辅导GNO44留学生、dataset arthritis讲解、Python，c++,java程序语言辅导 讲解Python程序|辅导R语言程序
- 首页 >> 其他 - Appropriate Methodology: Did you suggest appropriate method(s) for
addressing the question.
- Methodology Implementation: Did you correctly implement the indicated
methodology.
- Discussion for a non-statistical audience: Did you provide a coherent and
statistically literate response to the questions asked. Pretend you are explaining
your results to someone who knows next to nothing about statistics. I highly
encourage the use of relevant tables and plots while attempting this task.
Problem 1: In this problem we are interested in quantifying the effect of a new
anti-arthritic supplement (GNO44) on patients suffering from arthritis. Download
the dataset arthritis.csv. For this dataset 18 subjects were selected (10 male, 8
female) and each have received the supplement. Each was asked to rank their
arthritic pain on a scale of 0-10 (0 being painless, 10 being extreme) at the start of
the trial, as well as at 3 months, 6 months, 9 months and 12 months into the trial.
a.) Is there evidence that GNO44 is helpful in reducing perceived pain amongst
individuals suffering from arthritis.
b.) If you were to try and improve this study, what steps might you take with
regards to design of the experiment? Why?
Problem 2: Download the aids.csv dataset. There are three variables:
- Time (time from start of the observational period to death or censorship)
- death(1 = death, 0 = censorship)
- drug(drug prescribed for condition, ddC = zalcitabine, ddI = didanosine)
- CD4 (CD4 white blood cells count at initial observation)
I am interested in reporting on the effects of zalcitabine vs. didanosine in terms of
prolonging life for a patient diagnosed with AIDS. Using the data, interpret the
relative benefit of one drug over the other.
Problem 3: Hodgkin's Disease is a particular form of Lymphoma (which is an
immune system cancer that begins with the white blood cells of a host). The cancer
is identified in four stages, I, II, III and IV, with IV being the most progressed stage.
Additional risk factors associated with death post diagnosis include age, gender
(M/F), whether or not a person has had mono OR has tested positive for HIV (Y/N),
red blood cell count (in cells per microliter - cells/ul) and white blood cell counts -
cells per millimeter cubed). Provided in the Hodgkins.csv file is data consisting of
343 test subjects who have been diagnosed with the disease. The response variable
of interest is whether or not a subject survived five years past diagnosis (Y/N) a.) Assess the impact of having previously had mono/HIV as a risk factor for death
prior to five years of a Hodgkin's diagnosis.
b.) How much more at risk is an individual diagnosed with stage IV Hodgkins as
opposed to someone diagnosed with stage I Hodgkins (in the context of the other
potential confounding variables)?
c.) A 33-year old male with a red blood cell count of 4.3 cells/ul, a while blood cell
count of 12,000 cells/mm3, no history of mono and no indication of HIV has been
diagnosed with stage II Hodgkins. Based on the data available to you, help this
person understand the probability of being alive five years from now.
Problem 4: Download the weight.csv dataset. You will find a single numerical
value in this dataset containing the weight (in lbs) or 472 randomly selected College
students.
a.) Create a box-plot for the sampled data.
b.) Create 95% confidence interval for the box-plot you created in part a (Don't
worry about the outliers, just the box and whiskers are all I'm really interested in).
If you're not entirely sure how a box-plot works, I suggest looking it up on the
internet.
Problem 5: Provided is time to event data for 44 patients who were diagnosed with
Leukemia. Download the Leukemia.csv dataset, you will find the following variables:
- Group: Whether or not a person received a new medical treatment or traditional
medical treatment for Leukemia: (A (treatment), B (Control))
- Result: Whether or not the person died or not (Death, Alive)
- Time: Time of death or last time living person was last seen (in weeks)
- logWBC: natural log of the white blood cell counts of individuals at time of initial
diagnosis (log(cells/mm3))
Is there a benefit to being placed in the treatment group as opposed to the control
group?
addressing the question.
- Methodology Implementation: Did you correctly implement the indicated
methodology.
- Discussion for a non-statistical audience: Did you provide a coherent and
statistically literate response to the questions asked. Pretend you are explaining
your results to someone who knows next to nothing about statistics. I highly
encourage the use of relevant tables and plots while attempting this task.
Problem 1: In this problem we are interested in quantifying the effect of a new
anti-arthritic supplement (GNO44) on patients suffering from arthritis. Download
the dataset arthritis.csv. For this dataset 18 subjects were selected (10 male, 8
female) and each have received the supplement. Each was asked to rank their
arthritic pain on a scale of 0-10 (0 being painless, 10 being extreme) at the start of
the trial, as well as at 3 months, 6 months, 9 months and 12 months into the trial.
a.) Is there evidence that GNO44 is helpful in reducing perceived pain amongst
individuals suffering from arthritis.
b.) If you were to try and improve this study, what steps might you take with
regards to design of the experiment? Why?
Problem 2: Download the aids.csv dataset. There are three variables:
- Time (time from start of the observational period to death or censorship)
- death(1 = death, 0 = censorship)
- drug(drug prescribed for condition, ddC = zalcitabine, ddI = didanosine)
- CD4 (CD4 white blood cells count at initial observation)
I am interested in reporting on the effects of zalcitabine vs. didanosine in terms of
prolonging life for a patient diagnosed with AIDS. Using the data, interpret the
relative benefit of one drug over the other.
Problem 3: Hodgkin's Disease is a particular form of Lymphoma (which is an
immune system cancer that begins with the white blood cells of a host). The cancer
is identified in four stages, I, II, III and IV, with IV being the most progressed stage.
Additional risk factors associated with death post diagnosis include age, gender
(M/F), whether or not a person has had mono OR has tested positive for HIV (Y/N),
red blood cell count (in cells per microliter - cells/ul) and white blood cell counts -
cells per millimeter cubed). Provided in the Hodgkins.csv file is data consisting of
343 test subjects who have been diagnosed with the disease. The response variable
of interest is whether or not a subject survived five years past diagnosis (Y/N) a.) Assess the impact of having previously had mono/HIV as a risk factor for death
prior to five years of a Hodgkin's diagnosis.
b.) How much more at risk is an individual diagnosed with stage IV Hodgkins as
opposed to someone diagnosed with stage I Hodgkins (in the context of the other
potential confounding variables)?
c.) A 33-year old male with a red blood cell count of 4.3 cells/ul, a while blood cell
count of 12,000 cells/mm3, no history of mono and no indication of HIV has been
diagnosed with stage II Hodgkins. Based on the data available to you, help this
person understand the probability of being alive five years from now.
Problem 4: Download the weight.csv dataset. You will find a single numerical
value in this dataset containing the weight (in lbs) or 472 randomly selected College
students.
a.) Create a box-plot for the sampled data.
b.) Create 95% confidence interval for the box-plot you created in part a (Don't
worry about the outliers, just the box and whiskers are all I'm really interested in).
If you're not entirely sure how a box-plot works, I suggest looking it up on the
internet.
Problem 5: Provided is time to event data for 44 patients who were diagnosed with
Leukemia. Download the Leukemia.csv dataset, you will find the following variables:
- Group: Whether or not a person received a new medical treatment or traditional
medical treatment for Leukemia: (A (treatment), B (Control))
- Result: Whether or not the person died or not (Death, Alive)
- Time: Time of death or last time living person was last seen (in weeks)
- logWBC: natural log of the white blood cell counts of individuals at time of initial
diagnosis (log(cells/mm3))
Is there a benefit to being placed in the treatment group as opposed to the control
group?