这是一份NYU纽约大学Coding in R for Data作业代写的成功案例
– Read carefully instructions for each problem. You should follow them exactly. – Packages: All problems should be completed using – Base packages. To learn what packages are loaded when $r$ session starts enter one of the following c(getoption (“defaultPackages”), “base”) or search() – ggplot2, ggpubr, readxl, writexl. – no other packages should be used to complete the exam. – Create a pdf file that will contain graphs, comments, and etc. for problems $2,3,4$, and 5 . Here are more detailed instructions: – You will need to indicate the problem number. Do not copy the problem. – Provide all required information (read questions carefully). – Insert the images/graphs. – Graphs should have titles, proper labels for the axes. – Add a file that contains your script. Here are more detailed instructions: – Set you working directory as the one that contains the data file(s) and refer to them by name. – In your script, indicate the problem and part numbers. For examples, $3 \mathrm{a}$. – Review: I would recommend that you review your Math $161 \mathrm{~A}$ class notes (or the textbook) on Chapters 7 (Confidence Intervals) and 8 (One-Sample Hypothesis Testing).
- (10 points) In Chapter 7 , slide 60 , we discussed sample size calculations when $\alpha, \beta$, and $\Delta=\mu_{0}-\mu^{\prime}$ are provided. Write a function that computes the sample size for the given quantities $\alpha, \beta$, and $\Delta=\mu_{0}-\mu^{\prime}$.
- (5 points) The desired percentage of $\mathrm{SiO}{2}$ in a certain type of aluminous cement is 5.5. To test whether the true average percentage differs from $5.5$ for a particular production facility, $n$ independently obtained specimens should be analyzed. Suppose that the percentage of $\mathrm{SiO}{2}$ in a specimen is normally distributed with $\sigma=0.3$. What value of $n$ is required to satisfy $\alpha=0.01$ and $\beta(5.6)=0.01$ ? Use the function from problem 1 to obtain the sample size. State the hypotheses. Include the output of the function used to obtain $n$.
- ( 30 points, 5 points each part) Automatic identification of the boundaries of significant structures within a medical image is an area of ongoing research. The paper “Automatic Segmentation of Medical Images Using Image Registration: Diagnostic and Simulation Applications” (J. of Medical Engr. and Tech., 2005: 53-63) discussed a new technique for such identification. A measure of the accuracy of the automatic region is the average linear displacement (ALD). The paper gave the following ALD observations for a sample of 49 kidneys (units of pixel dimensions).
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