Homework # 3 (on Design) due by Thursday October 2 at 5pm

 

Problems students are to do -
    - students registered for BIOL/STAT 336 (UG) – do problems 2, 3 and 4
    - students registered for STAT 435 (G) – do problems 1 - 5

 

Please type up your answers or write very neatly - no need to attach a copy of the output.  Use the attached Outputs.  Summarize all results comparing treatments using the under-line method, and interpret these results. 

 

1. As part of a pollution study, lead levels at five monitoring sites in Chicago were measured.  Grass specimens from three areas within each monitoring site were collected and their lead levels, in mg/g dry weight, measured in duplicate as presented below.  Does lead pollution differ with monitoring site and areas within monitoring site? Please report all necessary assumptions, (exact) test statistics and p-values, and results of relevant diagnostics, along with a thorough analysis of the data.  Be sure to identify the type of design.  See this file.

 

NB - if we find that Sites are significant, our next step would be to perform a MCP/MSP to determine which sites differ - this is done here by averaging the sub-samples, doing the one-way analysis, and performing the SNK/MCP.

Site

A

A

A

B

B

B

C

C

C

D

D

D

E

E

E

Area

1

2

3

1

2

3

1

2

3

1

2

3

1

2

3

 

40.0

37.5

22.5

31.0

42.5

32.5

27.5

11.0

15.0

25.0

11.0

14.0

12.5

9.0

10.0

 

40.0

37.5

20.0

37.5

43.0

36.5

30.0

10.0

14.0

25.0

11.0

15.0

10.0

8.0

9.0

 

2. The female flower (A) and the seed (B) of two African plants are widely used in traditional medicine for the treatment of people with certain diseases.  It has been claimed, however, that use of these traditional remedies in combination can temporarily impair vision by reducing the patient's field of vision.  The measurements provided in the following table were collected from 18 subjects randomly assigned in groups of two to different combinations of flower and seed concentrations.  How do the plants appear to affect field of vision? Again, report all necessary assumptions, (exact) test statistics and p-values, the results of relevant diagnostics, along with a thorough analysis of the data, and be sure to identify the type of design and the appropriate analysis in your analysis. See this file.

 

                    Flower (A)        Seed (B)            Field of Vision
                        0.25 g            0.001 g                67     66
                                              0.005 g                65     61
                                              0.009 g                62     64
                        2.5 g              0.001 g                68     65
                                              0.005 g                68     61
                                              0.009 g                55     53
                        5.0 g              0.001 g                65     64
                                              0.005 g                62     63
                                              0.009 g                49     47

 

3. A horticulturalist studied the germination of tomato seed with four different temperatures (25C, 30C, 35C and 40C) such that each run of the experiment included only two different temperatures because there were only two growth chambers available for the study.  The two experimental temperatures were randomly assigned to the chambers for each run, but the researcher feels certain there may be run-to-run variability.  The data that follow are germination rates of the tomato seed.  Be sure to thoroughly identify the design (including all parameters and verify any claims concerning the design) and thoroughly analyze the data (after giving necessary assumptions) including subsequent analyses.  What is the commonly used term for "runs" in this experiment? See this file.

 

Run        25C          30C            35C             40C
  1         24.65           --                --             18.62
  2            --           24.11             --             17.08
  3         22.31        21.25             --                --
  4            --              --             17.95          18.93
  5         28.90           --             18.27             --
  6            --           25.53          20.91             --

 

4.  Associative effects occur in animal diets when feedstuffs are combined and diet utilization or animal performance is different from that predicted from a sum of the individual ingredients. The addition of roughage to the diets of ruminant animals had been shown to influence various diet utilization factors such as ruminal retention time.  However, information about the relative associative effects of different roughage was scarce, especially in mixed feedlot diets.  An animal scientist hypothesized roughage source could influence utilization of mixed diets of beef steers by altering ruminal digestion of other diet ingredients.  The basic mixed diet for the beef steers was a 65% concentrate based on steam flaked milo and 35% roughage.  Three roughage treatments were used with

(A) 35% alfalfa hay as a control treatment

(B) 17.5% wheat straw and 17.5% alfalfa and

(C) 17.5% cottonseed hulls and 17.5% alfalfa.
Twelve beef steers were available for the study.  Each of the three roughage diets was fed to

the steers in one of six possible sequences of the three diets
                  (seq 1: A -> B -> C,
                   seq 2: B -> C -> A,
                   seq 3: C -> A  -> B,
                   seq 4: A -> C -> B,
                   seq 5: B -> A -> C,
                   seq 6: C -> B -> A ).
Each diet in each sequence was fed to two steers for 30 days.  The steers were allowed a period of 21 days to adapt to a diet change before any data were collected.  The Neutral Detergent Fiber (NDF) digestion coefficient, which indicates the percent of dietary fiber digested by the steer, was calculated for each steer on each diet. The data are given below, with each row corresponding to a different steer and the variables are sequence, treatment, NDF, treatment, NDF, treatment and NDF for each steer in each of the 3 periods. Thoroughly analyze the data and report all assumptions, findings and results of diagnostics.  As always, report exact test statistics and p-values. See this file.

 

abc   a 50 b 61 c 53
abc   a 55 b 63 c 57
bca   b 44 c 42 a 57
bca   b 51 c 46 a 59
cab   c 35 a 55 b 47
cab   c 41 a 56 b 50
acb   a 54 c 48 b 51
acb   a 58 c 51 b 54
bac   b 50 a 57 c 51
bac   b 55 a 57 c 55
cba   c 41 b 56 a 58
cba   c 46 b 58 a 61

 

5.  Discuss the uses of blocking in biostatistical studies - Why is it used and useful? Why is it not used more?  What are its drawbacks?  In what sense (if any) is blocking used in CODs and split-plot designs? How is the analysis of a RCBD with one blocking and one treatment factor different from a two-way ANOVA with two treatment factors?