with a larger sample is less likely than a smaller study to get the result that P < 0.05. C. A study with a larger sample is equally likely than a smaller study to get the result that P < 0.05. If H0 is true, the P value accurately describes the probability of an observed outcome If H0 is true, then the probability that t exceeds t0.05 is 0.05 With a larger n, the value of t0.05 is recalculated to account for the smaller variance of 𝑿!. Therefore, the probability of exceeding t0.05 is still 0.05. Question 2 Assume that a null hypothesis is false. Which of the following statements is true? A. A study with a larger sample is more likely than a smaller study to get the result that P < 0.05. B. A study with a larger sample is less likely than a smaller study to get the result that P < 0.05. C. A study with a larger sample is equally likely than a smaller study to get the result that P < 0.05. If H0 is not true, then the P value does not accurately describe the probability of an observed outcome IfHA istrueandn is raised, then this area increases IfHA istrue,then this area gives the chance that P<0.05 Learning objectives to review • Explain the difference between a random effect and a fixed effect. • Given the Sums of squares and degrees of freedom, complete an ANOVA table and compute the p value (using R). • Given the Sums of squares, Sum of products, and sample size, estimate a simple linear regression equation. • State the effects on statistical power of changing the value of key parameters. • Interpret an ANCOVA table in terms of main effects and interactions. • For all statistical tests, know the key assumptions and how to test them. • Explain the concept of independence in a contingency table. Learning objectives to review • Use a probability distribution to calculate expected values for a chi-square goodness-of-fit test. • Calculate expected values for a chi-square test of independence. • Define the goodness of fit of a linear model and explain how it is measured • Explain the meaning of interaction in two-factor ANOVA, both graphically and in words. • Explain how multiple testing causes alpha inflation, in terms of testwise and experimentwise Type I error rates. • Identify and apply a testing method to avoid alpha inflation in ANOVA post hoc tests. • Calculate R2 from an ANOVA table or linear regression output. Learning objectives to review • Explain the uses and disadvantages of nonparametric tests. • Describe key similarities and differences between linear correlation and linear regression. • Describe the circumstances that make a test robust to violations of assumptions. • Explain the link between the confidence interval of a parameter and hypothesis tests about the value of the parameter. • List the three essential parts of any confidence interval. • Calculate the degrees of freedom of a test statistic. • Choose the most appropriate analysis method for a study. • Calculate variance components and ICC for a random effects ANOVA. Learning objectives to review • List the differences between continuous probability distribution functions and discrete probability functions. • Use plots of probability density functions to estimate probabilities. • Recognize and explain decision-making mistakes that can cause alpha inflation. • List the key features of important probability distributions (F, 𝜒!, Normal, t, Poisson). 程序代写 CS代考 加微信: powcoder QQ: 1823890830 Email: powcoder@163.com