Biometry, Stat 412
 

 

Second Midterm Study Guide

 Text Box: You do not need to memorize any formulas pertaining to these topics, they will be provided if needed (see below). However, you do need to be able to use and interpret all formulas and obtain critical values of X2, z, and t from the tables in Zar, which will also be provided if needed. Also  be able to interpret MINITAB output on all topics (use handouts and homework as guides of what to expect).

 

 

I. Major focus of exam

Graphing

Normal distribution

Z-distribution

Central limit theorem

T-test

Confidence intervals

ANOVA & assumptions

II. Unifying Concepts

  1. What are they basic elements of a graph and why are they important? Given a graph be able to interpret the information presented.

  2. What is a normal distribution? What type of traits exhibit a normal distribution? Know the possible departures from normality (truncated, skewness, kurtosis). How can non-normal data be transformed to normal data?

  3. What is the z-distribution and why is it important? Be able to predict proportions of a population given specific values. What can the z-distribution tell us about the distribution of samples? Be able to predict the probability of obtaining samples of a given sample size and value. Know the difference between s and  and when to use each.

  4. Why is the central limit theorem (CLT) important? What does the CLT tell us about: the distribution of sample means from a normal population? from a non-normal population? the relationship between statistics and parameters?

  5. What type of data are required for a t-test? How does the t-distribution differ from the z-distribution? What is the goal of the t-test? What is the difference between one-tailed and two-tailed tests? What is the difference between one-sample and two-sample tests?

  6. What are confidence intervals? What three factors influence their width? How can a study obtain narrow confidence intervals? What does the extent of overlap between confidence intervals tell you?

  7. What is the rationale behind the analysis of variance? What does the F-ratio measure?

  8. What are the assumptions of the analysis of variance? What is the equality of variance test and why is it used? What effect would violations of the assumptions have on the F-ratio?

 

III. Important formulas: (do not memorize but know how to use)

1) Proportions of the normal curve

 

      

2) Probability  of samples from normal population

 

 

3) One-sample T-test

 

 

4) Two-sample T-test

 

 

5) Confidence Interval