Cheatography

# BioStat3425 Cheat Sheet (DRAFT) by Hsuzc

One-Way ANova Two-Way ANova Simple Linear regression Proportions

This is a draft cheat sheet. It is a work in progress and is not finished yet.

### Comparison Procedures

 LSD Requires knowing the global F test is signif­icant before any testing should be done. More powerful than Bonfer­roni, but can lead to inflated Type I error rate. Not recomm­ended TUKEY Controls experi­men­twise Type I error rate exactly if sample sizes between groups are equal More powerful than Bonfer­rroni Use when only interested in all (or most) pairwise compar­isons of means(­unp­lanned compar­isons) BONFERRONI Very conser­vative Use when only interested in a small number of planned compar­isons Use when making compar­isons other than between pairs.

### Individual Vs Family CI

 **Indi­vidual confidence level (compa­ris­onw­ise­,πΌππ€): Success rate of a procedure for constr­ucting a single confidence interval (or conducting a single hypothesis test) Familywise confidence level (exper­ime­ntw­ise­,πΌππ€):Success rate of a procedure for constr­ucting a family of confidence intervals (or a family of hypothesis tests) A βsucce­ssfulβ usage is one in which ALL intervals in the family capture their parameters

### Pairwise Example

 Nematodes in tomato plants How do nematodes affect plant growth? 12 identical pots, Different# of nematodes per pot; 0, 1000, 5000, Measure increase in tomato seedling height (cm) 16 days after planting Hypothesis Test: Ho:u1=­u2=u3 vs Ha: Not all of the u are equal Overall F test signif­icant (p-value = 0.0027) --> not all of the group means are equal Hypo for pairwise compar­isons H0: ui-uj=0 Ha: ui-ujβ 0 β iβ j How many pairwise compar­isons are there for this example? How many pairwise compar­isons are there for this example? K=K=I(­I-1)2β 3(3-1)2= 3 Levels not connected by the same letter are signif­icantly different Ordered Differ­ences Report for lower CL and upper Cl= difference of growth Which nematode groups have signif­icantly different ang growth? 0-5000 (p -value = 0.0045)and 5000-1000 (0.006) are signif­icantly different Difference in growth between 0 and 5000 will be between 1.83 cm and 8.27 cm more than 5000

### Factor Effects Model

 ππππ = π+πΌπ +π½π +(πΌπ½)π­π+ππππ Response = overall avg growth + main effects +inter­action effect Assump­tions: ππππ~N­(0,π2) are indepe­ndent Errors are normal, indepe­ndent, with constant variance Constr­aints: ΟπΌπ =Οπ½π =Ο(πΌπ½)ππ =0 Required to keep model from being over-p­ara­met­erized # of test = factor­alevels X factor­blevels

### Assump­tions Errors are

 Normal Normal Probab­ility Plot Constant Variance Predicted Residual vs Fitted and Residual vs Factor Levels Indepe­ndence Usually Assumed and typically assumed

### Main Effects and Intera­ction Plot

 Main Effects Plot Separate plot for each factor Plot of mean response for each level of the factor Gives an indication of whether a factor is important Horizontal line indicates means are the same for both levels of the factor and thus factor is not important Intera­ction Plot Plot means for each treatment combin­ation against levels of one factor, with different lines for the other factor Parallel Lines β No Intera­ction Crossing Lines - Intera­ction

### Simple Liner Regression Vocal

 Least Squares Regression standard approach for estimating the lineThe line is chosen, so that the sum of squared vertical distances between points and the line is minimized. correl­ation coeffi­cient r A measure of associ­ation for two quanti­tative variables is the predicted or fitted values. The points that lie on the regression line vertically above (or below) an observed value Notation ^yi residuals. (vertical) distances between observed and predicted values notatio: ei=yi-^yi

### Assump­rions

 Linear relati­onship between X and Y Model assumes that the error terms are Indepe­ndent, Normal, Have constant variance. Residuals may be used to explore the legitimacy of these assump­tions.