Brief reflection to answer the questions below. Write it as if you were writing to a prospective employer. Be as specific as you can: when discussing strengths and weaknesses.

# Category: Statistics homework help

Upload the Excel data file Dataset BUS 770a Business Research Methods xlsx in a quantitative research tool. The data file is located in GAP under Week Seven. Complete the following tasks and copy your results in a word document and submit your report.

Create frequency table for the variable Distance From School

Use the Recode into Same Variables command to assign ascending numerical values to distances. (Note: highest numerical value should be assigned to Other OutofState)

Create value labels corresponding to numerical values, change the Type of the variable to Numeric and change the Measure to Ordinal. Rerun the frequencies command

Create standardized values for the HSGPA variable. Report key descriptive statistics for this variable. Make sure your report table includes measures of Kurtosis and Skewness.

Create a histogram for HSGPA. Based on your visual inspection and measures of Kurtosis and Skewness, what is your conclusion regarding the shape of the distribution?

Create a cross tabulation of Retained vs Received Financial Aid. Of the students who had received financial aid, what percentage were retained?

Test the following hypothesis at 95% confidence level:

H0: Overall GPA of students in the sample is equal to 3.0755

HA: Overall GPA of students in the sample is different than 3.0755

Test the following hypothesis at 95% confidence level:

H0: Overall GPA of students in the sample is greater than 3.0755

HA: Overall GPA of students in the sample is less than or equal to 3.0755

Conduct a test on the number of Infirmary Visits using the following hypothesis:

H0: The likelihood of infirmary visits is equal for students who visit the infirmary between one and six times.

HA: The likelihood of infirmary visits is not equal for students who visit the infirmary between one and six times.

Write a paragraph to explain your results. State you guess and the computed probability of success from Project #2. Show the formulas you used to compute the 90%, 95% and the 99% confidence intervals, and state each confidence interval. Explain if any of these intervals contain your original guess. Do you consider your original guess accurate or inaccurate?

In this week’s readings, you learned about the major contributors to the development of total quality management (TQM). Some of the methods that each developed are more suited to one application versus another. Select three contributors from the list shown here whose TQM methods could be readily applied to the daily business and medical operations of a medical center or hospital.

Philip Crosby.

Dr. W. Edwards Deming.

Armand Feigenbaum.

Dr. Joseph M. Juran.

Prof. Kaoru Ishikawa.

Genichi Taguchi.

Musaaki Imai.

In a Word document:

Describe two of the contributions to the development of TQM for each of the three individuals you selected.

Apply each of the contributions to a specific situation in the operations of a medical center or hospital. Include examples from both the medical and business operations to illustrate the use of TQM.

Review the tutorial in the unit study, which demonstrates t tests using two different scenarios. Then determine which type of t test to conduct. Once you have determined that, answer the following:What is the sample size?

What is the sample mean of each group?

Identify the following factors based on the table you develop in SPSS:Mean difference.

T obtained (t).

Degrees of freedom (df).

Significance (two-tailed).

Write a brief narrative explaining the implications of your findings based on the value of the test statistic:Would you retain or reject the null hypothesis?

What are limitations that must be considered in interpreting the results?

Week 5 Assignment – Case Study: Statistical Forecasting

Dr. Megan Zobb, a key researcher within the North Luna University Medical Center, has been studying a new variant of a skin disease virus that seems to be surfacing among the North Luna University population. This variant (which has been tentatively named Painful Rash or PR), leads to the formation of surface lesions on an individual’s body. These lesions are very similar to small boils or isolated shingles sores. These PR lesions are not necessarily clustered as shingles lesions are, but are isolated across the body.

Insights From Initial Interviews

Megan is initiating some efforts at a preliminary analysis. She has seen 20 initial patients and made several observations about the skin disease. She wants to analyze this initial data before structuring and recommending a more encompassing study.The signs and symptoms of this disorder usually affect multiple sections of the patient’s body. These signs and symptoms may include:

Pain, burning, numbness or tingling, but pain is always present.

Sensitivity to touch.

A red rash that begins a few days after the pain.

Fluid-filled blisters that break open and crust over.

Itching.

Some people also experience:

Fever.

Headache.

Sensitivity to light.

Fatigue.

Pain is always the first symptom of PR. For some, it can be intense. Depending on the location of the pain, it can sometimes be mistaken for a symptom of problems affecting the heart, lungs, or kidneys. Some people experience PR pain without ever developing the rash. The degree of pain that the individual experiences is seemingly proportional to the number of lesions.Dr. Zobb is extremely concerned that this new variant is especially challenging to the younger population, who are active and like to be outdoors. She has asked you as an analyst and statistician for some assistance in analyzing her initial data. She is not a biostatistician, so she requests that you explain the process you use and your interpretation of the results for each task.

Initial Data Analysis

Dr. Zobb has accumulated some data on an initial set of 20 patients across multiple age groups. She believes that the data suggests younger individuals are affected more than others. She wants you to complete the tasks shown here based on the data below.For each of the following, provide a detailed explanation of the process you used along with your interpretation of the results. Submit the response in a Word document and attach your Excel spreadsheet to show your calculations (where applicable). Be sure to number each response (e.g., 1.a, 1.b,…).

Develop an equation to model the data using a regression analysis approach and explain your calculation process in Excel.

Calculate the r-square statistic using Excel. Interpret the meaning of the r-square statistic in this case.

Determine three conclusions that address the initial observations and are supported by the regression analysis.

CHART IS LISTED AS CHART 1

Effects of Sunlight Analysis

In her initial observations, Dr. Zobb notices that the number of lesions that appear on a patient seems to be dependent on the amount of direct sunlight exposure that the patient receives. She is uncertain at this point why this would be the case, but she is a good experimentalist and is trying to establish some observations that have statistical validity. She has taken a limited amount of data on 8 patients and wants you to complete the appropriate analysis based on the data below (be sure to show your work):

Develop an equation to model the data using a regression analysis approach and explain your calculation process, using Excel.

Megan has a small group of three additional patients that are the same age that she wants to examine for lesions. She knows the number of minutes of continuous exposure to direct sunlight that each has experienced. Predict the number of lesions that each of these patients will have based on the regression analysis that you completed in your initial data analysis:

Patient 9 – 193 minutes.

Patient 10 – 219 minutes.

Patient 11 – 84 minutes.

Determine three conclusions based on the correlation of the number of lesions to minutes of sunlight exposure, using regression analysis.

CHART IS ATTACHED

Over the Counter Medication Effectiveness Analysis

Dr. Zobb wants to test several over the counter lotions—that is, lotions available without a prescription—that can be applied directly to the lesions. She wants to determine whether there is a difference in the mean length of time it takes these three types of pain lotions to provide relief from the pain caused by these lesions. Megan is hoping that one of these lotions might be more promising than the others. Several sufferers (with roughly the same number of lesions) are randomly selected and given one of the three medications. Each sufferer records the time (in minutes) it takes the medication to begin working. The results are shown in the table below. She asks you to answer these questions (be sure to show your work).

State the null hypothesis and the alternative hypothesis for this situation.

At α = 0.01, can you conclude that the mean times are different? Assume that each population of relief times is normally distributed and that the population variances are equal. Hint: Use a one-way ANOVA to solve this problem. Be certain to show your calculations and describe the process you used to solve this problem.

Determine three conclusions on the effectiveness of the medication by addressing observations or hypotheses regarding these initial tests.

CHART IS ATTACHED

PLEASE LOOK AT POWERPOINT AND FOLLOW THOSE DIRECTIONS

WEEK 5 ASSIGNMENT – CASE STUDY: STATISTICAL FORECASTING

Week 5 Assignment – Case Study: Statistical Forecasting

Dr. Megan Zobb, a key researcher within the North Luna University Medical Center, has been studying a new variant of a skin disease virus that seems to be surfacing among the North Luna University population. This variant (which has been tentatively named Painful Rash or PR), leads to the formation of surface lesions on an individual’s body. These lesions are very similar to small boils or isolated shingles sores. These PR lesions are not necessarily clustered as shingles lesions are, but are isolated across the body.

Insights From Initial Interviews

Megan is initiating some efforts at a preliminary analysis. She has seen 20 initial patients and made several observations about the skin disease. She wants to analyze this initial data before structuring and recommending a more encompassing study.The signs and symptoms of this disorder usually affect multiple sections of the patient’s body. These signs and symptoms may include:

Pain, burning, numbness or tingling, but pain is always present.

Sensitivity to touch.

A red rash that begins a few days after the pain.

Fluid-filled blisters that break open and crust over.

Itching.

Some people also experience:

Fever.

Headache.

Sensitivity to light.

Fatigue.

Pain is always the first symptom of PR. For some, it can be intense. Depending on the location of the pain, it can sometimes be mistaken for a symptom of problems affecting the heart, lungs, or kidneys. Some people experience PR pain without ever developing the rash. The degree of pain that the individual experiences is seemingly proportional to the number of lesions.Dr. Zobb is extremely concerned that this new variant is especially challenging to the younger population, who are active and like to be outdoors. She has asked you as an analyst and statistician for some assistance in analyzing her initial data. She is not a biostatistician, so she requests that you explain the process you use and your interpretation of the results for each task.

Initial Data Analysis

Dr. Zobb has accumulated some data on an initial set of 20 patients across multiple age groups. She believes that the data suggests younger individuals are affected more than others. She wants you to complete the tasks shown here based on the data below.For each of the following, provide a detailed explanation of the process you used along with your interpretation of the results. Submit the response in a Word document and attach your Excel spreadsheet to show your calculations (where applicable). Be sure to number each response (e.g., 1.a, 1.b,…).

Develop an equation to model the data using a regression analysis approach and explain your calculation process in Excel.

Calculate the r-square statistic using Excel. Interpret the meaning of the r-square statistic in this case.

Determine three conclusions that address the initial observations and are supported by the regression analysis.

Regression Analysis Initial DataPatient NumberAge of PatientNumber of Lesions124162637345124172452120672473213836169262110471011311512231813518142422152618162519173112181929191825202117

Effects of Sunlight Analysis

In her initial observations, Dr. Zobb notices that the number of lesions that appear on a patient seems to be dependent on the amount of direct sunlight exposure that the patient receives. She is uncertain at this point why this would be the case, but she is a good experimentalist and is trying to establish some observations that have statistical validity. She has taken a limited amount of data on 8 patients and wants you to complete the appropriate analysis based on the data below (be sure to show your work):

Develop an equation to model the data using a regression analysis approach and explain your calculation process, using Excel.

Megan has a small group of three additional patients that are the same age that she wants to examine for lesions. She knows the number of minutes of continuous exposure to direct sunlight that each has experienced. Predict the number of lesions that each of these patients will have based on the regression analysis that you completed in your initial data analysis:

Patient 9 – 193 minutes.

Patient 10 – 219 minutes.

Patient 11 – 84 minutes.

Determine three conclusions based on the correlation of the number of lesions to minutes of sunlight exposure, using regression analysis.

Sunlight Exposure Regression DataPatient NumberTime of Continuous Exposure to Direct Sunlight

(Minutes)Number of Lesions122524218416322020424026518014618416718620821522

Over the Counter Medication Effectiveness Analysis

Dr. Zobb wants to test several over the counter lotions—that is, lotions available without a prescription—that can be applied directly to the lesions. She wants to determine whether there is a difference in the mean length of time it takes these three types of pain lotions to provide relief from the pain caused by these lesions. Megan is hoping that one of these lotions might be more promising than the others. Several sufferers (with roughly the same number of lesions) are randomly selected and given one of the three medications. Each sufferer records the time (in minutes) it takes the medication to begin working. The results are shown in the table below. She asks you to answer these questions (be sure to show your work).

State the null hypothesis and the alternative hypothesis for this situation.

At α = 0.01, can you conclude that the mean times are different? Assume that each population of relief times is normally distributed and that the population variances are equal. Hint: Use a one-way ANOVA to solve this problem. Be certain to show your calculations and describe the process you used to solve this problem.

Determine three conclusions on the effectiveness of the medication by addressing observations or hypotheses regarding these initial tests.

Effectiveness of Over the Counter MedicationsMedication 1 (Minutes)Medication 2 (Minutes)Medication 3 (Minutes)121614151417172120121515 19

Summary of Data Analysis

Now that you have all of your data analysis:

Provide a three-paragraph summary of the findings you learned through the analysis.

Provide three data-driven suggestions for further exploration.

This course requires the use of Strayer Writing Standards. For assistance and information, please refer to the Strayer Writing Standards link in the left-hand menu of your course. Check with your professor for any additional instructions.The specific course learning outcome associated with this assignment is:

Recommend a course of action utilizing quantitative methods for health services including biostatistics, forecasting, and the modeling of predictive functions.

Please use this as a guide that I attached

For this discussion:

Explain the importance of the correlation coefficient in a multiple regression model.

Support your reasoning with an example.

Be sure to respond to at least one of your classmates’ posts. Cite any resources used.