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Function.

In math, a function is a relationship between a set of elements typically called X (or domain) and another

set of elements Y (codomain or f(x)) so that each element X of the domain corresponds to a single element

Y of the codomain.

Right triangle.

A right triangle is a triangle with one interior angle of 90°.

Tangent and inverse tangent.

The tangent of an angle is defined as the ratio of the length of the

opposite side to the length of the adjacent side.

tan

(

𝐴

)

=

𝑎

𝑏

The inverse tangent is the inverse function of the tangent function.

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Function. In math, a function is a relationship between a set of elements typically called X (or domain) and another set of elements Y (codomain or f(x)) so that each element X of the domain corresponds to a single element Y of the codomain. Right triangle. A right triangle is a triangle with one in ...
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10 pages

Deliverable5 Hypothesis Tests For Two Samples

Instructions: The following worksheets describe two problems – the first problem is for independent samples and the seco ...

Deliverable5 Hypothesis Tests For Two Samples

Instructions: The following worksheets describe two problems – the first problem is for independent samples and the seco is for dependent samples. ...

7 pages

Assignment Statistical Thinking In Healthcare.edited

From the above process map, there are two key problems that the HMO Pharmacy might be experiencing. Firstly, the pharmacy ...

Assignment Statistical Thinking In Healthcare.edited

From the above process map, there are two key problems that the HMO Pharmacy might be experiencing. Firstly, the pharmacy may be processing inaccurate ...

Capella University Descriptive Statistics and Data Visualizations Summary

Create a descriptive statistics table and histogram for selected variables in a dataset. Write a 2-3 page narrative summar ...

Capella University Descriptive Statistics and Data Visualizations Summary

Create a descriptive statistics table and histogram for selected variables in a dataset. Write a 2-3 page narrative summary in a Word document and insert the table and histogram graphic into this document.IntroductionNote: You are strongly encouraged to complete the assessments in this course in the order they are presented.Descriptive statistics play an important role in analyzing data. It numerically summarizes key characteristics of variables. Measures of central tendency (mean, median, and mode) and dispersion (variance, standard deviation, and range) characterize the probability distribution. A histogram visualizes the distribution of numerical data indicating the number of data points that lie within a range of values. For this assessment, you will perform descriptive statistics and create a histogram for selected variables in a dataset.OverviewNote: Complete the assessments in this course in the order in which they are presented.Data Analysis SoftwareTo complete the assessments in this course, you will need to use data analysis software to complete statistical calculations, create graphs, and tabular summaries, and share your results in a Microsoft Word document. You will use Microsoft Excel and Microsoft Excel Analysis ToolPak add-in. Be aware that not all statistical calculations, graphs, and tabular summaries can be completed in this software, so you may need to do some work outside of the software.Microsoft Excel Data Analysis ToolPak Add-In for PC and MacMicrosoft Excel Analysis ToolPak Activation for PC Users: The Microsoft Excel Analysis ToolPak is a specialized Excel add-in included in the standard Microsoft Office suite. If your Excel software meets the standard Capella University Computer Requirements, you will not need to purchase this software. However, you may need to activate the application before you can use it. If you do not own Microsoft Excel, Capella arranges for a significant discount for Microsoft Office for Capella learners. The following provides an overview of the Analysis ToolPak, instructions for loading and activation and instructions on using the ToolPak to perform data analysis. Note that these instructions are for 2016 and older versions of Microsoft Excel for PC and for the 2016 version only for Mac. If you are using a different version, you may need to search Microsoft's website for the appropriate instructions.Use the Analysis ToolPak to Perform Complex Data Analysis.Preparation Download the Assessment 1 Dataset [XLSX]. The dataset contains the following variables:utilization (average number of patient days per month).satisfaction (patient satisfaction scores percentile rank).readmissions (readmission rate per month).InstructionsNursing home administration has the objectives of higher utilization, higher patient satisfaction, and lower readmissions, and they need to make a decision on whether to retain the department manager based on average performance over the past 70 months. For this assessment, create a descriptive statistics table and histogram based on the Assessment 1 Dataset. Write a narrative summary in a Word and insert the table and histogram graphic into this document. See Copy From Excel to Another Office Program for instructions. Submit both the Word document and the Excel file that shows the descriptive statistics output.Grading CriteriaThe numbered assessment instructions outlined below correspond to the grading criteria in the Descriptive Statistics and Data Visualizations Scoring Guide, so be sure to address each point. You may also want to review the performance-level descriptions for each criterion to see how your work will be assessed.Perform the appropriate descriptive statistics for selected variables in a dataset.Report descriptive statistics table with measures of central tendency and dispersion.Create a histogram that visually depicts the distribution of selected variables in a dataset.Interpret statistical results used in the data analysis.Write a narrative summary of the results that includes practical, administration-related implications of the descriptive statistics.Write a separate summary for the histogram.Write clearly and concisely, using correct grammar, mechanics, and APA formatting.Additional RequirementsYour assessment should meet the following requirements:Written communication: Write clearly, accurately, and professionally, incorporating sources appropriately.Length: 2–3 pagesResources: Not applicable.APA format: Cite your sources using current APA format.Font and font size: Times Roman, 12 point.Competencies MeasuredBy successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria:Competency 2: Analyze data using computer-based programming and software.Perform the appropriate descriptive statistics for selected variables in a dataset.Competency 3: Interpret results of data analysis for value-based health care decisions, policy, or practice.Interpret statistical results used in the data analysis.Competency 4: Present results of data analysis to support a decision or recommendation.Create a histogram that visually depicts the distribution of selected variables in a dataset.Write a narrative summary of the results that includes practical, administration-related implications of the descriptive statistics.Competency 5: Communicate audience-appropriate health management content in a logically structured and concise manner, writing clearly with correct use of grammar, punctuation, spelling, and APA style.Write clearly and concisely, using correct grammar, mechanics, and APA formatting.SCORING GUIDEUse the scoring guide to understand how your assessment will be evaluated.

Week 3 Discussion 1 and 2

must complete 8 post on with 3 days, must post dailyPractice: Week 3 Discussion Question Many people think of compounding ...

Week 3 Discussion 1 and 2

must complete 8 post on with 3 days, must post dailyPractice: Week 3 Discussion Question Many people think of compounding interest as a bad thing but it can also be good. Provide examples of the following:a time when compounding interest has been in your favora time when compounding interest has not been in your favorIf you do not have personal experience with compounding interest just explain in general terms when it would be good and when it would be badPractice: Week 3 Discussion Question 2Not completed (REQUIRED) DiscussionObjectives:3.2Have you ever signed a promissory note?If yes, what were the note's terms? If you have not, give an example of a time someone might sign a promissory note and what the terms might be.Post your response in the discussion area.

provability and statistics

Details:Complete the problems below from the textbook. You will need to use the "Buena School District Bus Data" file for ...

provability and statistics

Details:Complete the problems below from the textbook. You will need to use the "Buena School District Bus Data" file for this assignment.Chapter 1 - Problem 22Chapter 2 - Problem 53For problems requiring computations, please ensure that your Excel file includes the associated cell computations and/or statistics output. This information is needed in order to receive full credit on these problems.Submit output in one Excel file.You are not required to submit this assignment to Turnitin.SYM-506-RS-Buena School District Bus Data.xls

GCCCD Module 22 Stat Crunch Directions and T Score Calculations Problems

Learn by DoingMatched Pairs: In this lab you will learn how to conduct a matched pairs T-test for a population mean using ...

GCCCD Module 22 Stat Crunch Directions and T Score Calculations Problems

Learn by DoingMatched Pairs: In this lab you will learn how to conduct a matched pairs T-test for a population mean using StatCrunch. We will work with a data set that has historical importance in the development of the T-test.Some features of this activity may not work well on a cell phone or tablet. We highly recommend that you complete this activity on a computer.Here are the directions, grading rubric, and definition of high-quality feedback for the Learn by Doing discussion board exercises. A list of StatCrunch directions is provided at the bottom of this page.ContextGosset's Seed Plot DataWilliam S. Gosset was employed by the Guinness brewing company of Dublin. Sample sizes available for experimentation in brewing were necessarily small. At that time, Gosset contacted a famous statistician Karl Pearson (1857-1936) and was told that there were no techniques for developing probability models for small data sets. Gosset studied under Pearson, and the outcome of his study was perhaps the most famous paper in statistical literature, "The Probable Error of a Mean" (1908), which introduced the T-distribution.Since Gosset was employed by Guinness, any work he produced would be owned by Guinness, so he published under a pseudonym, "Student"; hence, the T-distribution is often referred to as Student's T-distribution.To illustrate his analysis, Gosset used the results of seeding 11 different plots of land with two different types of seed: regular and kiln-dried. He wanted to determine if drying seeds before planting increased plant yield. Since different plots of soil may be naturally more fertile, this confounding variable was eliminated by using the matched pairs design and planting both types of seed in all 11 plots.The resulting data (corn yield in pounds per acre) are as follows.PlotRegular seedKiln-dried Seed11903200921935191531910201142496246352108218061961192572060212281444148291612154210131614431115111535We use these data to test the hypothesis that kiln-dried seed yields more corn than regular seed.Because of the nature of the experimental design (matched pairs), we are testing the difference in yield.PlotRegular seedKiln-dried SeedDifference119032009–10621935191520319102011–10142496246333521082180–7261961192536720602122–62814441482–38916121542701013161443–1271115111535–24Note that the differences were calculated: regular − kiln-dried.VariablesRegular seed: regular seeds that were traditionally used for plantingkiln-dried: seed that were kiln-dried before plantingDataDownload the seed (Links to an external site.) data file, and then upload the file into StatCrunch. PromptState the hypotheses and define the parameter.Checking conditions: Since Gosset invented the T-distribution, we will assume that his sample meets the conditions and proceed with the T-test. Regardless, answer these questions to demonstrate your understanding of the conditions for use of the T-model.But first you will need to review the dotplots for the data (opens in a new tab). Which graph is used to check conditions? Why?What do we look for in the graph to verify that conditions are met?What else do we need to know about the sample of seeds before using the T-test?Use StatCrunch to find the T-score and the P-value. Hint: as you work through the StatCrunch directions, keep in mind that we want to calculate the differences as regular − kiln-dried . So you will choose Regular seed for Sample 1 and kiln-dried seed for Sample 2. (directions)Copy and paste the information in the StatCrunch output window into your initial post.State a conclusion based on the context of this scenario.List of StatCrunch DirectionsEach link will open in a new window. To return to this discussion, either close the new tab or select the tab for this discussion. Create Your Stats-Class Folder in Canvas (You only need to do this once.)Purchase StatCrunch (You only need to do this once.)Open StatCrunchDownload Excel Data FileUpload Excel Data File to StatCrunchDownload StatCrunch Output Window (no screenshots; please use these directions)Upload Files into Your Stat-Class Folder in CanvasConduct Matched-Pairs T-testCopy & Paste a StatCrunch TableHere is a PDF document with all StatCrunch directions (Links to an external site.).

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Deliverable5 Hypothesis Tests For Two Samples

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Assignment Statistical Thinking In Healthcare.edited

From the above process map, there are two key problems that the HMO Pharmacy might be experiencing. Firstly, the pharmacy ...

Assignment Statistical Thinking In Healthcare.edited

From the above process map, there are two key problems that the HMO Pharmacy might be experiencing. Firstly, the pharmacy may be processing inaccurate ...

Capella University Descriptive Statistics and Data Visualizations Summary

Create a descriptive statistics table and histogram for selected variables in a dataset. Write a 2-3 page narrative summar ...

Capella University Descriptive Statistics and Data Visualizations Summary

Create a descriptive statistics table and histogram for selected variables in a dataset. Write a 2-3 page narrative summary in a Word document and insert the table and histogram graphic into this document.IntroductionNote: You are strongly encouraged to complete the assessments in this course in the order they are presented.Descriptive statistics play an important role in analyzing data. It numerically summarizes key characteristics of variables. Measures of central tendency (mean, median, and mode) and dispersion (variance, standard deviation, and range) characterize the probability distribution. A histogram visualizes the distribution of numerical data indicating the number of data points that lie within a range of values. For this assessment, you will perform descriptive statistics and create a histogram for selected variables in a dataset.OverviewNote: Complete the assessments in this course in the order in which they are presented.Data Analysis SoftwareTo complete the assessments in this course, you will need to use data analysis software to complete statistical calculations, create graphs, and tabular summaries, and share your results in a Microsoft Word document. You will use Microsoft Excel and Microsoft Excel Analysis ToolPak add-in. Be aware that not all statistical calculations, graphs, and tabular summaries can be completed in this software, so you may need to do some work outside of the software.Microsoft Excel Data Analysis ToolPak Add-In for PC and MacMicrosoft Excel Analysis ToolPak Activation for PC Users: The Microsoft Excel Analysis ToolPak is a specialized Excel add-in included in the standard Microsoft Office suite. If your Excel software meets the standard Capella University Computer Requirements, you will not need to purchase this software. However, you may need to activate the application before you can use it. If you do not own Microsoft Excel, Capella arranges for a significant discount for Microsoft Office for Capella learners. The following provides an overview of the Analysis ToolPak, instructions for loading and activation and instructions on using the ToolPak to perform data analysis. Note that these instructions are for 2016 and older versions of Microsoft Excel for PC and for the 2016 version only for Mac. If you are using a different version, you may need to search Microsoft's website for the appropriate instructions.Use the Analysis ToolPak to Perform Complex Data Analysis.Preparation Download the Assessment 1 Dataset [XLSX]. The dataset contains the following variables:utilization (average number of patient days per month).satisfaction (patient satisfaction scores percentile rank).readmissions (readmission rate per month).InstructionsNursing home administration has the objectives of higher utilization, higher patient satisfaction, and lower readmissions, and they need to make a decision on whether to retain the department manager based on average performance over the past 70 months. For this assessment, create a descriptive statistics table and histogram based on the Assessment 1 Dataset. Write a narrative summary in a Word and insert the table and histogram graphic into this document. See Copy From Excel to Another Office Program for instructions. Submit both the Word document and the Excel file that shows the descriptive statistics output.Grading CriteriaThe numbered assessment instructions outlined below correspond to the grading criteria in the Descriptive Statistics and Data Visualizations Scoring Guide, so be sure to address each point. You may also want to review the performance-level descriptions for each criterion to see how your work will be assessed.Perform the appropriate descriptive statistics for selected variables in a dataset.Report descriptive statistics table with measures of central tendency and dispersion.Create a histogram that visually depicts the distribution of selected variables in a dataset.Interpret statistical results used in the data analysis.Write a narrative summary of the results that includes practical, administration-related implications of the descriptive statistics.Write a separate summary for the histogram.Write clearly and concisely, using correct grammar, mechanics, and APA formatting.Additional RequirementsYour assessment should meet the following requirements:Written communication: Write clearly, accurately, and professionally, incorporating sources appropriately.Length: 2–3 pagesResources: Not applicable.APA format: Cite your sources using current APA format.Font and font size: Times Roman, 12 point.Competencies MeasuredBy successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria:Competency 2: Analyze data using computer-based programming and software.Perform the appropriate descriptive statistics for selected variables in a dataset.Competency 3: Interpret results of data analysis for value-based health care decisions, policy, or practice.Interpret statistical results used in the data analysis.Competency 4: Present results of data analysis to support a decision or recommendation.Create a histogram that visually depicts the distribution of selected variables in a dataset.Write a narrative summary of the results that includes practical, administration-related implications of the descriptive statistics.Competency 5: Communicate audience-appropriate health management content in a logically structured and concise manner, writing clearly with correct use of grammar, punctuation, spelling, and APA style.Write clearly and concisely, using correct grammar, mechanics, and APA formatting.SCORING GUIDEUse the scoring guide to understand how your assessment will be evaluated.

Week 3 Discussion 1 and 2

must complete 8 post on with 3 days, must post dailyPractice: Week 3 Discussion Question Many people think of compounding ...

Week 3 Discussion 1 and 2

must complete 8 post on with 3 days, must post dailyPractice: Week 3 Discussion Question Many people think of compounding interest as a bad thing but it can also be good. Provide examples of the following:a time when compounding interest has been in your favora time when compounding interest has not been in your favorIf you do not have personal experience with compounding interest just explain in general terms when it would be good and when it would be badPractice: Week 3 Discussion Question 2Not completed (REQUIRED) DiscussionObjectives:3.2Have you ever signed a promissory note?If yes, what were the note's terms? If you have not, give an example of a time someone might sign a promissory note and what the terms might be.Post your response in the discussion area.

provability and statistics

Details:Complete the problems below from the textbook. You will need to use the "Buena School District Bus Data" file for ...

provability and statistics

Details:Complete the problems below from the textbook. You will need to use the "Buena School District Bus Data" file for this assignment.Chapter 1 - Problem 22Chapter 2 - Problem 53For problems requiring computations, please ensure that your Excel file includes the associated cell computations and/or statistics output. This information is needed in order to receive full credit on these problems.Submit output in one Excel file.You are not required to submit this assignment to Turnitin.SYM-506-RS-Buena School District Bus Data.xls

GCCCD Module 22 Stat Crunch Directions and T Score Calculations Problems

Learn by DoingMatched Pairs: In this lab you will learn how to conduct a matched pairs T-test for a population mean using ...

GCCCD Module 22 Stat Crunch Directions and T Score Calculations Problems

Learn by DoingMatched Pairs: In this lab you will learn how to conduct a matched pairs T-test for a population mean using StatCrunch. We will work with a data set that has historical importance in the development of the T-test.Some features of this activity may not work well on a cell phone or tablet. We highly recommend that you complete this activity on a computer.Here are the directions, grading rubric, and definition of high-quality feedback for the Learn by Doing discussion board exercises. A list of StatCrunch directions is provided at the bottom of this page.ContextGosset's Seed Plot DataWilliam S. Gosset was employed by the Guinness brewing company of Dublin. Sample sizes available for experimentation in brewing were necessarily small. At that time, Gosset contacted a famous statistician Karl Pearson (1857-1936) and was told that there were no techniques for developing probability models for small data sets. Gosset studied under Pearson, and the outcome of his study was perhaps the most famous paper in statistical literature, "The Probable Error of a Mean" (1908), which introduced the T-distribution.Since Gosset was employed by Guinness, any work he produced would be owned by Guinness, so he published under a pseudonym, "Student"; hence, the T-distribution is often referred to as Student's T-distribution.To illustrate his analysis, Gosset used the results of seeding 11 different plots of land with two different types of seed: regular and kiln-dried. He wanted to determine if drying seeds before planting increased plant yield. Since different plots of soil may be naturally more fertile, this confounding variable was eliminated by using the matched pairs design and planting both types of seed in all 11 plots.The resulting data (corn yield in pounds per acre) are as follows.PlotRegular seedKiln-dried Seed11903200921935191531910201142496246352108218061961192572060212281444148291612154210131614431115111535We use these data to test the hypothesis that kiln-dried seed yields more corn than regular seed.Because of the nature of the experimental design (matched pairs), we are testing the difference in yield.PlotRegular seedKiln-dried SeedDifference119032009–10621935191520319102011–10142496246333521082180–7261961192536720602122–62814441482–38916121542701013161443–1271115111535–24Note that the differences were calculated: regular − kiln-dried.VariablesRegular seed: regular seeds that were traditionally used for plantingkiln-dried: seed that were kiln-dried before plantingDataDownload the seed (Links to an external site.) data file, and then upload the file into StatCrunch. PromptState the hypotheses and define the parameter.Checking conditions: Since Gosset invented the T-distribution, we will assume that his sample meets the conditions and proceed with the T-test. Regardless, answer these questions to demonstrate your understanding of the conditions for use of the T-model.But first you will need to review the dotplots for the data (opens in a new tab). Which graph is used to check conditions? Why?What do we look for in the graph to verify that conditions are met?What else do we need to know about the sample of seeds before using the T-test?Use StatCrunch to find the T-score and the P-value. Hint: as you work through the StatCrunch directions, keep in mind that we want to calculate the differences as regular − kiln-dried . So you will choose Regular seed for Sample 1 and kiln-dried seed for Sample 2. (directions)Copy and paste the information in the StatCrunch output window into your initial post.State a conclusion based on the context of this scenario.List of StatCrunch DirectionsEach link will open in a new window. To return to this discussion, either close the new tab or select the tab for this discussion. Create Your Stats-Class Folder in Canvas (You only need to do this once.)Purchase StatCrunch (You only need to do this once.)Open StatCrunchDownload Excel Data FileUpload Excel Data File to StatCrunchDownload StatCrunch Output Window (no screenshots; please use these directions)Upload Files into Your Stat-Class Folder in CanvasConduct Matched-Pairs T-testCopy & Paste a StatCrunch TableHere is a PDF document with all StatCrunch directions (Links to an external site.).