Statistical Analysis of Corneal Thickness in Glaucoma Patients
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The document provides a statistical analysis of corneal thickness in glaucoma patients and describes methodologies including a paired sample t-test for comparing data. It also includes exercises on analyzing barometric pressure and boiling point relationships using linear regression techniques. The analysis seeks to ensure accurate statistical conclusions for academic purposes and includes specific statistical tests and graphical representations.
Statistical Analysis of Corneal Thickness in Glaucoma Patients
- 1. Visit: www.statisticsassignmenthelp.com Email: [email protected] Phone: +1 (315)-557-6473 Statistical Analysis of Corneal Thickness in Glaucoma Patients
- 2. At statisticsassignmenthelp.com we provide expert solutions to your statistical problems. In this sample assignment, we investigate whether glaucoma affects corneal thickness by comparing measurements in eyes affected by glaucoma to those not affected. Using a paired sample t-test, we analyze corneal thickness data from 8 individuals to test if there is a significant difference. This comprehensive solution demonstrates our approach to tackling complex statistical questions, ensuring you achieve accurate and reliable results for your academic needs. Statistical Analysis of Corneal Thickness in Glaucoma Patients
- 3. Exercise 1 To determine whether glaucoma affects the corneal thickness, measurements were made in 8 people affected by glaucoma in one eye but not in the other. The corneal thicknesses (in microns) were as follows: Assume the corneal thicknesses are normally distributed with mean μ1 and variance σ2 1 for eyes affected by glaucoma, and mean μ2 and variance σ 2 2 for eyes not affected by glaucoma. Test H0: μ1 = μ2 against H1: μ1 < μ2 using α = 0.1. What kind of test will you perform? Base your conclusion on a 90% confidence interval.
- 4. Solution Here is the script and the results:
- 6. Exercise 2 The following data give the barometric pressure x (in inches of mercury) and the boiling point y (in °F) of water in the Alps. a. Make a scatterplot with pressure x on the x‐axis and boiling point y on the y‐axis. Does the relationship appear to be approximately linear? b. Fit a least‐squares line. What are the coefficients of the line? What are their t‐statistics? c. Plot the least‐squares line in the same figure with the scatterplot.
- 7. d. What proportion of variation in the boiling point is explained by the linear regression model on the barometric pressure? e. Is the slope coefficient significantly different from zero? How do you know? If yes, at what significance level? f. What are SSR, SSE, SST, MSR, MSE and F? Solution Here is the script and the results:
- 9. Yes, the relationship appears approximately linear.