Heat resistance in rice, continued. Excercise 21.2 investigated a genetic model oh heat resistance in rice plants. Software gives a P-value of 0.397. Interpret this result. Can you conclude that the distribution of heat-resistant phenotypes in F2 follows a dominant-recessive model (75%~ 25%)?
Heat resistance in rice, continued. Excercise 21.2 investigated a genetic model oh heat resistance in rice plants. Software gives a P-value of 0.397. Interpret this result. Can you conclude that the distribution of heat-resistant phenotypes in F2 follows a dominant-recessive model (75%~ 25%)?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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21.6 Heat resistance in rice, continued. Excercise 21.2 investigated a genetic model oh heat resistance in rice plants. Software gives a P-value of 0.397. Interpret this result. Can you conclude that the distribution of heat-resistant phenotypes in F2 follows a dominant-recessive model (75%~ 25%)?

Transcribed Image Text:EXAMPLE 21.2
Righting behavior of aphids
Pea aphids (Acyrthosiphon pisum) are small, wingless, sap-sucking insects that live
on plants. They evade predators (such as ladybugs) by dropping off. A study exam-
ined the mechanism of aphid drops. Researchers hung live aphids upside down
from delicate tweezers and then released them. The videotaped drops showed that
19 of the 20 aphids landed on their legs.? Is this evidence that live aphids land
right side up (on their legs) more often than chance alone would predict?

Transcribed Image Text:side. Therefore, the relevant null hypothesis is
Te this scenario, aphids can land either on their legs or on their back. If chance
vas at work, we would expect aphids to be equally likely to land on either
1
and Pback =
1
Ho: Plegs =
The alternative hypothesis is simply that Ho is not true.
The goodness-of-fit test can assess a variety of null hypotheses that reflect
how we expect one categorical variable to be distributed in the target popula-
tion. Because the variable is categorical, the parameters in Ho are population
proportions for the k possible outcomes (levels) making up the variable. The only
constraint on the null hypothesis for a goodness-of-fit test is that these k propor-
tions sum to 1, so that Ho accounts for all possible outcomes. In Example 21.1
the variable seed color had three possible outcomes (black, brown, pale) and the
three population proportions in Ho were 12/16, 3/16, and 1/16, which sum to 1.
In Example 21.2 the variable landing side had only two possible outcomes (legs,
back) and the two population proportions in Ho were 1/2 and 1/2.
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