Homework 10

docx

School

Georgia Institute Of Technology *

*We aren’t endorsed by this school

Course

2400

Subject

Health Science

Date

Dec 6, 2023

Type

docx

Pages

5

Report

Uploaded by BrigadierHummingbirdPerson940

Name: Rebecca Fisher Collaborators 1 : Beth Patterson Basic information Topics covered: Logistic regression. Assigned: 11/14 Due: 11/21 Data notes: No external data is needed to answer the questions. Submission: Please submit via Canvas. Submit one word or pdf file, with each problem starting on a new page. Put all code, figures, and work in the word/pdf document. 1 Please list anyone you worked with on the homework. This uses the honor system. Remember that I encourage you to work together but that the work your turn in should be your own work.
Problem 1: Suzuki et al. (2006) measured sand grain size on 28 beaches in Japan and observed the presence or absence of the burrowing wolf spider Lycosa ishikariana on each beach. Sand grain size is a measurement variable, and spider presence or absence is a nominal variable. At what grain size, the odds of seeing a spider is 1 to 3. If the sand grain size is increased 0.05 mm, how will the odds of seeing a spider be affected, explain your work. a) Equations: Odds= P/(1-P) = 1/3 Ln[Y/(1-Y)]= β 0 + β 1 x 1 X 1 = (ln(P/1-P) - β 0 )/ β 1 Calculations: Logistic regression in excel (x-var= grain size, y-var= spiders(presence) X 1 = ln(1/3) +1.255/ 4.40927 .0355 Analysis: At a grain size of .0355mm the odds of seeing a side is 1/3 b) Equations: Estimated regression equation: P = e B 0 + B 1 x 1 1 + e B 0 + B 1 x 1
Calculations: Logistic regression in excel with all sizes+.05 mm (x-var= grain size +.05, y-var= spiders(presence) P= = grain ¿¿ ¿ 1.6476 + 5.1215 ¿ 1 + e ¿ e 1.6476 + 5.1215 ( grainsize ) ¿ grain size =.0355 .2109 Analysis: In the shifted model the odds of seeing a spider at grain size .035 decreased to about 1 to 5.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help
Problem 2: Insulin on Opossum Liver. Corkill (1932) provides data on the influence of insulin on opossum liver. In the experimental setup the 20 animals (common gray Australian opossums — Trichosurus) fasted for 24 or 36 hours. Ten animals, four from the 24-hour fasting group and six from the 36-hour fasting group, were injected with insulin, while the remaining ten animals served as controls, that is, they received no insulin. After 3 to 4 hours liver glycogen and blood sugar were measured. The weights of the animals were recorded as well. The goal of the study was to explore the deposition of liver glycogen after the insulin regimen in opossums. In rabbits and cats, for example, it was previously found that insulin induced significant glycogen storage. This study found a slight depletion of liver glycogen after the insulin treatment. Our goal is to model the liver glycogen based on weight, level of blood sugar, insulin indicator, and fasting regime. Is the insulin indicator (0 no, 1 yes) an important covariate in the model? Hypothesis: H 0 = the insulin indicator is not an important covariate to the liver glycogen level. H 1 = the insulin indicator is an important covariate to the liver glycogen level. Testing: Logistic Regression Test on excel (x-var= liver gly, y-var= insulin) Liver Glycogen= β0+β1×Weight β2×Blood Sugar+β3×Fasting Period+β4×Insulin Indicator+ϵ Analysis: The P-value for insulin indicator(.066415) is > .05, therefore we fail to reject the null hypothesis: showing insulin indicator is not an important covariate to the liver glycogen
The P-value for Fast Period(.001) < .05; indicating statistically significant relationship between fast period & liver glycogen. The r-squared value is .656, which shows a moderate-high level of correlation