Our lecture discussed the ICD-10-CM manual (indexes, tabular list, appendix) and

Our lecture discussed the ICD-10-CM manual (indexes, tabular list, appendix) and the correct steps to find and assign an ICD-10-CM code. It also touched upon certain areas of a patient’s record that coders should not use when assigning a code. In at least 150 of your own words, reflect on the following:
Based on coding guidelines, coders may only code from a physician or other qualified healthcare provider’s notes. Discuss why you think this is a requirement and whether you agree. Explain your reasoning for why or why not.
In relation to the prompt above, consider the following scenario and explain the correct course of action. While reviewing a patient record, you notice documentation in the nursing notes that may conflict with the physician’s diagnosis. As a coder, what is your responsibility in this case, and how do you proceed?

Examine and test the relationships between several variables. Note this requires

Examine and test the relationships between several variables. Note this requires sufficient continuous variables, that are measured at the Interval level, or higher. Provide the R codes in the R-script file under the sub-heading Module 5, and the statistical outputs under the sub-heading “Module 5” in the Word file. In general, your statistical outputs should be titled and labeled so that they can stand on their own (and can be understood by anyone who looks at them for the first time).
Produce and export at least one correlation table or correlation chart. A correlation chart is diagnostic, and should not be larger than 5 variables for reporting purposes. Why is this? Provide several sentences describing the key analytical findings.
Produce and export at least one regression table. You may pick your own outcome and predictor variables. How does regression analysis differ from correlation analysis? Provide several sentences discussing the key results.

Examine and test the relationships between several variables. Note this requires

Examine and test the relationships between several variables. Note this requires sufficient continuous variables, that are measured at the interval level, or higher. Provide the R codes in the R-script file under the sub-heading Module 5, and the statistical outputs under the sub-heading “Module 5” in the Word file. In general, your statistical outputs should be titled and labeled so that they can stand on their own (and can be understood by anyone who looks at them for the first time).
Produce and export at least one correlation table or correlation chart. A correlation chart is diagnostic, and should not be larger than 5 variables for reporting purposes. Why is this? Provide several sentences describing the key analytical findings.
Produce and export at least one regression table. You may pick your own outcome and predictor variables. How does regression analysis differ from correlation analysis? Provide several sentences discussing the key results.

The purpose of this final is to apply your knowledge in comparing and contrastin

The purpose of this final is to apply your knowledge in comparing and contrasting a supervised and an unsupervised algorithm as well as a comparing and contrasting a NoSQL and SQL solution and presenting it in a 3 page minimum written report.
Total of 3 pages (minimum) with the following sections:
· 2 pages (minimum):
· Select a supervised and an unsupervised algorithm to compare and contrast
· Metrics such as what parameters are used in the algorithms, performance of algorithms, explainability/complexity, etc.
· Recommended use cases for each algorithm (How can this be applied in the real world?)
· Code sample (optional)
· Select a data storage solution (NoSQL and SQL) to compare and contrast
· Metrics such as languages, capacity, scalability, etc.
· Recommended use cases for each algorithm (How can this be applied in the real world?)
· Code sample (optional)
· 1 page (minimum):
· Create or source a solution/conclusion in Visual format (i.e. charts, graphics, other visuals etc.)
· Submit data used (or link to data used if ‘too large’, i.e. 15 mb or more), and code/if any
Note: Code is optional (and tables count as visual as well)
(Attached are the complete assignment requirements with grading criteria)

Assignment Overview Select a data set that pertains to a topic of your own inter

Assignment Overview
Select a data set that pertains to a topic of your own interest.
After completing an exploratory data analysis, you should have a good sense of the data as well as specific questions you want to ask of your data or the population as a whole. Often these can be answered through inferential statistics and hypothesis testing. In this assignment, you will identify specific questions you have about the data or population parameters and document your inferential testing of your data.
Assignment Instructions
Using your dataset, and substantive research you have done on this topic, identify at least 2 -3 questions you have about the data. These questions should employ inferential statistics and hypothesis testing to find answers. Then find the answer to the questions using hypothesis testing. Be sure that you use both one-sample and two-sample tests.
For each test, document the hypothesis testing steps and the results at each step. Finally, provide an analysis of the final results with an explanation of your interpretation.
What to Submit
You must submit a 2-3 page report that includes:
Your original questions you explored
Your null and alternative hypothesis (including an explanation of whether it was a one-sample or two-sample test)
Explanation of the hypothesis testing you completed
The results
Your interpretation of the results
Also, submit your R code.

Select a dataset of your own interest, that allows you to perform t-tests. Expla

Select a dataset of your own interest, that allows you to perform t-tests. Explain and justify, why your research question of interest should be a one- or a two-sided test.
For this assignment, please submit your R code, the output, graphs and figures (if any), and your interpretation in a document with a cover page that shows your name, course number, instructor name, and the assignment number.
Should this be a one- or a two-sample test, in other words: are the population parameters given? Why? Think about all the eheuristics that apply to choosing the appropriate test(s).

This week’s assignment involves writing a Python program to compute the weekly

This week’s assignment involves writing a Python program to compute the
weekly pay for a paper carrier. Your program should prompt the user for
the following numeric values:
the number of papers on the route
the number of days the paper is delivered per week
the amount of tips received for the week
Your program should define (not prompt) for the following:
the cost of each newspaper,
a percentage rate for his/her commission. (the paper carrier gets a percentage of the cost of each newspaper)
The total number of papers for week is number of papers times number of
days. The weekly salary is total number of papers for week times cost of
each newspaper times his percentage rate. The total pay should be
computed as his weekly salary plus his tips.
Your program should display output at the end of the program for the following:
the total number of papers delivered for the week,
the weekly salary,
the tips for the week,
the total pay for the week.
Your program should include Header comments (what the program does) and
in-line comments (the major design steps). Document the values you
chose for the cost per paper and percentage rate in your comments as
well.
Submit your Python program as a text file (.py) file. In addition,
submit a Design outline and a Test plan (3 different test cases) in a
Word document or a .pdf file which also includes a screen shot of
execution of your program for each test case.
Your submission must also adhere to the SubmissionRequirements document.
(i.e. Filename and display your name, class, date in the output).
GRADING:
10% – Design – outline proper sequence of steps, calculations (if
necessary). Identify values
of any known constants (hourly rate,
commission rate). Identify what the user inputs will be
and what the
output will be.
10% – Documentation – Header and in-line comments. Include document for
the values you
chose as the known constants (hourly rate, commission
rate) in your comments as well.
Documentation of major steps (from
design outline);
70% – Program prompts and executes correctly on all test cases.
Satisfies all requirements
(each requirement -10pts), compiles -20pts,
effectiveness and neatness -10pts, descriptive
variables – 5pts, def
main() -5pts).