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Grossmont College Treating Depression Lab Report
ContextClinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who ...
Grossmont College Treating Depression Lab Report
ContextClinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who have an episode of major depression will have a recurrence within 2-3 years. Being able to prevent the recurrence of depression in people who are at risk for the disease would go a long way to alleviate the pain and suffering of patients. During the 1980's the federal government, through the National Institutes of Health (NIH), sponsored a large clinical trial to evaluate two drugs for depression. There were 3 treatment groups. Patients received either Imipramine (Imip), Lithium (Li), or a Placebo (Pl). Researchers randomly assigned patients to one of the 3 treatment groups and followed them for 2-4 years to track any recurrences of depression. (Prien et al., Archives of General Psychiatry, 1984).VariablesHospt: Which hospital the patient was from: Labeled 1, 2, 3, 5 or 6Treat: 0=Lithium; 1=Imipramine; 2=PlaceboOutcome: 0=Success 1=Failure (recurrence of depression)Time: Number of weeks until a recurrence (if outcome=1) or until study ended (if outcome=0)AcuteT: How long the patient was depressed before the start of the current study, measured in daysAge: Age in yearsGender: 1=Female 2=MaleDataIf you have not done so already, download the depression datafile (as always, if a login box opens, just close it and then download the file). Then upload the file in StatCrunch. PromptWe will analyze the data to answer the second research question: Which of the drugs (if either) delayed the recurrence of depression longer relative to the placebo? In the previous lab-preparation activity, we identified Treat as the explanatory variable and Time as the response variable. We also determined that we will analyze the data using side-by-side boxplots and descriptive statistics (i.e. 5-number summaries since the graphs are boxplots).Make graphs and tables.Use StatCrunch to produce side-by-side boxplots. (directions) Embed your graphs with your initial post. Use StatCrunch to produce the descriptive statistics (a table containing the 5-number summaries for each comparison group). (directions) Copy and paste the StatCrunch output table into your initial post. Analyze the data: Compare the distributions for the treatment groups as demonstrated in Unit 2. For example, compare medians and intervals of typical values. Describe the shape and any outliers. Be sure to write your comparisons so the reader can understand the context of the numbers. For example, don't just say the median is 30; instead, say something like this: on average patients taking the placebo relapsed in 30 days (Q2=30 days).Draw a conclusion: What can we conclude from your analysis? Did one drug successfully delay a relapse of depression better than the others? What evidence supports your conclusion?Summarize your conclusions in response to both research questions: In this lab you compared three treatments (two drugs and the placebo) using two different variables. In Part 1 you compared whether or not a relapse into depression occurred for each of the two drugs and the placebo. In Part 2 you compared the length of time until the next relapse for the two drugs and the placebo. What can you conclude in light of both analyses? Is one treatment better than the other? How does the data support your conclusion?
Sections 4.2-4.5 online algebra assignment
Online work for McGraw Hill Connect. Involves solving, calculating and plotting trig, log, and exponential functions.
Sections 4.2-4.5 online algebra assignment
Online work for McGraw Hill Connect. Involves solving, calculating and plotting trig, log, and exponential functions.
Discussion: Data Assumptions and Parametric Statistical Tests
Discussion: Data Assumptions and Parametric Statistical Tests The accuracy of parametric statistical tests is largely bas ...
Discussion: Data Assumptions and Parametric Statistical Tests
Discussion: Data Assumptions and Parametric Statistical Tests The accuracy of parametric statistical tests is largely based on the data distribution of the collected data. Parametric tests are based on distribution assumptions, such as normality, linearity, equality of variances, etc. These assumptions and others vary based on the statistical test; therefore, it is critical for quantitative researchers to evaluate the assumptions pertaining to their statistical analyses and identify actions taken if assumptions are grossly violated. To prepare for this Discussion, review the Lumley et al. (2002) article, as well as Lessons 19–21 and 24 in the Green and Salkind (2017) text. Use the Walden Library databases to identify a research example using your doctoral research proposal and consider the role and importance of the assumptions underlying each parametric test. Post a comparison of one-sample, paired-samples, and independent-samples t-tests within the context of quantitative doctoral business research. In your comparison, do the following: Describe the research example related to your doctoral research proposal.Describe a hypothetical example appropriate for each t-test, ensuring that the variables are appropriately identified.Analyze the assumptions associated with the independent-samples t-tests and the implications when assumptions are violated.Explain options researchers have when assumptions are violated. Be sure to support your work with a minimum of two specific citations from this week’s Learning Resources and at least one additional scholarly source. Resources Green, S. B., & Salkind, N. J. (2017). Using SPSS for Windows and Macintosh: Analyzing and understanding data (8th ed.). Upper Saddle River, NJ: Pearson. Unit 5, “Creating Variables and Computing Descriptive Statistics”Lesson 19, “Creating Variables” (pp. 88–98)Lesson 20, “Univariate Descriptive Statistics for Qualitative Variables” (pp. 99–103)Lesson 21, “Univariate Descriptive Statistics for Quantitative Variables” (pp. 104–115)Unit 6, “t Test Procedures”Lesson 24, “Independent-Samples t Test” (pp. 125–129) Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.). Essex, England: Pearson Education Unlimited. Chapter 12, “Analysing Quantitative Data” Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets, Annual Review of Public Health, 23(1), 151–170. doi:10.1146.annurev.publheath.23.100901.140546 Note: You will access this article from the Walden Library databases. Paul, H., & Garg, P. (2014). Organizational commitment of frontline sales professionals in India: Role of resilience. International Journal of Business Insights and Information, 7(2), 12–18. Retrieved from http://www.ijbit.org/home Note: This article contains several statistical analyses, to include the independent-samples t-test. You will access this article from the Walden Library databases.
BUS 308 Ashford University Air Force Training Program Case Study
Review Case Problem 1: Air Force Training Program Chapter 11.Step 2: Do
Step 2:
Run the F-Test Two-Sample for Varian ...
BUS 308 Ashford University Air Force Training Program Case Study
Review Case Problem 1: Air Force Training Program Chapter 11.Step 2: Do
Step 2:
Run the F-Test Two-Sample for Variances for the Data File Training (Chapter 11) using the video How to Add Excel's Data Analysis ToolPak.
In a managerial report,
Use appropriate descriptive statistics to summarize the training time data for each method. What similarities or differences do you observe from the sample data?
Conduct a hypothesis test on the difference between the population means for the two methods. Discuss your findings.
Compute the standard deviation and variance for each training method. Conduct a hypothesis test about the equality of population variances for the two training methods. Discuss your findings.
Explain what conclusion you can reach about any differences between the two methods. What is your recommendation? Explain.
Suggest other data or testing that might be desirable before making a final decision on the training program to be used in the future.
Step 3: Discuss:
What did you find in your analysis of the data? Were there any surprising results? What recommendations would you make based on your findings? Include details from your managerial report to support your recommendations.
Nonparametric Tests Research
Nonparametric Tests: Analysis, description and applying techniques.For this assignment, you will critically evaluate, comp ...
Nonparametric Tests Research
Nonparametric Tests: Analysis, description and applying techniques.For this assignment, you will critically evaluate, compare and contrast parametric and nonparametric tests, identify multiple applications where nonparametric tests approaches are appropriate and identify the appropriate nonparametric hypothesis testing procedures base on type of outcome variable and number of samples. You are invigorated to choose a specific Nonparametric test as a reference to do this assignment. The paper will be 4-5 pages long. Each paper must be typewritten with 12-point font and double-spaced with standard margins. Follow APA style 7th edition format when referring to the selected articles and include a reference page. EACH PAPER SHOULD INCLUDE THE FOLLOWING: 1. Introduction (25%) Distribute a short-lived summary of the meaning (not a description) of the Chapter and articles you read, in your own words. 2. Nonparametric Test (50%) Compare and contrast parametric and nonparametric tests. Identify multiple applications where nonparametric tests approaches are appropriate. Identify the appropriate nonparametric hypothesis testing procedures base on type of outcome variable and number of samples Did these Chapter and articles change your thoughts about Nonparametric Test? If so, how? If not, what remained the same? 3. Conclusion (15%) Briefly recapitulate your thoughts & conclusion to your analysis of the articles and Chapter you read.Evaluation will be based on how clearly you respond to the above, in particular: a) The clarity with which you critique the articles; b) The depth, scope, and organization of your paper; and, c) Your conclusions, including a description of the impact of these articles and Chapters on any Health Care Setting.
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Grossmont College Treating Depression Lab Report
ContextClinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who ...
Grossmont College Treating Depression Lab Report
ContextClinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who have an episode of major depression will have a recurrence within 2-3 years. Being able to prevent the recurrence of depression in people who are at risk for the disease would go a long way to alleviate the pain and suffering of patients. During the 1980's the federal government, through the National Institutes of Health (NIH), sponsored a large clinical trial to evaluate two drugs for depression. There were 3 treatment groups. Patients received either Imipramine (Imip), Lithium (Li), or a Placebo (Pl). Researchers randomly assigned patients to one of the 3 treatment groups and followed them for 2-4 years to track any recurrences of depression. (Prien et al., Archives of General Psychiatry, 1984).VariablesHospt: Which hospital the patient was from: Labeled 1, 2, 3, 5 or 6Treat: 0=Lithium; 1=Imipramine; 2=PlaceboOutcome: 0=Success 1=Failure (recurrence of depression)Time: Number of weeks until a recurrence (if outcome=1) or until study ended (if outcome=0)AcuteT: How long the patient was depressed before the start of the current study, measured in daysAge: Age in yearsGender: 1=Female 2=MaleDataIf you have not done so already, download the depression datafile (as always, if a login box opens, just close it and then download the file). Then upload the file in StatCrunch. PromptWe will analyze the data to answer the second research question: Which of the drugs (if either) delayed the recurrence of depression longer relative to the placebo? In the previous lab-preparation activity, we identified Treat as the explanatory variable and Time as the response variable. We also determined that we will analyze the data using side-by-side boxplots and descriptive statistics (i.e. 5-number summaries since the graphs are boxplots).Make graphs and tables.Use StatCrunch to produce side-by-side boxplots. (directions) Embed your graphs with your initial post. Use StatCrunch to produce the descriptive statistics (a table containing the 5-number summaries for each comparison group). (directions) Copy and paste the StatCrunch output table into your initial post. Analyze the data: Compare the distributions for the treatment groups as demonstrated in Unit 2. For example, compare medians and intervals of typical values. Describe the shape and any outliers. Be sure to write your comparisons so the reader can understand the context of the numbers. For example, don't just say the median is 30; instead, say something like this: on average patients taking the placebo relapsed in 30 days (Q2=30 days).Draw a conclusion: What can we conclude from your analysis? Did one drug successfully delay a relapse of depression better than the others? What evidence supports your conclusion?Summarize your conclusions in response to both research questions: In this lab you compared three treatments (two drugs and the placebo) using two different variables. In Part 1 you compared whether or not a relapse into depression occurred for each of the two drugs and the placebo. In Part 2 you compared the length of time until the next relapse for the two drugs and the placebo. What can you conclude in light of both analyses? Is one treatment better than the other? How does the data support your conclusion?
Sections 4.2-4.5 online algebra assignment
Online work for McGraw Hill Connect. Involves solving, calculating and plotting trig, log, and exponential functions.
Sections 4.2-4.5 online algebra assignment
Online work for McGraw Hill Connect. Involves solving, calculating and plotting trig, log, and exponential functions.
Discussion: Data Assumptions and Parametric Statistical Tests
Discussion: Data Assumptions and Parametric Statistical Tests The accuracy of parametric statistical tests is largely bas ...
Discussion: Data Assumptions and Parametric Statistical Tests
Discussion: Data Assumptions and Parametric Statistical Tests The accuracy of parametric statistical tests is largely based on the data distribution of the collected data. Parametric tests are based on distribution assumptions, such as normality, linearity, equality of variances, etc. These assumptions and others vary based on the statistical test; therefore, it is critical for quantitative researchers to evaluate the assumptions pertaining to their statistical analyses and identify actions taken if assumptions are grossly violated. To prepare for this Discussion, review the Lumley et al. (2002) article, as well as Lessons 19–21 and 24 in the Green and Salkind (2017) text. Use the Walden Library databases to identify a research example using your doctoral research proposal and consider the role and importance of the assumptions underlying each parametric test. Post a comparison of one-sample, paired-samples, and independent-samples t-tests within the context of quantitative doctoral business research. In your comparison, do the following: Describe the research example related to your doctoral research proposal.Describe a hypothetical example appropriate for each t-test, ensuring that the variables are appropriately identified.Analyze the assumptions associated with the independent-samples t-tests and the implications when assumptions are violated.Explain options researchers have when assumptions are violated. Be sure to support your work with a minimum of two specific citations from this week’s Learning Resources and at least one additional scholarly source. Resources Green, S. B., & Salkind, N. J. (2017). Using SPSS for Windows and Macintosh: Analyzing and understanding data (8th ed.). Upper Saddle River, NJ: Pearson. Unit 5, “Creating Variables and Computing Descriptive Statistics”Lesson 19, “Creating Variables” (pp. 88–98)Lesson 20, “Univariate Descriptive Statistics for Qualitative Variables” (pp. 99–103)Lesson 21, “Univariate Descriptive Statistics for Quantitative Variables” (pp. 104–115)Unit 6, “t Test Procedures”Lesson 24, “Independent-Samples t Test” (pp. 125–129) Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.). Essex, England: Pearson Education Unlimited. Chapter 12, “Analysing Quantitative Data” Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets, Annual Review of Public Health, 23(1), 151–170. doi:10.1146.annurev.publheath.23.100901.140546 Note: You will access this article from the Walden Library databases. Paul, H., & Garg, P. (2014). Organizational commitment of frontline sales professionals in India: Role of resilience. International Journal of Business Insights and Information, 7(2), 12–18. Retrieved from http://www.ijbit.org/home Note: This article contains several statistical analyses, to include the independent-samples t-test. You will access this article from the Walden Library databases.
BUS 308 Ashford University Air Force Training Program Case Study
Review Case Problem 1: Air Force Training Program Chapter 11.Step 2: Do
Step 2:
Run the F-Test Two-Sample for Varian ...
BUS 308 Ashford University Air Force Training Program Case Study
Review Case Problem 1: Air Force Training Program Chapter 11.Step 2: Do
Step 2:
Run the F-Test Two-Sample for Variances for the Data File Training (Chapter 11) using the video How to Add Excel's Data Analysis ToolPak.
In a managerial report,
Use appropriate descriptive statistics to summarize the training time data for each method. What similarities or differences do you observe from the sample data?
Conduct a hypothesis test on the difference between the population means for the two methods. Discuss your findings.
Compute the standard deviation and variance for each training method. Conduct a hypothesis test about the equality of population variances for the two training methods. Discuss your findings.
Explain what conclusion you can reach about any differences between the two methods. What is your recommendation? Explain.
Suggest other data or testing that might be desirable before making a final decision on the training program to be used in the future.
Step 3: Discuss:
What did you find in your analysis of the data? Were there any surprising results? What recommendations would you make based on your findings? Include details from your managerial report to support your recommendations.
Nonparametric Tests Research
Nonparametric Tests: Analysis, description and applying techniques.For this assignment, you will critically evaluate, comp ...
Nonparametric Tests Research
Nonparametric Tests: Analysis, description and applying techniques.For this assignment, you will critically evaluate, compare and contrast parametric and nonparametric tests, identify multiple applications where nonparametric tests approaches are appropriate and identify the appropriate nonparametric hypothesis testing procedures base on type of outcome variable and number of samples. You are invigorated to choose a specific Nonparametric test as a reference to do this assignment. The paper will be 4-5 pages long. Each paper must be typewritten with 12-point font and double-spaced with standard margins. Follow APA style 7th edition format when referring to the selected articles and include a reference page. EACH PAPER SHOULD INCLUDE THE FOLLOWING: 1. Introduction (25%) Distribute a short-lived summary of the meaning (not a description) of the Chapter and articles you read, in your own words. 2. Nonparametric Test (50%) Compare and contrast parametric and nonparametric tests. Identify multiple applications where nonparametric tests approaches are appropriate. Identify the appropriate nonparametric hypothesis testing procedures base on type of outcome variable and number of samples Did these Chapter and articles change your thoughts about Nonparametric Test? If so, how? If not, what remained the same? 3. Conclusion (15%) Briefly recapitulate your thoughts & conclusion to your analysis of the articles and Chapter you read.Evaluation will be based on how clearly you respond to the above, in particular: a) The clarity with which you critique the articles; b) The depth, scope, and organization of your paper; and, c) Your conclusions, including a description of the impact of these articles and Chapters on any Health Care Setting.
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