verify identity , fill in the blank! both 5 and 6!
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7.02 systems of equations
QUESTION 1Is the following system consistent or inconsistent?8x + y + 4 = 0y = -8x - 4 consistentinconsistent2 points ...
7.02 systems of equations
QUESTION 1Is the following system consistent or inconsistent?8x + y + 4 = 0y = -8x - 4 consistentinconsistent2 points QUESTION 2Is the following system consistent or inconsistent?7x + 6 = -2y-14x -4y + 2 = 0 consistentinconsistent2 points QUESTION 3Is the following system consistent or inconsistent?-2x - 2y = 610x + 10y = -30 consistentinconsistent2 points QUESTION 4Is the following system consistent or inconsistent?2y = x - 7-2x - 6y = -14 consistentinconsistent2 points QUESTION 5Is the following system consistent or inconsistent?y = 2x + 5-2x + y = -2 consistentinconsistent
University of Mary Washington The Arithmetic Mean and Data Manipulation Questions
Please download the required CSV data file here:https://drive.google.com/file/d/1R63J3Z2LtnIsdnhqdBNt-4lxEzTOxpmI/view?usp ...
University of Mary Washington The Arithmetic Mean and Data Manipulation Questions
Please download the required CSV data file here:https://drive.google.com/file/d/1R63J3Z2LtnIsdnhqdBNt-4lxEzTOxpmI/view?usp=sharing (Links to an external site.)This assignment will focus on using some of the techniques we have seen over the past three weeks for manipulating data in R. For this assignment we will be working with a dataset consisting of demographics together with weekly religious service attendance for 5000 individuals. Most of the variable names are self-explanatory, but the “Weekly Attendance” column shows a value of “1” for those who report attending religious services weekly and “0” for those who don’t. You will read this file into R and answer a few questions that will require you to apply data transforms for finding the answer.Important Formatting Instructions: Please round all answers to the nearest whole number. There should be no decimals or commas, only integers. Here some examples of what to do (good examples) and what NOT to do (bad examples):Good Examples (enter examples like these): 5, 2, 22, 11, 128, 228000Bad Examples (don’t do this!): 5.31, 2.0, 22.000, 11.88231, 128.0, 228,000INTEGRITY STATEMENTYou must work on this entirely alone without consulting with any other student. You may certainly use your notes and any class materials as well as outside R resources, but you may NOT ask others for help or seek the answers on the Internet or from any other source. This must be entirely your own work. Additionally, you may NOT discuss these answers or in any way make them public. The Honor Code is in full effect!HOW TO SUBMITUse R to answer the questions below. It is recommended to work the answers out first, then log into the website below to enter your answers. You will need to enter them all in one sitting. Please MAKE SURE to follow the formatting instructions so you don’t needlessly lose points.Once you are ready, you may log on with your NetID and enter your answers HERE (Links to an external site.)QUESTIONS1. What is the mean number of years of education?2. What is the median age?3. How many males are in this dataset?4. How many females are in this dataset?5. How many married females are in this dataset?6. How many unmarried males attend services weekly in this dataset?7. What is the median income of those who attend services weekly?8. What is the median income of those who DO NOT attend services weekly?For the next 3 questions, add a new column called “HigherEd” that provides a label based on years of education according to the following rules:Educ < 12: “None”12 <= Educ < 16: “HighSchool”16 <= Educ < 17: “College”17 <= Educ < 19: “Masters”Educ >= 19: “Doctorate”9. What is the median income for those with HigherEd = “College”?10. How many females with HigherEd = “Masters” in this data?11. How many individuals who hold a doctorate attend weekly religious services?
Confidence Intervals
Part I Healthcare administration leaders are asked to make evidence-based decisions on a daily basis. Sometimes, these d ...
Confidence Intervals
Part I Healthcare administration leaders are asked to make evidence-based decisions on a daily basis. Sometimes, these decisions involve high levels of uncertainty, as you have examined previously. Other times, there are data upon which evidence-based analysis might be conducted. This week, you will be asked to think of scenarios where building and interpreting confidence intervals (CIs) would be useful for healthcare administration leaders to conduct a two-sided hypothesis test using fictitious data. For example, Ralph is a healthcare administration leader who is interested in evaluating whether the mean patient satisfaction scores for his hospital are significantly different from 87 at the .05 level. He gathers a sample of 100 observations and finds that the sample mean is 83 and the standard deviation is 5. Using a t-distribution, he generates a two-sided confidence interval (CI) of 83 +/- 1.984217 *5/sqrt(100). The 95% CI is then (82.007, 83.992). If repeated intervals were conducted identically, 95% should contain the population mean. The two-sided hypothesis test can be formulated and tested just with this interval. Ho: Mu = 87, Ha: Mu<>87. Alpha = .05. If he assumes normality and that population standard deviation is unknown, he selects the t-distribution. After constructing a 95% CI, he notes that 87 is not in the interval, so he can reject the null hypothesis that the mean satisfaction rates are 87. In fact, he has an evidence-based analysis to suggest that the mean satisfaction rates are not equal to (less than) 87. Review the resources and consider how a CI might be used to support hypothesis testing in a healthcare scenario. Describe of a healthcare scenario where a CI might be used, and then complete a fictitious two-sided hypothesis test using a CI and fictitious data. PART II Confidence intervals (CIs) and hypothesis tests assist healthcare administration leaders in executing decision making for a wide variety of conditions or experiences in a health services organization. Interpreting the significance of the information provided in hypothesis testing can ensure that healthcare administration leaders execute the best and appropriate measures possible to ensure effective healthcare delivery. For this Assignment, review the resources for this week. Then, review your course text, and complete Problem 66 on page 455 and Case Study 9.4 on pages 459–460. Consider the type of analysis tools you may use to best fit the case study provided. The Assignment: (3–5 pages) Complete Problem 66 on page 455 of your course text using SPSS.Complete Case Study 9.4, "Removing Vioxx From the Market," on pages 459–460 of your course text.This case study requires only calculations and analysis of them, so you may complete this case study using any tool you choose. ATTACH ALL FILES Albright, S. C., & Winston, W. L. (2015). Business analytics: Data analysis and decision making (5th ed.). Stamford, CT: Cengage Learning. Chapter 8, "Confidence Interval Estimation" (pp. 335–400) Chapter 9, "Hypothesis Testing" (pp. 401–459) Fulton, L. V., Ivanitskaya, L. V., Bastian, N. D., Erofeev, D. A., & Mendez, F. A. (2013). Frequent deadlines: Evaluating the effect of learner control on healthcare executives' performance in online learning. Learning and Instruction, 23, 24–32.
9 pages
Statistical Exercises
A maker of energy drinks is considering abandoning can containers and going exclusively to bottles because the sales manag ...
Statistical Exercises
A maker of energy drinks is considering abandoning can containers and going exclusively to bottles because the sales manager believes customers prefer ...
SNHU Module 3 Discussion Model of Transistors Calculations Exercise
Solve the problem below. Copy the description of your forecast in the box below and include that as part of your initial D ...
SNHU Module 3 Discussion Model of Transistors Calculations Exercise
Solve the problem below. Copy the description of your forecast in the box below and include that as part of your initial Discussion post in Brightspace. Using "copy" from here in Mobius and "paste" into Brightspace should work.Hint: The chart is taken from https://ourworldindata.org/technological-progress.From the chart, estimate (roughly) the number of transistors per IC in 2018. Using your estimate and Moore's Law, what would you predict the number of transistors per IC to be in 2040?In some applications, the variable being studied increases so quickly ("exponentially") that a regular graph isn't informative. There, a regular graph would show data close to 0 and then a sudden spike at the very end. Instead, for these applications, we often use logarithmic scales. We replace the y-axis tick marks of 1, 2, 3, 4, etc. with y-axis tick marks of 101 = 10, 102 = 100, 103 = 1000, 104 = 10000, etc. In other words, the logarithms of the new tick marks are equally spaced.Technology is one area where progress is extraordinarily rapid. Moore's Law states that the progress of technology (measured in different ways) doubles every 2 years. A common example counts the number of transitors per integrated circuit. A regular y-axis scale is appropriate when a trend is linear, i.e. 100 transistors, 200 transistors, 300 transistors, 400 transistors, etc. However, technology actually increased at a much quicker pace such as 100 transistors,.1,000 transistors, 10,000 transistors, 100,000 transistors, etc.The following is a plot of the number of transistors per integrated circuit over the period 1971 - 2008 taken from https://ourworldindata.org/technological-progress (that site contains a lot of data, not just for technology). At first, this graph seems to show a steady progression until you look carefully at the y-axis ... it's not linear. From the graph, it seems that from 1971 to 1981 the number of transistors went from about 1,000 to 40,000. Moore's Law predicts that in 10 years, it would double 5 times, i.e. go from 1,000 to 32,000, and the actual values (using very rough estimates) seem to support this.The following is the same plot but with the common logarithm of the y-axis shown. You can see that log(y) goes up uniformly.(the map is attach)Questions to be answered in your Brightspace Discussion:Part a: The number of transistors per IC in 1972 seems to be about 4,000 (a rough estimate by eye). Using this estimate and Moore's Law, what would you predict the number of transistors per IC to be 20 years later, in 1992?Prediction = Part b: From the chart, estimate (roughly) the number of transistors per IC in 2018. Using your estimate and Moore's Law, what would you predict the number of transistors per IC to be in 2040?Part c: Do you think that your prediction in Part b is believable? Why or why not?
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Most Popular Content
7.02 systems of equations
QUESTION 1Is the following system consistent or inconsistent?8x + y + 4 = 0y = -8x - 4 consistentinconsistent2 points ...
7.02 systems of equations
QUESTION 1Is the following system consistent or inconsistent?8x + y + 4 = 0y = -8x - 4 consistentinconsistent2 points QUESTION 2Is the following system consistent or inconsistent?7x + 6 = -2y-14x -4y + 2 = 0 consistentinconsistent2 points QUESTION 3Is the following system consistent or inconsistent?-2x - 2y = 610x + 10y = -30 consistentinconsistent2 points QUESTION 4Is the following system consistent or inconsistent?2y = x - 7-2x - 6y = -14 consistentinconsistent2 points QUESTION 5Is the following system consistent or inconsistent?y = 2x + 5-2x + y = -2 consistentinconsistent
University of Mary Washington The Arithmetic Mean and Data Manipulation Questions
Please download the required CSV data file here:https://drive.google.com/file/d/1R63J3Z2LtnIsdnhqdBNt-4lxEzTOxpmI/view?usp ...
University of Mary Washington The Arithmetic Mean and Data Manipulation Questions
Please download the required CSV data file here:https://drive.google.com/file/d/1R63J3Z2LtnIsdnhqdBNt-4lxEzTOxpmI/view?usp=sharing (Links to an external site.)This assignment will focus on using some of the techniques we have seen over the past three weeks for manipulating data in R. For this assignment we will be working with a dataset consisting of demographics together with weekly religious service attendance for 5000 individuals. Most of the variable names are self-explanatory, but the “Weekly Attendance” column shows a value of “1” for those who report attending religious services weekly and “0” for those who don’t. You will read this file into R and answer a few questions that will require you to apply data transforms for finding the answer.Important Formatting Instructions: Please round all answers to the nearest whole number. There should be no decimals or commas, only integers. Here some examples of what to do (good examples) and what NOT to do (bad examples):Good Examples (enter examples like these): 5, 2, 22, 11, 128, 228000Bad Examples (don’t do this!): 5.31, 2.0, 22.000, 11.88231, 128.0, 228,000INTEGRITY STATEMENTYou must work on this entirely alone without consulting with any other student. You may certainly use your notes and any class materials as well as outside R resources, but you may NOT ask others for help or seek the answers on the Internet or from any other source. This must be entirely your own work. Additionally, you may NOT discuss these answers or in any way make them public. The Honor Code is in full effect!HOW TO SUBMITUse R to answer the questions below. It is recommended to work the answers out first, then log into the website below to enter your answers. You will need to enter them all in one sitting. Please MAKE SURE to follow the formatting instructions so you don’t needlessly lose points.Once you are ready, you may log on with your NetID and enter your answers HERE (Links to an external site.)QUESTIONS1. What is the mean number of years of education?2. What is the median age?3. How many males are in this dataset?4. How many females are in this dataset?5. How many married females are in this dataset?6. How many unmarried males attend services weekly in this dataset?7. What is the median income of those who attend services weekly?8. What is the median income of those who DO NOT attend services weekly?For the next 3 questions, add a new column called “HigherEd” that provides a label based on years of education according to the following rules:Educ < 12: “None”12 <= Educ < 16: “HighSchool”16 <= Educ < 17: “College”17 <= Educ < 19: “Masters”Educ >= 19: “Doctorate”9. What is the median income for those with HigherEd = “College”?10. How many females with HigherEd = “Masters” in this data?11. How many individuals who hold a doctorate attend weekly religious services?
Confidence Intervals
Part I Healthcare administration leaders are asked to make evidence-based decisions on a daily basis. Sometimes, these d ...
Confidence Intervals
Part I Healthcare administration leaders are asked to make evidence-based decisions on a daily basis. Sometimes, these decisions involve high levels of uncertainty, as you have examined previously. Other times, there are data upon which evidence-based analysis might be conducted. This week, you will be asked to think of scenarios where building and interpreting confidence intervals (CIs) would be useful for healthcare administration leaders to conduct a two-sided hypothesis test using fictitious data. For example, Ralph is a healthcare administration leader who is interested in evaluating whether the mean patient satisfaction scores for his hospital are significantly different from 87 at the .05 level. He gathers a sample of 100 observations and finds that the sample mean is 83 and the standard deviation is 5. Using a t-distribution, he generates a two-sided confidence interval (CI) of 83 +/- 1.984217 *5/sqrt(100). The 95% CI is then (82.007, 83.992). If repeated intervals were conducted identically, 95% should contain the population mean. The two-sided hypothesis test can be formulated and tested just with this interval. Ho: Mu = 87, Ha: Mu<>87. Alpha = .05. If he assumes normality and that population standard deviation is unknown, he selects the t-distribution. After constructing a 95% CI, he notes that 87 is not in the interval, so he can reject the null hypothesis that the mean satisfaction rates are 87. In fact, he has an evidence-based analysis to suggest that the mean satisfaction rates are not equal to (less than) 87. Review the resources and consider how a CI might be used to support hypothesis testing in a healthcare scenario. Describe of a healthcare scenario where a CI might be used, and then complete a fictitious two-sided hypothesis test using a CI and fictitious data. PART II Confidence intervals (CIs) and hypothesis tests assist healthcare administration leaders in executing decision making for a wide variety of conditions or experiences in a health services organization. Interpreting the significance of the information provided in hypothesis testing can ensure that healthcare administration leaders execute the best and appropriate measures possible to ensure effective healthcare delivery. For this Assignment, review the resources for this week. Then, review your course text, and complete Problem 66 on page 455 and Case Study 9.4 on pages 459–460. Consider the type of analysis tools you may use to best fit the case study provided. The Assignment: (3–5 pages) Complete Problem 66 on page 455 of your course text using SPSS.Complete Case Study 9.4, "Removing Vioxx From the Market," on pages 459–460 of your course text.This case study requires only calculations and analysis of them, so you may complete this case study using any tool you choose. ATTACH ALL FILES Albright, S. C., & Winston, W. L. (2015). Business analytics: Data analysis and decision making (5th ed.). Stamford, CT: Cengage Learning. Chapter 8, "Confidence Interval Estimation" (pp. 335–400) Chapter 9, "Hypothesis Testing" (pp. 401–459) Fulton, L. V., Ivanitskaya, L. V., Bastian, N. D., Erofeev, D. A., & Mendez, F. A. (2013). Frequent deadlines: Evaluating the effect of learner control on healthcare executives' performance in online learning. Learning and Instruction, 23, 24–32.
9 pages
Statistical Exercises
A maker of energy drinks is considering abandoning can containers and going exclusively to bottles because the sales manag ...
Statistical Exercises
A maker of energy drinks is considering abandoning can containers and going exclusively to bottles because the sales manager believes customers prefer ...
SNHU Module 3 Discussion Model of Transistors Calculations Exercise
Solve the problem below. Copy the description of your forecast in the box below and include that as part of your initial D ...
SNHU Module 3 Discussion Model of Transistors Calculations Exercise
Solve the problem below. Copy the description of your forecast in the box below and include that as part of your initial Discussion post in Brightspace. Using "copy" from here in Mobius and "paste" into Brightspace should work.Hint: The chart is taken from https://ourworldindata.org/technological-progress.From the chart, estimate (roughly) the number of transistors per IC in 2018. Using your estimate and Moore's Law, what would you predict the number of transistors per IC to be in 2040?In some applications, the variable being studied increases so quickly ("exponentially") that a regular graph isn't informative. There, a regular graph would show data close to 0 and then a sudden spike at the very end. Instead, for these applications, we often use logarithmic scales. We replace the y-axis tick marks of 1, 2, 3, 4, etc. with y-axis tick marks of 101 = 10, 102 = 100, 103 = 1000, 104 = 10000, etc. In other words, the logarithms of the new tick marks are equally spaced.Technology is one area where progress is extraordinarily rapid. Moore's Law states that the progress of technology (measured in different ways) doubles every 2 years. A common example counts the number of transitors per integrated circuit. A regular y-axis scale is appropriate when a trend is linear, i.e. 100 transistors, 200 transistors, 300 transistors, 400 transistors, etc. However, technology actually increased at a much quicker pace such as 100 transistors,.1,000 transistors, 10,000 transistors, 100,000 transistors, etc.The following is a plot of the number of transistors per integrated circuit over the period 1971 - 2008 taken from https://ourworldindata.org/technological-progress (that site contains a lot of data, not just for technology). At first, this graph seems to show a steady progression until you look carefully at the y-axis ... it's not linear. From the graph, it seems that from 1971 to 1981 the number of transistors went from about 1,000 to 40,000. Moore's Law predicts that in 10 years, it would double 5 times, i.e. go from 1,000 to 32,000, and the actual values (using very rough estimates) seem to support this.The following is the same plot but with the common logarithm of the y-axis shown. You can see that log(y) goes up uniformly.(the map is attach)Questions to be answered in your Brightspace Discussion:Part a: The number of transistors per IC in 1972 seems to be about 4,000 (a rough estimate by eye). Using this estimate and Moore's Law, what would you predict the number of transistors per IC to be 20 years later, in 1992?Prediction = Part b: From the chart, estimate (roughly) the number of transistors per IC in 2018. Using your estimate and Moore's Law, what would you predict the number of transistors per IC to be in 2040?Part c: Do you think that your prediction in Part b is believable? Why or why not?
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