Category: Statistics

This is part two to this assignment. Due on 3/19/24 Can you help?
Secondary anal

This is part two to this assignment. Due on 3/19/24 Can you help?
Secondary analysis of existing data collected by other researchers,
for other purposes, offers researchers the potential to answer research
questions without having to go through the process of collecting the
data themselves. Based on your Unit III Assignment, address the prompts
below.
Identify a specific academic, governmental, or commercial source of
quantitative secondary data that could be used to solve the problem you
stated in Part 2 of the Unit III assignment. Provide reference
information for this source.
Describe how you will obtain access to the raw data.
Explain why the data are suitable for addressing your research problem.
List the limitations of using the data.
This journal should be at least two pages in length, not counting the
required references page. Please thoroughly address all areas listed
above, and include at least two credible sources. An abstract is not
required. Please use APA compliant headings and sub-headings that align
with the individual assignment requirements. Adhere to APA Style,
including in-text citations and references for all sources that are
used.

Descriiptive Statistics
Let’s begin by testing your knowledge of descriiptive st

Descriiptive Statistics
Let’s begin by testing your knowledge of descriiptive statistics. Below is an example of a discrete random variable, i.e., family size. The attributes (values) of the variables are the number of persons in the family.
Family Size Values
1
2
3
4
5
6
7
8 or more
You draw a sample of 50 families from Alexandria, LA, and observe family size. What are the attributes (possible values) of family size?
When you collect data on your variables, you want to find ways to present your findings. Descriiptive, or summary, statistics help you present a snapshot of how the values of the variable are distributed in your sample. This is different from the use of inferential statistics in which you express your degree of confidence in how well the data you collected using a sample represents the whole population from which the sample was taken.
For now, let’s review some of the ways you can describe your data.
You ask family 1 how many members are in the family, so you designate the value for this family (x1), which is the first data point in the data set, or value of 2.
You proceed to ask family 2 (x2); for the second family, x2 = 9. You continue to family 50 = x50.
What can you tell by looking at the raw data alone? Actually, very little since it is hard to detect any patterns in a list of 50 raw data entries.
You would list the possible values of the variable “family size” in the first column. In the second column, you would list the number of families of that size. In the third column, you would report the percentage of families of that size. A percentage is calculated by dividing the number of families by the sample size of 50 and multiplying that result by 100%.
Here are the frequency and percentage distributions for the data on family size. Note the construction of the table so that all the information is clear to your audience. The numbers in your frequency column should add up to the sample size. The percentages in your percentage column should add up to 100.
Family Size
Frequency
Percentage
1
5
10.0
2
21
42.0
3
12
24.0
4
4
8.0
5
3
6.0
6
2
4.0
7
1
2.0
8+
2
4.0
Total
50
100.0
If you notice, you can now get a much better sense of how family size varies in the sample and which are the most prominent values. Most families in the sample have 2 or 3 members.
Summary Measures With Single Statistics
How can we summarize the data with single statistics?
Averages, or measures of central tendency, include mode, median, and mean.
Mode is the value category that appears most often (i.e., is the most frequently observed).
Median is the middle value in the distribution, such that 50% of the data points are above and 50% are below.
Must rank order data from lowest to highest, or vice versa
Find the median location with formula, (n+1)/2
Count to the median; if it is between 2 values, take the average
Mean is the arithmetic average, sum of all the observations divided by the number of observations.
Note: When using the frequency distribution, we made the judgment call to use “8” as the final category. If you used all 50 data points from the raw data, you would note that there is a family size of 9. Using the raw data would give us a more accurate mean, and it would be slightly higher, or 2.88.
The mean uses all the data in the data set, whereas the mode and median only use the most frequently observed value, or the middle value. Therefore, the mean is influenced by extreme high or low values. In this case, the few families of size 8 and 9 pulled the mean higher, actually closer to 3 as the “average” family size.
In summary, mode = 2; median = 2; mean = 2.88. The distribution of family size is skewed to the right because of some high values of the variable. In such cases, the median would be a better measure of central tendency to report to your audience.
Measures of Variation
We can also compute measures of variation. Without doing the actual computations, can you define and interpret what the above would tell you about the variable “family size”?
Range: The range would tell you how the observed values are spread out by subtracting the lowest value observed from the highest.
Interquartile range: Because the range is influenced by outliers (i.e., very high or very low values), the interquartile range is often used instead. This statistic uses the middle 50% of the data values, eliminating any outliers. This gives us a better sense of how the values cluster around the median.
Variance: The variance tells us how the values are clustered about the mean. This is not always an easy statistic to interpret, but it is valuable in many advanced statistical computations.
Standard deviation: The standard deviation is the square root of the variance, and a bit more intuitive to interpret. Simply, you might think about the standard deviation as the average distance of the observations from the computed mean of the distribution. Like the mean, it is influenced by very high and very low outliers.
Practice Question: Using Mean and Standard Deviations
Suppose you are the director of an agency and you want to promote one of your front line staff to supervisor. As a basis for your decision, you look at the mean number of days each staff person takes to get a client needed treatment.
Worker A: Mean = 22.4, s= 15.9
Worker B: Mean = 18.7, s = 36.5
Worker C: Mean = 24.6, s = 19.7
Whom would you select on the basis of this observation, and why?
How are summary statistics used in decision making? We often use means and standard deviations in progress reports. For example, at the end of this semester, you will all fill out a student evaluation of teaching for the instructor. The evaluative items are represented as an interval scale so that means and standard deviations of all the scores can be computed and given to the instructor as feedback. The means on each item tell the instructor how students, on average, rated him/her on that item. The standard deviations tell the instructor how much variability there was among students on that item.
In this example, the director has summarized some important productivity data. At first glance at the means, you might say Worker B gets to his/her clients much quicker than the others, so he/she is the logical person to promote. However, the standard deviations are also revealing. Worker B’s standard deviation is very high compared to the other two workers. This might mean there were one or two cases that he/she got to very, very quickly and those pulled his/her average lower. In other words, a couple of outlier cases make Worker B’s performance, on average, look better. But that variation is captured in the higher standard deviation. Worker A’s and C’s cases seem to cluster closer to their averages. Worker A has the second lowest average, and also the lowest variation. So he/she would be the better choice to promote if you are only considering these quantitative data.

Categorizing Positions
Please ensure you make assignment decisions using the p

Categorizing Positions
Please ensure you make assignment decisions using the project guidelines in “Data Projects;” as well as the below items.
Research the employment opportunities at a large business (such as a hospital, college, etc.) closest to you by visiting their employment website. Create a frequency table of how many positions are available in different categories such as Finance, Office/Clerical, Research, Security, and, Maintenance. If the chosen business does not have at least 20 open positions, find another employer. Respond to all questions below, (do not forget to provide the questions with your response).
included screenshot to see how it is suppose to be
Category Frequency Relative Frequency Cumulative Frequency
1.
2.
3.
4.
5.
What employer website did you visit?
Complete the frequency table summarizing the number of positions in each category.
Using the data in the table, make a statement about what each relative frequency tells you about the data.
Create a bar chart for the frequency table in Question 2 only highlighting the category and frequency.
Create a pie chart for a category and relative frequency. You may need to create a frequency table in Excel to create the pie chart and insert it into your Word Document.

At the start of the project, you documented your typical responses along with yo

At the start of the project, you documented your typical responses along with your question formulations. You did not know it at the time, but you were hypothesizing about the future survey results. Now that you have the actual survey data, you can go back and apply the tools of inferential statistics to test your hypotheses.
Assignment Overview and Preparation
For this assignment, you will:
Calculate an appropriate 95% confidence interval for each question.
Perform an appropriate, one-sample hypothesis test for each question. Based on the context of your questions, you may choose to set up your hypothesis test as a one-sided test or two-sided test.
Remember, we want to estimate population proportions in questions 1–4 and population means in questions 5–6 (from Week 7). We already calculated the sample statistics for each question in Week 7. Feel free to use this prior work to help complete the above tasks. That is, we already have sample proportions for questions 1–4 as well as sample means and standard deviations for questions 5–6.
Use the Analyzing Data With Inferential Statistics Template [XLSX]. The template has two pages. Be sure to review each one carefully. The first page is the blank template that you will complete, and the second page is a completed example. Almost every type of situation is shown, so try to model your results after the ones shown.
Make sure you have watched the Week 9 Project Video. https://media.capella.edu/coursemedia/mat2001eleme…Your instructor will walk you through the process for completing this assignment.
Instructions
Complete the following in order to apply the tools of inferential statistics to test your hypotheses:
Open and complete the Excel Analyzing Data With Inferential Statistics Template [XLSX].
Calculate a 95% confidence interval for each of your survey questions (1–6). Your final product should have six confidence intervals.
Perform a hypothesis test for each survey question (1–6). Your final product should have six hypothesis tests.
When you have completed this assignment, submit it to your instructor in the Analyzing Data Using Inferential Statistics assignment area.
Before submitting it, refer to the Analyzing the Data Set Using Inferential Statistics rubric to ensure that it meets the grading criteria.
Competencies Measured
By successfully completing this assignment , you will demonstrate your proficiency in the following course competencies and rubric criteria:
Competency 1: Interpret a data set’s central tendency and variability using descriptive statistical procedures.
Compute a 95% confidence interval correctly for variable 1.
Compute a 95% confidence interval correctly for variable 2.
Compute a 95% confidence interval correctly for variable 3.
Compute a 95% confidence interval correctly for variable 4.
Compute a 95% confidence interval correctly for variable 5.
Compute a 95% confidence interval correctly for variable 6.
Calculate a hypothesis test correctly for variable 1.
Calculate a hypothesis test correctly for variable 2.
Calculate a hypothesis test correctly for variable 3.
Calculate a hypothesis test correctly for variable 4.
Calculate a hypothesis test correctly for variable 5.
Calculate a hypothesis test correctly for variable 6.

Read the case study “Case 5: The Philadelphia Sugar-Sweetened Beverage Tax” in y

Read the case study “Case 5: The Philadelphia Sugar-Sweetened Beverage Tax” in your supplemental textbook, JPHMP’s 21 Public Health Case Studies on Policy & Administration.
Additionally, review the chapter in your textbook, Health Policy Analysis: An Interdisciplinary Approach, entitled “The Policy Analysis Process: Analysis of Values and Social Context.” Pay particular attention to the sections in this chapter on “Personal Responsibility” and “Consumer Sovereignty.” In a 5-6 page paper, answer the questions below:
While the sugar-sweetened beverage tax was proposed on varied platforms, what discussion can be built around the idea that a tax may discourage consumption of these beverages and, in turn, improve overall health?
What level of personal responsibility can be expected in one’s health?
Does this proposed tax, while levied at the corporate level, impose on consumer sovereignty?
City Council opposed the sugar-sweetened beverage tax because they believed the tax was regressive and disproportionately and unfairly burdened low-income people. Does this policy have any inequitable implications for people from different socioeconomic statuses?
Submission Details
Cite all sources using APA format. NO AI STRICKLY PROHIBITED
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1.Shown below are the 30 survival times (months) of 30 melanoma patients, 11 of

1.Shown below are the 30 survival times (months) of 30 melanoma patients, 11 of which were treated with the
immunotherapy BCG and 19 treated with the immunotherapy c. parvum. The “+” indicates
censoring.
BCG: 3.9 5.4 7.9 10.5 16.6+ 16.9+ 17.1+ 19.5 23.8+ 33.7+
33.7+
c parvum: 6.9 7.7 7.8+ 8.0 8.2+ 8.2+ 8.3 10.8+ 11.0+ 12.2+
12.5+ 14.8+ 16.0+ 18.1+ 21.4+ 23.0+ 24.4 24.8+ 26.9+
a. Estimate the survival curves for each treatment using the Kaplan-Meier method by hand.
b. Plot the curves on the same graph.
2. As one example of survival data, we looked at the remission duration times on 42 patients with acute
leukemia, half of which received 6-MP and the other half a placebo. The dataset LEUKEMIA.DAT on
Canvas has with the following format:
Variable Columns
Group (1 = 6-MP, 2 = Placebo) 1
Remission time (in weeks) 3-4
Censoring status (1=censored, 2=not censored) 6
a. Estimate the Kaplan-Meier remission duration curves using SAS.
b. Test to determine if the groups are significantly different and interpret your results.
3. A study examines the effect of stopping smoking on survival in advanced lung cancer patients. Of 137
patients, 122 died by the end of the study period. Survival time is taken from date of diagnosis to death or
last contact. The following variables are included in the analysis:
Variable Coding _
1. Smoking status 0: continues smoking after Dx
1: stopped >1 yr prior to Dx
2. Performance status 0 : ≥ 80
1 : ≤ 70
3 & 4. Weight loss prior to therapy (3 categories)
(2 dummy variables created: WTL1 and WTL2)
None WTL1=0 and WTL2=0
≤ 10% WTL1=1 and WTL2=0
> 10% WTL1=0 and WTL2=1
5 & 6. Histology (3 categories)
(2 dummy variables created: HIST1 and HIST2)
Squamous Cell HIST1=0 and HIST2=0
Adenocarcinoma HIST1=1 and HIST2=0
Larqe cell HIST1=0 and HIST2=1
The table below summarizes the results of fitting several proportional hazard models. Regression coefficients &
log likelihoods are shown.
Model
Variables 1 * 2 3 4 5
Smoking(SMK) -0.552 -0.162 -0.032
Perf. Status (PS) 0.561 0.588 0.513
WTL1 0.056 0.058 0.063
WTL2 0.083 0.094 0.110
HIST1 0.061 0.066 0.132
HIST2 0.811 0.766 0.435
SMKxPS interaction -0.006
SMKxWTL1 ” 0.012
SMKxWTL2 ” 0.003
SMKxHIST1 ” -0.002
SMKxHIST2 ” -0.105
Log likelihood -486.54 -483.20 -476.12 -475.84 -472.73
* No variables in model
a. Compare the unadjusted survival curves for smoking status. If significant, describe the relationship
(direction, magnitude).
b. Determine whether the interaction terms of smoking status with performance status, weight loss and
histology taken together affect survival.
c. Assuming no interactions, compare the survival curves for smoking status adjusted for performance
status, weight loss and histology. If significant, describe the relationship.
d. What are your conclusions?

Categorizing Positions
Please ensure you make assignment decisions using the p

Categorizing Positions
Please ensure you make assignment decisions using the project guidelines in “Data Projects;” as well as the below items.
Research the employment opportunities at a large business (such as a hospital, college, etc.) closest to you by visiting their employment website. Create a frequency table of how many positions are available in different categories such as Finance, Office/Clerical, Research, Security, and, Maintenance. If the chosen business does not have at least 20 open positions, find another employer. Respond to all questions below, (do not forget to provide the questions with your response). included screenshot to see how it is suppose to be
Category Frequency Relative Frequency Cumulative Frequency
1. 2. 3. 4. 5. What employer website did you visit?
Complete the frequency table summarizing the number of positions in each category.
Using the data in the table, make a statement about what each relative frequency tells you about the data.
Create a bar chart for the frequency table in Question 2 only highlighting the category and frequency.
Create a pie chart for a category and relative frequency. You may need to create a frequency table in Excel to create the pie chart and insert it into your Word Document.

Hello,
attached is my excel sheet data that i want to make statistics
and introd

Hello,
attached is my excel sheet data that i want to make statistics
and introduction – Materials and methods so the writer can know more about the idea.
Also attached are screenshots for the statistics that i want to make like it
i attached pdf for similar article for our needs in statistics
i want the writer to make comments anout all statistics that will be done

PH 627: Advanced Statistical Methods in Public Health
M5 Assignment
1. A study w

PH 627: Advanced Statistical Methods in Public Health
M5 Assignment
1. A study was undertaken to measure and compare sexist attitudes of students at various types of colleges.
Random samples of 10 undergrad seniors of each sex were selected from each of three types of colleges. A
questionnaire was then administered to each student, from which a score for “degree of sexism”—defined as
the extent to which a student considered males and females to have different life roles—was determined (the
higher the score, the more sexist the attitude). The resulting data are given in the following table.
College Type Male Female
Coed with 75% or more
males
50, 35, 37, 32,
46, 38, 36, 40,
38, 41
38, 27, 34, 30,
22, 32, 26, 24,
31, 33
Coed with less than 75%
males
30, 29, 31, 27,
22, 20, 31, 22,
25, 30
28, 31, 28, 26,
20, 24, 31, 24,
31, 26
Not coed 45, 40, 32, 31,
26, 28, 39, 27,
37, 35
40, 35, 32, 29,
24, 26, 36, 25,
35, 35
a. Plot the cell means by college type for males and females.
b. Use SAS to calculate the F statistics corresponding to a model with both factors fixed.
c. Discuss the analysis of the data for this fixed-effects model case.
2. A crime victimization study was undertaken in a medium-sized southern city. The main purpose was to
determine the effects of being a crime victim on confidence in the law enforcement authority and in the
legal system itself. A questionnaire was administered to a stratified random sample of 40 city residents;
among the information elicited were data on the number of times victimized, a measure of social class
status, and a measure of the respondent’s confidence in law enforcement and in the legal system. The data
are produced in the following table.
Number of times
Victimized
Social Class Status
LO MED HI
0 4, 14, 15, 19, 17, 17,
16
7, 10, 12, 15,
16
8, 19, 10,
17
1 2, 7, 18 6, 19, 12, 12 7, 6, 5, 3,
16
2+ 7, 8, 2, 11, 12 1, 2, 4 4, 2, 8, 9
a. Plot the cell means by number of times victimized and social class status.
b. Use SAS to calculate the F statistics corresponding to a model with both factors fixed.
c. Discuss the analysis of the data for this fixed-effects model case.

There is often a requirement to evaluate descriptive statistics for data within

There is often a requirement to evaluate descriptive statistics for data within an organization or for health care information. Every year, the National Cancer Institute collects and publishes data based on patient demographics. Understanding differences between the groups based upon the collected data often informs health care professionals about research, treatment options, or patient education.
Using the data on the “National Cancer Institute Data” Excel spreadsheet, calculate the descriptive statistics indicated below for each of the race/ethnicity groups.
Provide the following descriptive statistics:
Measures of central tendency: mean, median, and mode.
Measures of variation: variance, standard deviation, and range (a formula is not needed for range).
Once the data are calculated, provide a 150-250-word analysis of the descriptive statistics on the spreadsheet. This should include differences and health outcomes between groups.
APA style is not required, but solid academic writing is expected.