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MIS 660 Females Schemic Heart Disease Data Manipulation Excel Worksheet
Data ManipulationThe purpose of this assignment is to use spreadsheet capabilities to perform data manipulation and to exp ...
MIS 660 Females Schemic Heart Disease Data Manipulation Excel Worksheet
Data ManipulationThe purpose of this assignment is to use spreadsheet capabilities to perform data manipulation and to explain the process used in the handling of the data.For this assignment, you will use the "Claims" dataset. In the dataset, the claims data for n = 608 people are recorded. The data derive from a random sample of females diagnosed with ischemic heart disease over 24 months (see Exercise 7.27 in the textbook).Instead of using urgent care centers, some people rely on the Emergency Room (ER) to address most, if not all, of their medical needs. In fact, someone who has three or more ER visits within 24 months is considered a high ER user. Complete the steps below to execute this assignment. Using the dataset and Excel, create a new column titled "High_ER_User" with "Yes" if three or more ER visits; otherwise "No." Duration is measured in days, but 30-day intervals are more appropriate for most reporting purposes. Using Excel, create a new column titled "Duration_Months" by converting the duration into 30-day intervals. Many times complications and comorbidities are rare; therefore, these two negative events are summed together. Using Excel, create a new column titled "Comps_Comorbs" by adding complications with comorbidities. Many times age is grouped in 10-year intervals. Using Excel's VLOOKUP function, create a new column titled "Age_Group" with grouped ages of "21-30 yrs," "31-40 yrs," and so on for 10-year intervals. The last age group would be "61-70 yrs." Use a tab titled "Age_Groups" for this task.Next you will create a pivot table with the data and execute the following (refer to the examples in the resource "Data Manipulation Screenshots"). Use "High_ER_User" as a filter to obtain two filtered views of the pivot table. Summarize the data to get counts of claims, sum of claims and months, and average of procedures, prescribed drugs, ER visits, and complications/comorbidities. Add a calculated field titled "Claims PM" to the pivot table. This calculated field is the sum of claims divided by the sum of duration months and measures the average claim amount per month (PM).APA format is not required, but solid academic writing is expected.This assignment uses a grading rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.You are not required to submit this assignment to LopesWrite.
FIN 307 Grantham University Principle of Finance Discussion
Ratio analysis enables stockholders, lenders, and the firm’s managers to evaluate the firm’s financial per ...
FIN 307 Grantham University Principle of Finance Discussion
Ratio analysis enables stockholders, lenders, and the firm’s managers to evaluate the firm’s financial performance. Compare and contrast who uses financial ratios and for what purposes they use the ratios.
Keiser Career College Converting Excel to SPSS X bar and Range Charts in SPSS Essay
Control charts assist health care administration leaders in determining which processes in their health services organizat ...
Keiser Career College Converting Excel to SPSS X bar and Range Charts in SPSS Essay
Control charts assist health care administration leaders in determining which processes in their health services organization are in control. As a tool, creating variable control charts such as the Xbar and R charts are useful since they help to present the data in meaningful ways, which allow health care administration leaders to make quick decisions regarding those processes not in control.
For this Assignment, review the resources for this week that are specific to variable control charts. Focus on mimicking the development of the Xbar and R charts in the chapter rather than the Xbar and S charts or I-MR charts.
Grossmont Cuyamaca Community College District Module 24 ANOVA Discussion
Learn by DoingThe purpose of this activity is to give you guided practice in carrying out the ANOVA F-test using StatCrunc ...
Grossmont Cuyamaca Community College District Module 24 ANOVA Discussion
Learn by DoingThe purpose of this activity is to give you guided practice in carrying out the ANOVA F-test using StatCrunch.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 and grading rubric for the Learn by Doing discussion board exercises. A list of StatCrunch directions is provided at the bottom of this page.ContextCritical flicker frequency (CFF) and eye colorComputer screens and fluorescent bulbs flicker. If the frequency of the flicker is below a certain threshold, the eye detects the flicker, and it is annoying!Different people have different flicker "threshold" frequencies (known as the critical flicker frequency, or CFF). The mean critical threshold frequency is important for product manufacturing as well as tests for ocular disease.In 1973, researchers conducted a study to answer the following question.Research question: Do people with different eye color have different threshold flicker sensitivity?The 1973 study ("The Effect of Iris Color on Critical Flicker Frequency," Journal of General Psychology [1973], 91–95) obtained the following data from a random sample of 19 subjects.ColorThresholdFrequency (CFF)BlueBrownGreen Brown 26.825.726.826.4 Brown 27.927.227.924.2 Brown 23.729.923.728 Brown 25.028.52526.9 Brown 26.329.426.329.1 Brown 24.828.324.8 Brown 25.725.7 Brown 24.524.5 Green 26.4 Green 24.2 Green 28.0 Green 26.9 Green 29.1 Blue 25.7 Blue 27.2 Blue 29.9 Blue 28.5 Blue 29.4 Blue 28.3In this spreadsheet the data is presented in two formats.Stacked data: The quantitative data is stacked in one column. The first two columns show the data in a stacked format. Each variable is a column (one column for the explanatory variable eye color; one column for the response variable CFF) and each row is an individual. For example, the first row is a brown-eyed person with a CFF of 26.8.Unstacked data: The quantitative data is distributed across the groups in multiple columns. The last three columns show the same data in an unstacked format. In this format, each column is a group defined by a value of the explanatory variable: one column for blue-eyed people, one column for brown-eyed people and one column for green-eyed people. Each column contains the response values (CFF) for that group.The format of the data in the spreadsheet affects how we use StatCrunch to analyze it.VariablesColor: This is the explanatory variable. The categorical data represents the groups we will compare.CFF (flicker threshold sensitivity): This is the response variable. The quantitative data represents the frequency threshold at which the subject sees the flicker.DataDownload the flicker (Links to an external site.) datafile. As always, ignore or close any prompt that invites you to login while downloading the file. Upload the datafile to StatCrunch.PromptWe will conduct an ANOVA F-test for the variables Color and CFF. The flicker datafile is available in the Data section below. Also, the StatCrunch directions are provided in the list a the bottom of this page.What are the hypotheses for the ANOVA test? Be sure that you define clearly the parameters.Are the conditions that allow us to safely use the ANOVA F-test met? Explain.Note: To verify conditions, you will need to examine the distribution of CFF scores for each sample (because the samples are small).Use StatCrunch to create side-by-side dotplots, histograms or boxplots (your choice) to examine the distribution of CFF scores for each sample. You can use either data format; choose one (stacked data in the first two columns; or unstacked data in the the last three columns). To create the side-by-side graphs (for either data format) see the list of StatCrunch directions below. Download the StatCrunch output window (your graph), upload it to your Stats-Class folder, and then embed the .png file (your graph) in your initial post. To recall how to complete these tasks, see the list of StatCrunch directions below.You will also need to compare the sample standard deviations. Use StatCrunch to find the summary statistics, means and standard deviations for the comparison groups (select the the appropriate Descriptive Statistics StatCrunch directions from the list below). Then copy and paste the table into your initial post and explain how the rule of thumb for comparing standard deviations is met.Use StatCrunch to carry out the ANOVA F-test (select the appropriate ANOVA StatCrunch directions from the following two options). Anova F-test Stacked Data Format ORAnova F-test Unstacked Data FormatCopy and paste the output table into your initial post.State your conclusion in context of eye color and mean CFF.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 CanvasEmbed Pictures in a Discussion Post (no attachments; please use these directions)Side-by-side Boxplots Stacked Data FormatSide-by-side Boxplots Unstacked Data FormatSide-by-side Dotplots Stacked Data FormatSide-by-side Dotplots Unstacked Data FormatSide-by-side Histograms Stacked Data FormatSide-by-side Histograms Unstacked Data FormatDescriptive Statistics Multiple Categories Stacked Data FormatDescriptive Statistics Multiple Categories Unstacked Data FormatCopy & Paste a StatCrunch TableAnova F-test Stacked Data FormatAnova F-test Unstacked Data Format
QMB 2100 University of South Florida Module 3 Business Statistics Project
A project assignment in statistics.
Provide the data and simply complete the questions in the assignment.
QMB 2100 University of South Florida Module 3 Business Statistics Project
A project assignment in statistics.
Provide the data and simply complete the questions in the assignment.
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MIS 660 Females Schemic Heart Disease Data Manipulation Excel Worksheet
Data ManipulationThe purpose of this assignment is to use spreadsheet capabilities to perform data manipulation and to exp ...
MIS 660 Females Schemic Heart Disease Data Manipulation Excel Worksheet
Data ManipulationThe purpose of this assignment is to use spreadsheet capabilities to perform data manipulation and to explain the process used in the handling of the data.For this assignment, you will use the "Claims" dataset. In the dataset, the claims data for n = 608 people are recorded. The data derive from a random sample of females diagnosed with ischemic heart disease over 24 months (see Exercise 7.27 in the textbook).Instead of using urgent care centers, some people rely on the Emergency Room (ER) to address most, if not all, of their medical needs. In fact, someone who has three or more ER visits within 24 months is considered a high ER user. Complete the steps below to execute this assignment. Using the dataset and Excel, create a new column titled "High_ER_User" with "Yes" if three or more ER visits; otherwise "No." Duration is measured in days, but 30-day intervals are more appropriate for most reporting purposes. Using Excel, create a new column titled "Duration_Months" by converting the duration into 30-day intervals. Many times complications and comorbidities are rare; therefore, these two negative events are summed together. Using Excel, create a new column titled "Comps_Comorbs" by adding complications with comorbidities. Many times age is grouped in 10-year intervals. Using Excel's VLOOKUP function, create a new column titled "Age_Group" with grouped ages of "21-30 yrs," "31-40 yrs," and so on for 10-year intervals. The last age group would be "61-70 yrs." Use a tab titled "Age_Groups" for this task.Next you will create a pivot table with the data and execute the following (refer to the examples in the resource "Data Manipulation Screenshots"). Use "High_ER_User" as a filter to obtain two filtered views of the pivot table. Summarize the data to get counts of claims, sum of claims and months, and average of procedures, prescribed drugs, ER visits, and complications/comorbidities. Add a calculated field titled "Claims PM" to the pivot table. This calculated field is the sum of claims divided by the sum of duration months and measures the average claim amount per month (PM).APA format is not required, but solid academic writing is expected.This assignment uses a grading rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.You are not required to submit this assignment to LopesWrite.
FIN 307 Grantham University Principle of Finance Discussion
Ratio analysis enables stockholders, lenders, and the firm’s managers to evaluate the firm’s financial per ...
FIN 307 Grantham University Principle of Finance Discussion
Ratio analysis enables stockholders, lenders, and the firm’s managers to evaluate the firm’s financial performance. Compare and contrast who uses financial ratios and for what purposes they use the ratios.
Keiser Career College Converting Excel to SPSS X bar and Range Charts in SPSS Essay
Control charts assist health care administration leaders in determining which processes in their health services organizat ...
Keiser Career College Converting Excel to SPSS X bar and Range Charts in SPSS Essay
Control charts assist health care administration leaders in determining which processes in their health services organization are in control. As a tool, creating variable control charts such as the Xbar and R charts are useful since they help to present the data in meaningful ways, which allow health care administration leaders to make quick decisions regarding those processes not in control.
For this Assignment, review the resources for this week that are specific to variable control charts. Focus on mimicking the development of the Xbar and R charts in the chapter rather than the Xbar and S charts or I-MR charts.
Grossmont Cuyamaca Community College District Module 24 ANOVA Discussion
Learn by DoingThe purpose of this activity is to give you guided practice in carrying out the ANOVA F-test using StatCrunc ...
Grossmont Cuyamaca Community College District Module 24 ANOVA Discussion
Learn by DoingThe purpose of this activity is to give you guided practice in carrying out the ANOVA F-test using StatCrunch.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 and grading rubric for the Learn by Doing discussion board exercises. A list of StatCrunch directions is provided at the bottom of this page.ContextCritical flicker frequency (CFF) and eye colorComputer screens and fluorescent bulbs flicker. If the frequency of the flicker is below a certain threshold, the eye detects the flicker, and it is annoying!Different people have different flicker "threshold" frequencies (known as the critical flicker frequency, or CFF). The mean critical threshold frequency is important for product manufacturing as well as tests for ocular disease.In 1973, researchers conducted a study to answer the following question.Research question: Do people with different eye color have different threshold flicker sensitivity?The 1973 study ("The Effect of Iris Color on Critical Flicker Frequency," Journal of General Psychology [1973], 91–95) obtained the following data from a random sample of 19 subjects.ColorThresholdFrequency (CFF)BlueBrownGreen Brown 26.825.726.826.4 Brown 27.927.227.924.2 Brown 23.729.923.728 Brown 25.028.52526.9 Brown 26.329.426.329.1 Brown 24.828.324.8 Brown 25.725.7 Brown 24.524.5 Green 26.4 Green 24.2 Green 28.0 Green 26.9 Green 29.1 Blue 25.7 Blue 27.2 Blue 29.9 Blue 28.5 Blue 29.4 Blue 28.3In this spreadsheet the data is presented in two formats.Stacked data: The quantitative data is stacked in one column. The first two columns show the data in a stacked format. Each variable is a column (one column for the explanatory variable eye color; one column for the response variable CFF) and each row is an individual. For example, the first row is a brown-eyed person with a CFF of 26.8.Unstacked data: The quantitative data is distributed across the groups in multiple columns. The last three columns show the same data in an unstacked format. In this format, each column is a group defined by a value of the explanatory variable: one column for blue-eyed people, one column for brown-eyed people and one column for green-eyed people. Each column contains the response values (CFF) for that group.The format of the data in the spreadsheet affects how we use StatCrunch to analyze it.VariablesColor: This is the explanatory variable. The categorical data represents the groups we will compare.CFF (flicker threshold sensitivity): This is the response variable. The quantitative data represents the frequency threshold at which the subject sees the flicker.DataDownload the flicker (Links to an external site.) datafile. As always, ignore or close any prompt that invites you to login while downloading the file. Upload the datafile to StatCrunch.PromptWe will conduct an ANOVA F-test for the variables Color and CFF. The flicker datafile is available in the Data section below. Also, the StatCrunch directions are provided in the list a the bottom of this page.What are the hypotheses for the ANOVA test? Be sure that you define clearly the parameters.Are the conditions that allow us to safely use the ANOVA F-test met? Explain.Note: To verify conditions, you will need to examine the distribution of CFF scores for each sample (because the samples are small).Use StatCrunch to create side-by-side dotplots, histograms or boxplots (your choice) to examine the distribution of CFF scores for each sample. You can use either data format; choose one (stacked data in the first two columns; or unstacked data in the the last three columns). To create the side-by-side graphs (for either data format) see the list of StatCrunch directions below. Download the StatCrunch output window (your graph), upload it to your Stats-Class folder, and then embed the .png file (your graph) in your initial post. To recall how to complete these tasks, see the list of StatCrunch directions below.You will also need to compare the sample standard deviations. Use StatCrunch to find the summary statistics, means and standard deviations for the comparison groups (select the the appropriate Descriptive Statistics StatCrunch directions from the list below). Then copy and paste the table into your initial post and explain how the rule of thumb for comparing standard deviations is met.Use StatCrunch to carry out the ANOVA F-test (select the appropriate ANOVA StatCrunch directions from the following two options). Anova F-test Stacked Data Format ORAnova F-test Unstacked Data FormatCopy and paste the output table into your initial post.State your conclusion in context of eye color and mean CFF.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 CanvasEmbed Pictures in a Discussion Post (no attachments; please use these directions)Side-by-side Boxplots Stacked Data FormatSide-by-side Boxplots Unstacked Data FormatSide-by-side Dotplots Stacked Data FormatSide-by-side Dotplots Unstacked Data FormatSide-by-side Histograms Stacked Data FormatSide-by-side Histograms Unstacked Data FormatDescriptive Statistics Multiple Categories Stacked Data FormatDescriptive Statistics Multiple Categories Unstacked Data FormatCopy & Paste a StatCrunch TableAnova F-test Stacked Data FormatAnova F-test Unstacked Data Format
QMB 2100 University of South Florida Module 3 Business Statistics Project
A project assignment in statistics.
Provide the data and simply complete the questions in the assignment.
QMB 2100 University of South Florida Module 3 Business Statistics Project
A project assignment in statistics.
Provide the data and simply complete the questions in the assignment.
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