Helpful terms (not from book)

Stat Glossary




#**Chapter 1**

#**Chapter 2**

#**2.1 Introduction**

#**2.2 Basic Statistical Concepts**

Graphs that show variability (page 25)

dot diagram - small sets, shows tendency and spread; illustrate the major features of the distribution of the data in a convenient form, can also help detect any unusual observations (outliers), or any gaps in the data set.

histogram - larger sets, tendency, spread, distribution

box plot (box and whisker)

#**2.3 Sampling and Sampling Distributions**

#**2.4 Inferences about the Differences in Means, Randomized Designs**

(most tests in this section)

Comparing the means of two samples/populations

σ1 = σ2, variances equal and known (page 45)

σ1 ≠ σ2, variances not equal but known

Comparing a single mean to a specified value (page 46)

#**2.5 Inferences about the Differences in Means, Paired Comparison Designs**

Difference between means

paired t-test (page 50) - tests if there is a difference in means between 2 treatments, confidence interval on page 52

#**2.6 Inferences About the Variances of Normal Distributions**

(page 53) is for testing whether the variance of a normal population equals some value

F0 (page 53) is for testing whether the variances of two normal populations are equal

#**Chapter 3**

#**3.2 The Analysis of Variance**

Linear statistical models (page 64)

a = number of treatments/levels (rows)
n = number of replications of a treatment (columns)
N = a*n

#**3.3 Analysis of the Fixed Effects Model**

yi. - sum of observations of ith level (add row)
ӯi. - average of observations of ith level (add row and divide by n)
y.. - total of all observations (add all rows and columns)
ӯ.. - average of all observations (add all rows and columns and divide by an=N)

#**3.4 Model Adequacy Checking**

Tests for equality between 2 sets of means

  1. Tuckey (page 93), compares between all means, uses ANOVA
  2. Fisher LSD (page 94), compares between all means, uses ANOVA
  3. Dunnet (page 96), compares means to control, uses ANOVA

#**`Which test do I use?`**

1 set of data

One sample Z-test

One sample t-test

for chapter 2

2 sets of data

Two sample Z-test

Two sample t-test

Modified t-test

Differences in means

F0 for chapter 2

3+ sets of data (ANOVA)

Fo for chapter 3

    TO GET P VALUE
      * must already have Fo
      * find bounds on Fo in Fα table holding (a-1) and (N-a) constant

Bartlett’s test

Tukey’s test

Fisher LSD

Dunnett test

Helpful terms (not from book)

Stat Glossary

randomized complete block design is a design in which the subjects are matched according to a variable which the experimenter wishes to control. The subjects are put into groups (blocks) of the same size as the number of treatments. The members of each block are then randomly assigned to different treatment groups.

Example
A researcher is carrying out a study of the effectiveness of four different skin creams for the treatment of a certain skin disease. He has eighty subjects and plans to divide them into 4 treatment groups of twenty subjects each. Using a randomised blocks design, the subjects are assessed and put in blocks of four according to how severe their skin condition is; the four most severe cases are the first block, the next four most severe cases are the second block, and so on to the twentieth block. The four members of each block are then randomly assigned, one to each of the four treatment groups.

#**Chapter 4**

#**4.1 The Randomized Complete Block Design**

nuisance factor (page 121) - a design factor that has some effect on the response but we’re not interested in this effect. Can be unknown and uncontrollable, known and uncontrollable, or known and controllable

blocking (page 121) - used to eliminate the effect of known an controllable nuisance factors in comparisons among treatments

randomized complete block design (RCBD) (page 122) - an experimental design in which each block contains all the treatments;

Ho: μ1 = μ2 = μ3
H1: at least one μi ≠ μj

To compare treatment means (page 128) use any of the Ch. 3 methods but:

Randomization (page 121) - design technique

#**4.2 The Latin Square Design**

#**4.3 The Graeco-Latin Square Design**

#**4.4 Balanced Incomplete Block Designs**