The maple is a common tree found across the northern hemisphere recognized for their vibrant slide colors. Maples are also recognized for their interesting seed dispersal methods. These seed products, or samaras, are often named helicopters or whirlybirds due to their distinct two-winged appearance. With two papery wings, the samaras can spin for the ground whilst being found by the blowing wind, allowing them to travel and leisure farther through the parent woods. As a group of botanists, were interested in studying the design of maple seeds plus the way in which they will fly. Samaras typically develop sets of two wings per seeds, with a number of natural growth factors influencing the rate by which they spin, including the pounds of the seed and wings. By learning this style, we are looking for which variables allow the seed products to travel farthest, making them more optimal intended for populating an area.

Try things out Explanation/ How Data Was Collected

To get our test we decided to test these types of three elements: number of paperclips, number of staples, and paper type. Our factors diverse between notebook or building paper, you, 2, or 3 newspaper clips on the body of the heli, and 0, 1, or perhaps 2 staples on the unit helicopter wings. The number of paperclips were chosen to represent differing body weight load. The number of staples were decided to see if different wing weights would impact the flight length. The different types of paper the helicopter was constructed out of utilized to represent if an overall distinct weight might affect the trip time. Whenever we created our helicopters, the wings had been half of the document, and the body system was minimize halfway down into the paper. The wings were not collapsed. This was held consistent with all the helicopters. Our group figures the micro helicopters in order to keep these people organized although collecting info, and had precisely the same person drop the helicopter from the top of a staircase in Violette Hall for each trial. This helped to keep info consistent. We randomized the order that the micro helicopters were flown. We started out keeping period from if the helicopter was dropped to the moment this hit the ground for each trial. The aspect variations had been run five times in a random flight buy.

It was a two by a few Factor Examination experiment. A confounding adjustable could have been the trial amount. As every airplane was flown, wear and tear could have happened each time this hit the floor, affecting later on trials. Our team tried to control for several third variables as possible. For example, we had one person drop the plane each time, one person made the measurements for any planes, one person folded issues the plane into formation, and we had one person putting your paperclips and staples in the same position.

Unit Statement

I d t e r a c big t i u n t Error

yijkl = M +? l + ÃŸk +? d +? ÃŸjk +? jl + ÃŸ? kl &? ÃŸ? jkl + eijkl

Y= Air travel time,? = Paperclips, ÃŸ = Favorites,? = Newspaper type

we = one particular to 5, l = 1 to 3, t = zero to two, l = 1 to 2

T? = SÃŸ = H? = T? ÃŸ = S? = SÃŸC = S? ÃŸ? = zero e~Nind(0, s2)

M = Center

Examination

After entering our data, we happened to run the tests for equal variance (see Figure you in Appendix) and normality. For the equal difference test, the null hypothesis was that almost all variances were equal amongst groups, plus the alternate hypothesis was right now there there was not equal difference. We utilized the being rejected region of your p-value below 0. 05, and since each of our p-value was 0. 206, we could certainly not reject the null speculation, and it absolutely was safe to assume the same variance. To get our normality test, the null speculation was that the errors were normal (the alternate hypothesis being that these were not). Seeing that our p-value was more than the rejection region of 0. 05 at a value of 0. 422, and AD value of zero. 368, it had been safe to assume normality as well.

Following the preliminary tests, all of us ran the ANOVA (see Figure 2 in Appendix). We hand picked the General Linear Model simply because there were multiple factors and interactions to take into consideration. Upon obtaining our output, we initial looked to verify that there was a three-way connection effect. Our null hypothesis was that there was no result, and the alternate speculation was that there were. With the being rejected region of your p-value less than 0. 05, and with our p-value becoming 0. 019, we rejected the null hypothesis and concluded that there is a three-way interaction impact.

As there was a three-way conversation, we could then stop with the testing. Nevertheless , if one particular were to continue, they would discover there was an important paper clip*staple interaction (p-value of 0. 0001) and a newspaper clip*paper type interaction (p-value of zero. 015). They are considered two-way interactions and could be used if perhaps there was not a three-way interaction impact.

Our results likewise indicated that there was an important paperclip effect (p-value of 0. 0001) and software program effect (p-value of zero. 0001). They are considered primary effects and would just be used if there were zero found conversation effects.

We simply included the three-way conversation plot (see Figure a few in Appendix) since that held choice over the additional interaction results and main effects. While confirmed in the plot by nonparallel lines, there is a three-way interaction happening in our info. One aspect (number of paperclips, range of staples, newspaper type) will not necessarily create the highest or lowest outcome over any and all situations. Nevertheless , according to the data, it appears that the longest airline flight time, the average mean of 4. 51 seconds, was achieved by the helicopter with 1 paper cut, 0 worn, and made of notebook daily news. The quickest flight time, an average mean of 2. nineteen seconds, was achieved by the helicopter with 3 paper fasteners, 1 staple in every wing, and made of notebook computer paper. Generally, according to the conversation plot, it appears that 1 paperclip, or a lower body weight, triggered the fastest flight time, while 3 paperclips ended in the slowest. Zero favorites resulted in the fastest air travel for you and two paperclips, or maybe a lesser wing weight, nevertheless did not change much for 3 paperclips. The laptop paper was fastest for 1 and 2 paperclips, or a reduced overall excess weight, but slowest for three or more paperclips. The paper type did not include much impact on the staples.

We also used Tukey’s pairwise approach as a post-hoc test to compare the statistical significance of the means (see Number 4 in Appendix). The helicopter with the greatest flight time (a imply of four. 51 seconds) had statistical significance with three various other helicopters (with means of some. 44, a few. 88, and 3. forty eight, in order of longer to shorter flight times). The helicopter with the least flight period (a indicate of 2. 19) had record significance with 12 different helicopters (with means of 2 . 30, 2 . 398, installment payments on your 42, installment payments on your 66, 2 . 70, 2 . 76, 2 . 79, 2 . 83, installment payments on your 96, installment payments on your 99, three or more. 00, and 3. 23, in order of shorter to longer flight times).

We were able to conclude several things from our try things out. We assumed there was equivalent variance for any factors whenever we tested that because we got a larger p-value than. 05. Once looking at the ANOVA table, we learned that there was a three-way interaction effect, because the p-value was less space-consuming than. 05. Now, testing could be finished. Looking at figure a few in the Appendix, it is evident that the lines are intersecting and non-parallel, further resistant that we could conclude a three-way conversation. However , all of us went on to verify that there were virtually any significant associated with the elements. We located that there was two two-way interactions: there was clearly a significant paperclip by software program interaction as well as a paperclip by simply paper type interaction. As stated before, we would need this information if we weren’t able to find a three-way discussion. There was zero evidence that a person factor only affected the bird flight period. The greatest flight period average was a result of a helicopter with 1 paper clip, zero staples, to make of notebook computer paper. The shortest airline flight time common resulted via a helicopter made with several paper videos, 1 staple in each wing, and notebook newspaper. However , taking a look at the conversation plot, an over-all conclusion may be drawn that lesser weight on parts of the heli-copter results in a longer flight time. We advise for future replications of the experiment to become consistent with almost all factors in the helicopter: the wing length, the worn and paperclips being in the same positions, and to make certain the folds up are all folded away at the same areas. If one were to transform our test, they can include development paper too, as a heavy paper choice. Because the trials can result to damage to the helicopters and thus could possibly impact the results, the next group might want to make the same helicopter for the five different tests so that the effect of the damage can be kept little.

Appendix:

Figure you: Bartlett’s Test for equivalent variance:

Physique 2: Normality test employing residuals.

General Thready Model: air travel time vs paperclips, favorites, paper type

Factor Details

Factor Type Levels Beliefs

paperclips Fixed 3 -1, 0, 1

staples Set 3 -1, 0, 1

paper type Fixed 2 -1, 1

Analysis of Variance

Supply DF Adj SS Adj MS F-Value P-Value

paperclips a couple of 20. 5909 10. 2954 42. sixty six 0. 500

favorites 2 6th. 4886 several. 2443 13. 44 0. 000

paper type 1 0. 0102 zero. 0102 0. 04 zero. 837

paperclips*staples four 5. 8316 1 . 4579 6. 04 0. 000

paperclips*paper type a couple of 2 . 1644 1 . 0822 4. forty-eight 0. 015

staples*paper type two 0. 5064 0. 2532 1 . 05 0. 356

paperclips*staples*paper type some 3. 0563 0. 7641 3. 17 0. 019

Error seventy two 17. 3780 0. 2414

Total fifth 89 56. 0265

Model Overview

S i9000 R-sq R-sq(adj) R-sq(pred)

0. 491285 68. 98% 61. 66% 51. 54%

Number 3: ANOVA Output

Number 4: Interaction Plot intended for Paperclip*Staple*Paper Type for flight time.

Comparisons pertaining to flight period

Tukey Pairwise Comparisons: Response = airline flight time, Term = paperclips*staples*paper type

Collection Information Making use of the Tukey Approach and 95% Confidence

paperclips*staples*paper

type And Mean Grouping

-1 -1 -1 five 4. 512 A

-1 -1 1 5 four. 442 A B

-1 0 -1 5 several. 882 A B C

0 -1 -1 5 3. 480 A B C D

0 0 1 your five 3. 376 B C D Electronic

-1 0 1 5 3. 312 C Deb E Farrenheit

-1 1 -1 five 3. 004 C D E Farreneheit

-1 1 1 5 2 . 990 C D E F

0 you -1 5 2 . 962 C M E F

1 1 1 a few 2 . 828 C M E Farrenheit

0 -1 1 a few 2 . 792 C G E N

1 -1 1 your five 2 . 762 C M E Farreneheit

1 zero 1 five 2 . 704 D At the F

0 0 -1 5 2 . 656 M E Farrenheit

1 -1 -1 a few 2 . 424 D Elizabeth F

zero 1 you 5 installment payments on your 398 G E N

1 1 -1 5 2 . 302 E N

1 0 -1 your five 2 . 190 F

Ensures that do not talk about a notice are drastically different.

* TAKE NOTE * Are unable to draw the interval plan for the Tukey treatment. Interval and building plots for

comparisons are illegible exceeding 45 intervals.

Number 5: Tukey’s Pairwise Evaluations (Post Hoc Test)