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Descriptive Statistics and Interpretation Example QNT/561 Version 8 1 University of Phoenix Material Descriptive Statistics and Interpretation Example Interpretation Phrases Central Tendency: Mean = average of a set of data Median = half or equal number of data is above and half or equal number of data is below. It is a midpoint in an ordered (sorted) set of data, a physical location Mode = most frequent value in a set of data Dispersion: Standard deviation = variation Interquartile range (IQR) = the middle 50% of the data Range = the difference between the largest and smallest value of the data Confidence Interval: (data must be normal) There is 95% confidence that the population average is between _____ and ____ units. Normal or significantly skewed data: MegaStat: Descriptive statistics Normal curve goodness of fit p-value · Normal, p-value > .05 · Significantly Skewed, p-value < .05 Histogram: Eyeball the histogram. · Normal data will have a symmetrical or slightly skewed shape. · Significantly Skewed shape will have extreme skewness Use phrase combinations: Normally distributed: Mean and Standard Deviation, Not normally distributed: Median and IQR Descriptive Statistics Body Weight (Lbs.) Central Tendency: Mean = 149 Lbs. Dispersion: Standard deviation = 30 Lbs. Count: 100 Min/Max: 99 pounds and 234 Lbs. Confidence Interval: 144 to 155 Lbs. See the histogram in Appendix A, and descriptive statistics in Appendix B. Age Distribution is not normally distributed Central Tendency: Median = 36 years Dispersion: Interquartile Range = 20.5 years / 2 = ± 10 years Count: 100 Min/Max: 18 years and 74 years Confidence Interval: Not applicable (data is not normally distributed) See the histogram in Appendix A, and descriptive statistics in Appendix B. A scatter plot is in Appendix C. Education Level Thirteen percent of the subjects have no high school degree while 44% have high school degree. Forty three percent have a college or college graduate degree. See the bar chart in Appendix D. Descriptive Statistics Interpretation Interpretation Body Weight One hundred subjects were randomly selected. Their body weight was observed between 99 and 234 pounds. Their average weight was 149 pounds, with a variation of plus or minus 30 pounds. One half or more were above 149 pounds. There is 95% confidence that the population body weight average is between 144 and 155 pounds. Age The data was significantly skewed. One hundred subjects were randomly selected. Their ages were between 18 and 74 years, with a variation of plus or minus 10 years. One half or more subjects were 36 years of age or older. The middle half of the subjects’ ages fell between 27 and 47 years. The most frequent age was 36 years. APPENDIX A Body Weight and Age Histograms APPENDIX B Descriptive Statistics Body Weight and Age APPENDIX C Scatterplot Body Age versus Weight APPENDIX D Bar Chart Education Level Copyright © 2015 by University of Ph.
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Descriptive Statistics and Interpretation ExampleQNT561 Ver.docx
Descriptive Statistics and Interpretation Example
QNT/561 Version 8
7
University of Phoenix Material
Descriptive Statistics and Interpretation Example
Interpretation Phrases
Central Tendency:
Mean = average of a set of data
Median = half or equal number of data is above and half or equal number of data is below. It is a midpoint in an ordered (sorted) set of data, a physical location
Mode = most frequent value in a set of data
Dispersion:
Standard deviation = variation
Interquartile range (IQR) = the middle 50% of the data
Range = the difference between the largest and smallest value of the data
Confidence Interval: (data must be normal)
There is 95% confidence that the population average is between _____ and ____ units.
Normal or significantly skewed data:
MegaStat: Descriptive statistics Normal curve goodness of fit p-value
Normal, p-value > .05
Significantly Skewed, p-value < .05
Histogram: Eyeball the histogram.
Normal data will have a symmetrical or slightly skewed shape.
Significantly Skewed shape will have extreme skewness
Use phrase combinations: Normally distributed: Mean and Standard Deviation, Not normally distributed: Median and IQR
Descriptive Statistics
Body Weight (Lbs.)
Central Tendency:
Mean = 149 Lbs.
Dispersion:
Standard deviation = 30 Lbs.
Count:
100
Min/Max:
99 pounds and 234 Lbs.
Confidence Interval:
144 to 155 Lbs.
See the histogram in Appendix A, and descriptive statistics in Appendix B.
Age
Distribution is not normally distributed
Central Tendency:
Median = 36 years
Dispersion:
Interquartile Range = 20.5 years / 2 = ± 10 years
Count:
100
Min/Max:
18 years and 74 years
Confidence Interval:
Not applicable (data is not normally distributed)
See the histogram in Appendix A, and descriptive statistics in Appendix B. A scatter plot is in Appendix C.
Education Level
Thirteen percent of the subjects have no high school degree while 44% have high school degree. Forty three percent have a college or college graduate degree. See the bar chart in Appendix D.
Descriptive Statistics Interpretation
Interpretation
Body Weight
One hundred subjects were randomly selected. Their body weight was observed between 99 and 234 pounds. Their average weight was 149 pounds, with a variation of plus or minus 30 pounds. One half or more were above 149 pounds. There is 95% confidence that the population body weight average is between 144 and 155 pounds.
Age
The data was significantly skewed. One hundred subjects were randomly selected. Their ages were between 18 and 74 years, with a variation of plus or minus 10 years. One half or more subjects were 36 years of age or older. The middle half of the subjects’ ages fell between 27 and 47 years. The most frequent age was 36 years.
APPENDIX A
Body Weight and Age Histograms
APPENDIX B
Descriptive Statistics Body Weight and Age
APPENDIX C
Scatterplot Body Age versus Weight
APPENDIX D
Bar Chart Education Level
Copyright © 2015 by University of Ph.
Descriptive StatisticsNameQNT561DateInstructor’s Name.docx
Descriptive StatisticsNameQNT/561
Date
Instructor’s Name
SHORT TITLE OF PAPER
1
Running head: DESCRIPTIVE STATISTICS
1
Descriptive Statistics
Determine the appropriate descriptive statistics.
Note: If the data was normally distributed, use the mean and standard deviation. If the data was skewed significantly, use the median and interquartile range.
Numeric Variable Name1
Distribution: State if not normally distributed
Central Tendency:
Dispersion:
Number:
Min/Max:
Confidence Interval: (if distribution is normal)
Numeric Variable Name2 (if applicable)
Distribution: State if not normally distributed
Central Tendency:
Dispersion:
Number:
Min/Max:
Confidence Interval: (if distribution is normal)
Attribute Variable Name (if applicable)
Create a bar chart. Describe the proportions.
Descriptive Statistics Interpretation
Numeric Variable Name1
Describe the variable in laymen terms.
Numeric Variable Name2 (if applicable)
Describe the variable in laymen terms.
DESCRIPTIVE STATISTICS
2
Appendix A
Raw data used in the analysis
Fit data to one page.
Appendix B
Charts and Tables
This part of the paper will include items that are then cited in the body of the paper. Usually, large items are placed here not to distract from reading the paper.
Appendix C
Descriptive Statistics
This part of the paper will include descriptive statistics.
Create a Microsoft® Excel® spreadsheet with the two variables from your learning team's dataset.
Analyze the data with MegaStat®, StatCrunch®, Microsoft® Excel®or other statistical tool(s), including:
(a) Descriptive stats for each numeric variable
(b) Histogram for each numeric variable
(c) Bar chart for each attribute (non numeric) variable
(d) Scatter plot if the data contains two numeric variables
Determine the appropriate descriptive statistics.
(a) For normally distributed data use the mean and standard deviation.
(b) For significantly skewed data use the median and interquartile range.
Use the Individual Methodology Findings Template to complete the descriptive statistics.
Use the Descriptive Statistics and Interpretation Example to develop an interpretation of the descriptive statistics.
Format your paper consistent with APA guidelines.
Submit both the spreadsheet and the completed Individual Methodology Findings Template.
Click the Assignment Files tab to submit your assignment.
Please use the variables from this except:
The size of this population is 20 suppliers. These suppliers exclusively produce and make available for sale the material that is needed for manufacturing. In order to develop the sampling design it is important to identify the independent and dependent variables. Independent variables are variables that are manipulated or treated in a study in order to see what effect differences in them will have on those variables proposed as being dependent on them (Riyami, 2008). The independent variable in this research is the cost of the raw material. Dependent variables are variables .
Biostatistics research type of statics and examples
This document provides an introduction to statistics, including definitions of key terms. It discusses descriptive statistics, which summarize data from a sample, and inferential statistics, which make inferences about a population from a sample. It also defines populations and samples, variables and values, and distinguishes between data and information. Different types of variables are outlined including quantitative continuous, quantitative discrete, qualitative ordinal, and qualitative nominal variables. Methods for summarizing quantitative data through measures of central tendency and dispersion are also introduced.
INTRODUCTION TO HEALTHCARE RESEARCH METHODS: Correlational Studies, Case Seri...
INTRODUCTION TO HEALTHCARE RESEARCH METHODS: Correlational Studies, Case Series and Cross-Sectional Surveys
Sampling distributions
This document discusses sampling distributions and their properties. It defines key terms like population parameter, sample statistic, and sampling distribution. The central limit theorem states that as sample size increases, the sampling distribution of the mean will approach a normal distribution, regardless of the shape of the original population. This allows us to use properties of the normal distribution, like calculating confidence intervals, when making inferences about a population based on a sample. Several examples show how to apply the central limit theorem to find probabilities and determine necessary sample sizes. Practice questions at the end test understanding of these concepts.
83341 ch08 jacobsen
Correlational studies examine the relationship between exposure rates and disease rates using population-level data. They look for associations between characteristics of different populations grouped by place or time. Data on exposures and outcomes are collected from multiple populations and entered into a spreadsheet. Correlations are analyzed using scatterplots and correlation coefficients, with stronger linear relationships indicating higher correlation. While useful for generating hypotheses, these studies cannot prove causation at the individual level due to the ecological fallacy.
Running head Statistics 2Statistics Statistics Na.docx
Running head: Statistics
2
Statistics
Statistics
Name:
Course:
Instructor:
Institution:
Date of Submission:
Assignment #4: Model Diagnostics
A fundamental requirement in the classical linear regression is that the regression error term must be normally distributed with zero mean and constant variance (Greene, 2008). The normality tests results are presented below.
All the plots have values greater than the threshold probability value of 0.05 thus the null hypothesis of normality of the regression residuals could not be rejected at 5 per cent significance level. Conclusion is thus made that the regression residuals from the estimated equations followed a normal distribution. Since any linear function of normally distributed variables is considered to be normally distributed, normal distribution of the residuals had the implication that the coefficients of the estimates were also themselves normally distributed (Gujarati, 2008).
The residual plot is shown below:
From the residual plot it can be seen that all the residuals fall within the standard error bands thus confirming that the model is stable and can thus be used for forecasting.
References
Greene, W. (2008). Econometric analysis, 6th ed. . New Jersey: Pearson-Prentice Hall.
Gujarati, D. (2004). Basic econometrics 4th ed. . New York: McGraw Hill Companies.
Normal Probability Plot
2.6315789473684208 7.8947368421052602 13.15789473684211 18.421052631578942 23.684210526315791 28.947368421052641 34.21052631578948 39.473684210526301 44.73684210526315 50 55.26315789 4736857 60.526315789473699 65.789473684210563 71.052631578947384 76.315789473684163 81.578947368420984 86.842105263157904 92.105263157894726 97.368421052631547 10.7 11.3 11.8 11.9 12 12 12 12.4 12.5 12.6 13.1 13.2 13.4 13.5 13.5 14.2 14.5 14.5 14.6
Sample Percentile
MedianSchoolYears
Age Residual Plot
60 30 62 44 0 30 62 68 46 56 36 28 0 0 34 26 52 50 44 0.50878516451792 1.7144464705013149 -0.42159945482941003 0.54117037792769895 0.71299080887547295 1.269413932725179 0.26951686627728799 0.22131431339594501 -0.13472012994437299 0.22075061567252199 -1.3199768562363781 -0.18681496091028299 0.380020030213299 -1.451131014273024 -0.56052688701790399 -0.116260966970037 -0.67291294283960901 -0.49015761805784802 -0.48430774902780499
Age
Residuals
RUNNING HEADER: WEEK 3 ASSIGNMENT 4 1
WEEK 3 ASSIGNMENT 4 13
Week 3 Assignment 4
Introduction
In this project I selected six variables from the ' SampleDataSet.xlsx'. Among these six variables three of them were continuous and the reaming three were discrete variables. The continuous variables selected for this study are Age, WealthScore and MedianSchoolYears. The discrete variables selected for this study are NumberOfChildren, MailResponder and NumberOfCars.
Analysis
Age
The age is a continuous variable which takes only positive values even though we usually consider the integer part of it. The descriptive statistics summary of the age variable .
Recommended
Descriptive Statistics and Interpretation ExampleQNT561 Ver.docx
Descriptive Statistics and Interpretation Example
QNT/561 Version 8
7
University of Phoenix Material
Descriptive Statistics and Interpretation Example
Interpretation Phrases
Central Tendency:
Mean = average of a set of data
Median = half or equal number of data is above and half or equal number of data is below. It is a midpoint in an ordered (sorted) set of data, a physical location
Mode = most frequent value in a set of data
Dispersion:
Standard deviation = variation
Interquartile range (IQR) = the middle 50% of the data
Range = the difference between the largest and smallest value of the data
Confidence Interval: (data must be normal)
There is 95% confidence that the population average is between _____ and ____ units.
Normal or significantly skewed data:
MegaStat: Descriptive statistics Normal curve goodness of fit p-value
Normal, p-value > .05
Significantly Skewed, p-value < .05
Histogram: Eyeball the histogram.
Normal data will have a symmetrical or slightly skewed shape.
Significantly Skewed shape will have extreme skewness
Use phrase combinations: Normally distributed: Mean and Standard Deviation, Not normally distributed: Median and IQR
Descriptive Statistics
Body Weight (Lbs.)
Central Tendency:
Mean = 149 Lbs.
Dispersion:
Standard deviation = 30 Lbs.
Count:
100
Min/Max:
99 pounds and 234 Lbs.
Confidence Interval:
144 to 155 Lbs.
See the histogram in Appendix A, and descriptive statistics in Appendix B.
Age
Distribution is not normally distributed
Central Tendency:
Median = 36 years
Dispersion:
Interquartile Range = 20.5 years / 2 = ± 10 years
Count:
100
Min/Max:
18 years and 74 years
Confidence Interval:
Not applicable (data is not normally distributed)
See the histogram in Appendix A, and descriptive statistics in Appendix B. A scatter plot is in Appendix C.
Education Level
Thirteen percent of the subjects have no high school degree while 44% have high school degree. Forty three percent have a college or college graduate degree. See the bar chart in Appendix D.
Descriptive Statistics Interpretation
Interpretation
Body Weight
One hundred subjects were randomly selected. Their body weight was observed between 99 and 234 pounds. Their average weight was 149 pounds, with a variation of plus or minus 30 pounds. One half or more were above 149 pounds. There is 95% confidence that the population body weight average is between 144 and 155 pounds.
Age
The data was significantly skewed. One hundred subjects were randomly selected. Their ages were between 18 and 74 years, with a variation of plus or minus 10 years. One half or more subjects were 36 years of age or older. The middle half of the subjects’ ages fell between 27 and 47 years. The most frequent age was 36 years.
APPENDIX A
Body Weight and Age Histograms
APPENDIX B
Descriptive Statistics Body Weight and Age
APPENDIX C
Scatterplot Body Age versus Weight
APPENDIX D
Bar Chart Education Level
Copyright © 2015 by University of Ph.
Descriptive StatisticsNameQNT561DateInstructor’s Name.docx
Descriptive StatisticsNameQNT/561
Date
Instructor’s Name
SHORT TITLE OF PAPER
1
Running head: DESCRIPTIVE STATISTICS
1
Descriptive Statistics
Determine the appropriate descriptive statistics.
Note: If the data was normally distributed, use the mean and standard deviation. If the data was skewed significantly, use the median and interquartile range.
Numeric Variable Name1
Distribution: State if not normally distributed
Central Tendency:
Dispersion:
Number:
Min/Max:
Confidence Interval: (if distribution is normal)
Numeric Variable Name2 (if applicable)
Distribution: State if not normally distributed
Central Tendency:
Dispersion:
Number:
Min/Max:
Confidence Interval: (if distribution is normal)
Attribute Variable Name (if applicable)
Create a bar chart. Describe the proportions.
Descriptive Statistics Interpretation
Numeric Variable Name1
Describe the variable in laymen terms.
Numeric Variable Name2 (if applicable)
Describe the variable in laymen terms.
DESCRIPTIVE STATISTICS
2
Appendix A
Raw data used in the analysis
Fit data to one page.
Appendix B
Charts and Tables
This part of the paper will include items that are then cited in the body of the paper. Usually, large items are placed here not to distract from reading the paper.
Appendix C
Descriptive Statistics
This part of the paper will include descriptive statistics.
Create a Microsoft® Excel® spreadsheet with the two variables from your learning team's dataset.
Analyze the data with MegaStat®, StatCrunch®, Microsoft® Excel®or other statistical tool(s), including:
(a) Descriptive stats for each numeric variable
(b) Histogram for each numeric variable
(c) Bar chart for each attribute (non numeric) variable
(d) Scatter plot if the data contains two numeric variables
Determine the appropriate descriptive statistics.
(a) For normally distributed data use the mean and standard deviation.
(b) For significantly skewed data use the median and interquartile range.
Use the Individual Methodology Findings Template to complete the descriptive statistics.
Use the Descriptive Statistics and Interpretation Example to develop an interpretation of the descriptive statistics.
Format your paper consistent with APA guidelines.
Submit both the spreadsheet and the completed Individual Methodology Findings Template.
Click the Assignment Files tab to submit your assignment.
Please use the variables from this except:
The size of this population is 20 suppliers. These suppliers exclusively produce and make available for sale the material that is needed for manufacturing. In order to develop the sampling design it is important to identify the independent and dependent variables. Independent variables are variables that are manipulated or treated in a study in order to see what effect differences in them will have on those variables proposed as being dependent on them (Riyami, 2008). The independent variable in this research is the cost of the raw material. Dependent variables are variables .
Biostatistics research type of statics and examples
This document provides an introduction to statistics, including definitions of key terms. It discusses descriptive statistics, which summarize data from a sample, and inferential statistics, which make inferences about a population from a sample. It also defines populations and samples, variables and values, and distinguishes between data and information. Different types of variables are outlined including quantitative continuous, quantitative discrete, qualitative ordinal, and qualitative nominal variables. Methods for summarizing quantitative data through measures of central tendency and dispersion are also introduced.
INTRODUCTION TO HEALTHCARE RESEARCH METHODS: Correlational Studies, Case Seri...
INTRODUCTION TO HEALTHCARE RESEARCH METHODS: Correlational Studies, Case Series and Cross-Sectional Surveys
Sampling distributions
This document discusses sampling distributions and their properties. It defines key terms like population parameter, sample statistic, and sampling distribution. The central limit theorem states that as sample size increases, the sampling distribution of the mean will approach a normal distribution, regardless of the shape of the original population. This allows us to use properties of the normal distribution, like calculating confidence intervals, when making inferences about a population based on a sample. Several examples show how to apply the central limit theorem to find probabilities and determine necessary sample sizes. Practice questions at the end test understanding of these concepts.
83341 ch08 jacobsen
Correlational studies examine the relationship between exposure rates and disease rates using population-level data. They look for associations between characteristics of different populations grouped by place or time. Data on exposures and outcomes are collected from multiple populations and entered into a spreadsheet. Correlations are analyzed using scatterplots and correlation coefficients, with stronger linear relationships indicating higher correlation. While useful for generating hypotheses, these studies cannot prove causation at the individual level due to the ecological fallacy.
Running head Statistics 2Statistics Statistics Na.docx
Running head: Statistics
2
Statistics
Statistics
Name:
Course:
Instructor:
Institution:
Date of Submission:
Assignment #4: Model Diagnostics
A fundamental requirement in the classical linear regression is that the regression error term must be normally distributed with zero mean and constant variance (Greene, 2008). The normality tests results are presented below.
All the plots have values greater than the threshold probability value of 0.05 thus the null hypothesis of normality of the regression residuals could not be rejected at 5 per cent significance level. Conclusion is thus made that the regression residuals from the estimated equations followed a normal distribution. Since any linear function of normally distributed variables is considered to be normally distributed, normal distribution of the residuals had the implication that the coefficients of the estimates were also themselves normally distributed (Gujarati, 2008).
The residual plot is shown below:
From the residual plot it can be seen that all the residuals fall within the standard error bands thus confirming that the model is stable and can thus be used for forecasting.
References
Greene, W. (2008). Econometric analysis, 6th ed. . New Jersey: Pearson-Prentice Hall.
Gujarati, D. (2004). Basic econometrics 4th ed. . New York: McGraw Hill Companies.
Normal Probability Plot
2.6315789473684208 7.8947368421052602 13.15789473684211 18.421052631578942 23.684210526315791 28.947368421052641 34.21052631578948 39.473684210526301 44.73684210526315 50 55.26315789 4736857 60.526315789473699 65.789473684210563 71.052631578947384 76.315789473684163 81.578947368420984 86.842105263157904 92.105263157894726 97.368421052631547 10.7 11.3 11.8 11.9 12 12 12 12.4 12.5 12.6 13.1 13.2 13.4 13.5 13.5 14.2 14.5 14.5 14.6
Sample Percentile
MedianSchoolYears
Age Residual Plot
60 30 62 44 0 30 62 68 46 56 36 28 0 0 34 26 52 50 44 0.50878516451792 1.7144464705013149 -0.42159945482941003 0.54117037792769895 0.71299080887547295 1.269413932725179 0.26951686627728799 0.22131431339594501 -0.13472012994437299 0.22075061567252199 -1.3199768562363781 -0.18681496091028299 0.380020030213299 -1.451131014273024 -0.56052688701790399 -0.116260966970037 -0.67291294283960901 -0.49015761805784802 -0.48430774902780499
Age
Residuals
RUNNING HEADER: WEEK 3 ASSIGNMENT 4 1
WEEK 3 ASSIGNMENT 4 13
Week 3 Assignment 4
Introduction
In this project I selected six variables from the ' SampleDataSet.xlsx'. Among these six variables three of them were continuous and the reaming three were discrete variables. The continuous variables selected for this study are Age, WealthScore and MedianSchoolYears. The discrete variables selected for this study are NumberOfChildren, MailResponder and NumberOfCars.
Analysis
Age
The age is a continuous variable which takes only positive values even though we usually consider the integer part of it. The descriptive statistics summary of the age variable .
Research Methodology 1
This document discusses different study designs used in public health research, including observational and interventional studies. Observational studies include cross-sectional, case-control and cohort studies. Cross-sectional studies collect data at one point in time to measure disease prevalence. Case-control studies compare exposures in cases (people with disease) versus controls (people without disease). Cohort studies follow groups over time to measure disease incidence and identify risk factors. Interventional studies, like randomized controlled trials, are considered the strongest type of evidence as they involve intervention to reduce exposure or compare alternative treatments.
Running Head SCENARIO NCLEX MEMORIAL HOSPITAL .docx
Running Head: SCENARIO NCLEX MEMORIAL HOSPITAL 1
SCENARIO NCLEX MEMORIAL HOSPITAL 11
Course Project - Phase 4
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 03/14/17
Introduction
The scenario I will be working with is that I am working at NCLEX Memorial Hospital in the infectious disease unit. As a healthcare professional, I need to work to improve the health of individuals, families and communities in various settings. The current situation that has posed as a problem at the hospital and raised eyebrows is that in the past few days, there has been an increase in patients admitted with a particular infectious disease. The basic statistical analysis shows that the disease does not affect minors hence the ages of the infected patients does play a critical role in the method that shall be required to treat the patients in order to impact positively on the health and well-being of the clients being served whether infected with the disease or associated with those infected. After speaking to the manager, we decided that we shall work together in utilising the available statistical analysis to look closer into the ages of the infected patients. To do that, I had to put together a spreadsheet with the data containing the information we shall need to carry out the analysis.
Data Analysis
From the data collected and input on an Excel sheet, there are sixty patients with the infectious disease. Of the patient’s whose data has already been collected an input on the excel sheet, the ages range from thirty-five years of age to seventy-six. There is only one patient in their thirties with the age of thirty-five. There are five patients in their forties, One forty-five, one forty-six, two at forty-eight and two at forty-nine. There are fifteen patients in their fifties, two at fifty, one fifty-two, one fifty-three, one fifty-four, four at fifty-five, one fifty-six, one at fifty-eight and four at fifty-nine. There are twenty-three patients in their sixties, five at sixty, one at sixty-two, one at sixty-three, two at sixty-four, one at sixty-five, three at sixty-eight and seven at sixty-nine. Finally, we have fifteen infected patients in their seventies, six at seventy, three at seventy-one, three at seventy-two, one at seventy-three, one at seventy-four and one at seventy-six. From the graph in Figure 1 below, the horizontal axis depicts the age group of patients infected with the disease and the vertical axis depicts the number of patients in the age group infected with the disease.
Figure 1
Data Classification
The qualitative variables in our data analysis would be the names of the patients infected with the disease while the quantitative data would be their ages, nu ...
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The document describes a study that retrospectively analyzed patient information from 25 individuals diagnosed with squamous cell carcinoma of the head/neck who were treated with radiation therapy over 5 weeks. Weight change over this period was calculated and statistically analyzed to examine average weight loss and potential risk factors. While results did not reach statistical significance, average weight loss was 3.12% (2.38 kg) over 5 weeks. Female gender and oral tumor site were identified as potential risk factors for greater weight loss.
Confidence Intervals in the Life Sciences PresentationNamesS.docx
Confidence Intervals in the Life Sciences Presentation
Names
Statistics for the Life Sciences STAT/167
Date
Fahad M. Gohar M.S.A.S
1
Conservation Biology of Bears
Normal Distribution
Standard normal distribution
Confidence Interval
Population Mean
Population Variance
Confidence Level
Point Estimate
Critical Value
Margin of Error
Welcome to the presentation on Confidence Intervals of Conservation Biology on Bears.
The team will define normal distribution and use an example of variables why this is important. A standard and normal distribution is discussed as well as the difference between standard and other normal distributions. Confidence interval will be defined and how it is used in Conservation Biology and Bears. We will learn how a confidence interval helps researchers estimate of population mean and population variance. The presenters defined a point estimate and try to explain how a point estimate found from a confidence interval. Confidence level is defined and a short explanation of confidence level is related to the confidence interval. Lastly, a critical value and margin of error are explained with examples from the Statdisk.
2
Normal Distribution
A normal distribution is one which has the mean, median, and mode are the same and the standard deviations are apart from the mean in the probabilities that go with the empirical rule. Not all data has the measures of central tendency, since some data sets may not have one unique value which occurs more than once. But every data set has a mean and median. The mean is only good with interval and ratio data, while the median can be used with interval, ratio and ordinal data. Mean is used when they're a lot of outliers, and median is used when there are few.
The normal distribution is continuous, and has only two parameters - mean and variance. The mean can be any positive number and variance can be any positive number (can't be negative - the mean and variance), so there are an infinite number of normal distributions. You want your data to represent the population distribution because when you make claims from the distribution of the sample you took, you want it to represent the whole entire population.
Some examples in the business world: Some industries which use normal distributions are pharmaceutical companies. They model the average blood pressure through normal distributions, and can make medicine which will help majority of the people with high blood pressure. A company can also model its average time to create something using the normal distribution. Several statistics can be calculated with the normal distribution, and hypothesis tests can be done with the normal distribution which models the average time.
Our chosen life science is BEARS. The age of the bears can be modeled by normal distributions and it is important to monitor since that tells us the average age of the bear, and can tell us a lot about the population. If the mean is high and the standard deviatio.
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Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterpri...
Clinical trials and health outcomes research differ in important ways that impact statistical modeling approaches. Clinical trials typically use homogeneous samples and focus on a single endpoint, while health outcomes data is heterogeneous with multiple endpoints. Predictive modeling techniques used in health outcomes research, like those in SAS Enterprise Miner, are better suited than traditional methods as they can handle complex real-world data without strong assumptions and more accurately predict rare events. Validation of models on separate test data is also important for generalizing results.
Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterpri...
This document discusses the differences between clinical trials and health outcomes research. Clinical trials use homogeneous samples, surrogate endpoints, and focus on a single outcome. They are also typically underpowered for rare events. Health outcomes research uses heterogeneous data from the general population to examine multiple real endpoints simultaneously. It has larger samples and data that allow analysis of rare occurrences. Predictive modeling is better suited than traditional statistical methods for analyzing heterogeneous health outcomes data due to relaxed assumptions like normality.
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This document discusses statistical concepts and tests relevant to epidemiology and biomedical research. It begins by defining key terms like mean, standard deviation, confidence intervals, and p-values. It then discusses different types of data and variables, measures of central tendency, the central limit theorem, and applications of standard error. The document provides examples of choosing appropriate statistical tests for different study designs, including t-tests, ANOVA, chi-square, correlation, and comparing means between two or more groups. Finally, it presents a case study analyzing water-borne disease deaths before and after a water supply installation using appropriate statistical tests.
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how much would it cost to do the following:
How can graphics and/or statistics be used to misrepresent data? Where have you seen this done?
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Complete Exercises 6, 8, and 9 in
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Use MS Word to complete "Questions to be Graded: Exercise 27" in
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Copyright © 2017, Elsevier Inc. All rights reserved. 67 EXERCISE 6 Questions to Be Graded Follow your instructor ’ s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/statistics/ under “Questions to Be Graded.”
Name: _______________________________________________________
Class: _____________________
Date: ___________________________________________________________________________________ 68EXERCISE 6 •
1. What are the frequency and percentage of the COPD patients in the severe airfl ow limitation group who are employed in the Eckerblad et al. (2014) study?
2. What percentage of the total sample is retired? What percentage of the total sample is on sick leave?
3. What is the total sample size of this study? What frequency and percentage of the total sample were still employed? Show your calculations and round your answer to the nearest whole percent.
4. What is the total percentage of the sample with a smoking history—either still smoking or former smokers? Is the smoking history for study participants clinically important? Provide a rationale for your answer.
5. What are pack years of smoking? Is there a signifi cant difference between the moderate and severe airfl ow limitation groups regarding pack years of smoking? Provide a rationale for your answer.
6. What were the four most common psychological symptoms reported by this sample of patients with COPD? What percentage of these subjects experienced these symptoms? Was there a sig-nifi cant difference between the moderate and severe airfl ow limitation groups for psychological symptoms?
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8. Is there a signifi cant difference between the moderate and severe airfl ow limitation groups regarding the .
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Running head: PHASE 1 SCENARIO NCLEX MEMOORIAL HOSPITAL 1
PHASE 1 SCENARIO NCLEX MEMORIAL HOSPITAL 6
PHASE 1/ Option 2 SCENARIO NCLEX MEMORIAL HOSPITAL
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 02/17/17
Introduction
The scenario I will be working with is that I am working at NCLEX Memorial Hospital in the infectious disease unit. As a healthcare professional, I need to work to improve the health of individuals, families and communities in various settings. The current situation that has posed as a problem at the hospital and raised eyebrows is that in the past few days, there has been an increase in patients admitted with a particular infectious disease. The basic statistical analysis shows that the disease does not affect minors hence the ages of the infected patients does play a critical role in the method that shall be required to treat the patients in order to impact positively on the health and well-being of the clients being served whether infected with the disease or associated with those infected. After speaking to the manager, we decided that we shall work together in utilising the available statistical analysis to look closer into the ages of the infected patients. To do that, I had to put together a spreadsheet with the data containing the information we shall need to carry out the analysis.
Data Analysis
From the data collected and input on an Excel sheet, there are sixty patients with the infectious disease. Of the patient’s whose data has already been collected an input on the excel sheet, the ages range from thirty-five years of age to seventy-six. There is only one patient in their thirties with the age of thirty-five. There are five patients in their forties, One forty-five, one forty-six, two at forty-eight and two at forty-nine. There are fifteen patients in their fifties, two at fifty, one fifty-two, one fifty-three, one fifty-four, four at fifty-five, one fifty-six, one at fifty-eight and four at fifty-nine. There are twenty-three patients in their sixties, five at sixty, one at sixty-two, one at sixty-three, two at sixty-four, one at sixty-five, three at sixty-eight and seven at sixty-nine. Finally, we have fifteen infected patients in their seventies, six at seventy, three at seventy-one, three at seventy-two, one at seventy-three, one at seventy-four and one at seventy-six. From the graph in Figure 1 below, the horizontal axis depicts the age group of patients infected with the disease and the vertical axis depicts the number of patients in the age group infected with the disease.
Figure 1
Data Classification
The qualitative variables in our data analysis would be the names of the patients infected with the disease while the quantitative data would be their ages, number of patients in each age category or age bracket that are infected with the disease and the number of patients in each specific age that are affect ...
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The document discusses biostatistics and its importance in research and data analysis. It defines key biostatistics concepts like population and sample, parameter and statistic, and measures of central tendency and dispersion. It also discusses hypothesis testing and provides an example analyzing body mass index data from a student population to compare means between gender groups and examine sampling error.
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The document discusses data distribution and presentation. It covers topics like the normal distribution curve, calculating probabilities using the standardized normal distribution table, and presenting data through tables and graphs. Specifically, it provides details on creating frequency distribution tables for qualitative and quantitative variables. It also discusses cross tabulation and different types of graphs like pie charts, simple bar charts, and multiple bar charts for presenting categorical data.
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Data Presentation and Slide Preparation
Data:
A set of values recorded on one or more observational units i.e. Object, person etc
Types of data:
Qualitative/ Quantitative data
Discrete/ Continuous data
Primary/ Secondary data
Nominal/ Ordinal data
Research methodology3
Measures of disease frequency include rates, ratios, and proportions. A ratio expresses the relation between two quantities where the numerator is not part of the denominator. A proportion indicates the relation of a part to the whole, with the numerator included in the denominator. A rate measures the occurrence of an event in a population during a time period. Other concepts discussed include incidence, prevalence, measures of central tendency (mean, median, mode), and measures of variation (range, standard deviation). Factors that can affect study outcomes include various types of biases such as selection, response, information, and confounding variables.
Hours, A. (2014). Reading Fairy Tales and Playing A Way of Treati.docx
Hours, A. (2014). Reading Fairy Tales and Playing: A Way of Treating Abused Children. Journal Of Infant, Child & Adolescent Psychotherapy, 13(2), 122. doi:10.1080/15289168.2014.905337
Marshall, E. (2009). Girlhood, Sexual Violence, and Agency in Francesca Lia Block's "Wolf". Children's Literature In Education, 40(3), 217-234.
Sanyal, N., & Dasgupta, M. (2017). Fairy tales: The Emotional Processors of Childhood Conflicts in Dynamic Interpretative Lens. SIS Journal Of Projective Psychology & Mental Health, 24(1), 39-47.
Basile, G. (2012, May 24). Sun, Moon, and Talia. Enchanted Conversation, 1-4.
Grimm, J., & Grimm, W. (1975). Briar Rose: The Sleeping Beauty. London: Pelham.
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Research Methodology 1
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Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
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Introduction
The scenario I will be working with is that I am working at NCLEX Memorial Hospital in the infectious disease unit. As a healthcare professional, I need to work to improve the health of individuals, families and communities in various settings. The current situation that has posed as a problem at the hospital and raised eyebrows is that in the past few days, there has been an increase in patients admitted with a particular infectious disease. The basic statistical analysis shows that the disease does not affect minors hence the ages of the infected patients does play a critical role in the method that shall be required to treat the patients in order to impact positively on the health and well-being of the clients being served whether infected with the disease or associated with those infected. After speaking to the manager, we decided that we shall work together in utilising the available statistical analysis to look closer into the ages of the infected patients. To do that, I had to put together a spreadsheet with the data containing the information we shall need to carry out the analysis.
Data Analysis
From the data collected and input on an Excel sheet, there are sixty patients with the infectious disease. Of the patient’s whose data has already been collected an input on the excel sheet, the ages range from thirty-five years of age to seventy-six. There is only one patient in their thirties with the age of thirty-five. There are five patients in their forties, One forty-five, one forty-six, two at forty-eight and two at forty-nine. There are fifteen patients in their fifties, two at fifty, one fifty-two, one fifty-three, one fifty-four, four at fifty-five, one fifty-six, one at fifty-eight and four at fifty-nine. There are twenty-three patients in their sixties, five at sixty, one at sixty-two, one at sixty-three, two at sixty-four, one at sixty-five, three at sixty-eight and seven at sixty-nine. Finally, we have fifteen infected patients in their seventies, six at seventy, three at seventy-one, three at seventy-two, one at seventy-three, one at seventy-four and one at seventy-six. From the graph in Figure 1 below, the horizontal axis depicts the age group of patients infected with the disease and the vertical axis depicts the number of patients in the age group infected with the disease.
Figure 1
Data Classification
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Confidence Intervals in the Life Sciences PresentationNamesS.docx
Confidence Intervals in the Life Sciences Presentation
Names
Statistics for the Life Sciences STAT/167
Date
Fahad M. Gohar M.S.A.S
1
Conservation Biology of Bears
Normal Distribution
Standard normal distribution
Confidence Interval
Population Mean
Population Variance
Confidence Level
Point Estimate
Critical Value
Margin of Error
Welcome to the presentation on Confidence Intervals of Conservation Biology on Bears.
The team will define normal distribution and use an example of variables why this is important. A standard and normal distribution is discussed as well as the difference between standard and other normal distributions. Confidence interval will be defined and how it is used in Conservation Biology and Bears. We will learn how a confidence interval helps researchers estimate of population mean and population variance. The presenters defined a point estimate and try to explain how a point estimate found from a confidence interval. Confidence level is defined and a short explanation of confidence level is related to the confidence interval. Lastly, a critical value and margin of error are explained with examples from the Statdisk.
2
Normal Distribution
A normal distribution is one which has the mean, median, and mode are the same and the standard deviations are apart from the mean in the probabilities that go with the empirical rule. Not all data has the measures of central tendency, since some data sets may not have one unique value which occurs more than once. But every data set has a mean and median. The mean is only good with interval and ratio data, while the median can be used with interval, ratio and ordinal data. Mean is used when they're a lot of outliers, and median is used when there are few.
The normal distribution is continuous, and has only two parameters - mean and variance. The mean can be any positive number and variance can be any positive number (can't be negative - the mean and variance), so there are an infinite number of normal distributions. You want your data to represent the population distribution because when you make claims from the distribution of the sample you took, you want it to represent the whole entire population.
Some examples in the business world: Some industries which use normal distributions are pharmaceutical companies. They model the average blood pressure through normal distributions, and can make medicine which will help majority of the people with high blood pressure. A company can also model its average time to create something using the normal distribution. Several statistics can be calculated with the normal distribution, and hypothesis tests can be done with the normal distribution which models the average time.
Our chosen life science is BEARS. The age of the bears can be modeled by normal distributions and it is important to monitor since that tells us the average age of the bear, and can tell us a lot about the population. If the mean is high and the standard deviatio.
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Biostatics ppt
This document provides an overview of biostatistics and descriptive statistics. It defines key biostatistics concepts like data, distributions, and descriptive statistics. It explains how to display data through tables, graphs, and numerical summaries. These include frequency distribution tables, pie charts, bar diagrams, histograms, and more. Descriptive statistics are used to numerically summarize and describe data through measures of central tendency and dispersion.
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This document discusses the differences between clinical trials and health outcomes research. Clinical trials use homogeneous samples, surrogate endpoints, and focus on a single outcome. They are also typically underpowered for rare events. Health outcomes research uses heterogeneous data from the general population to examine multiple real endpoints simultaneously. It has larger samples and data that allow analysis of rare occurrences. Predictive modeling is better suited than traditional statistical methods for analyzing heterogeneous health outcomes data due to relaxed assumptions like normality.
Multiple Linear Regression Homework Help
I am Samson H. I am a Multiple Linear Regression Homework Expert at statisticshomeworkhelper.com. I hold a Master's in Statistics, from Michigan, USA. I have been helping students with their homework for the past 12 years. I solved homework related to Multiple Linear Regression.
Visit statisticshomeworkhelper.com or email info@statisticshomeworkhelper.com.You can also call on +1 678 648 4277 for any assistance with Multiple Linear Regression Homework Help.
sta
This document discusses statistical concepts and tests relevant to epidemiology and biomedical research. It begins by defining key terms like mean, standard deviation, confidence intervals, and p-values. It then discusses different types of data and variables, measures of central tendency, the central limit theorem, and applications of standard error. The document provides examples of choosing appropriate statistical tests for different study designs, including t-tests, ANOVA, chi-square, correlation, and comparing means between two or more groups. Finally, it presents a case study analyzing water-borne disease deaths before and after a water supply installation using appropriate statistical tests.
how much would it cost to do the followingHow can graphics and.docx
how much would it cost to do the following:
How can graphics and/or statistics be used to misrepresent data? Where have you seen this done?
What are the characteristics of a population for which it would be appropriate to use mean/median/mode? When would the characteristics of a population make them inappropriate to use?
Questions to Be Graded: Exercises 6, 8 and 9
Complete Exercises 6, 8, and 9 in
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and submit as directed by the instructor.
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Questions to Be Graded: Exercise 27
Use MS Word to complete "Questions to be Graded: Exercise 27" in
Statistics for Nursing Research: A Workbook for Evidence-Based Practice
. Submit your work in SPSS by copying the output and pasting into the Word document. In addition to the SPSS output, please include explanations of the results where appropriate.
Copyright © 2017, Elsevier Inc. All rights reserved. 67 EXERCISE 6 Questions to Be Graded Follow your instructor ’ s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/statistics/ under “Questions to Be Graded.”
Name: _______________________________________________________
Class: _____________________
Date: ___________________________________________________________________________________ 68EXERCISE 6 •
1. What are the frequency and percentage of the COPD patients in the severe airfl ow limitation group who are employed in the Eckerblad et al. (2014) study?
2. What percentage of the total sample is retired? What percentage of the total sample is on sick leave?
3. What is the total sample size of this study? What frequency and percentage of the total sample were still employed? Show your calculations and round your answer to the nearest whole percent.
4. What is the total percentage of the sample with a smoking history—either still smoking or former smokers? Is the smoking history for study participants clinically important? Provide a rationale for your answer.
5. What are pack years of smoking? Is there a signifi cant difference between the moderate and severe airfl ow limitation groups regarding pack years of smoking? Provide a rationale for your answer.
6. What were the four most common psychological symptoms reported by this sample of patients with COPD? What percentage of these subjects experienced these symptoms? Was there a sig-nifi cant difference between the moderate and severe airfl ow limitation groups for psychological symptoms?
7. What frequency and percentage of the total sample used short-acting β 2 -agonists? Show your calculations and round to the nearest whole percent.
8. Is there a signifi cant difference between the moderate and severe airfl ow limitation groups regarding the .
Running head PHASE 1 SCENARIO NCLEX MEMOORIAL HOSPITAL1PHASE .docx
Running head: PHASE 1 SCENARIO NCLEX MEMOORIAL HOSPITAL 1
PHASE 1 SCENARIO NCLEX MEMORIAL HOSPITAL 6
PHASE 1/ Option 2 SCENARIO NCLEX MEMORIAL HOSPITAL
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 02/17/17
Introduction
The scenario I will be working with is that I am working at NCLEX Memorial Hospital in the infectious disease unit. As a healthcare professional, I need to work to improve the health of individuals, families and communities in various settings. The current situation that has posed as a problem at the hospital and raised eyebrows is that in the past few days, there has been an increase in patients admitted with a particular infectious disease. The basic statistical analysis shows that the disease does not affect minors hence the ages of the infected patients does play a critical role in the method that shall be required to treat the patients in order to impact positively on the health and well-being of the clients being served whether infected with the disease or associated with those infected. After speaking to the manager, we decided that we shall work together in utilising the available statistical analysis to look closer into the ages of the infected patients. To do that, I had to put together a spreadsheet with the data containing the information we shall need to carry out the analysis.
Data Analysis
From the data collected and input on an Excel sheet, there are sixty patients with the infectious disease. Of the patient’s whose data has already been collected an input on the excel sheet, the ages range from thirty-five years of age to seventy-six. There is only one patient in their thirties with the age of thirty-five. There are five patients in their forties, One forty-five, one forty-six, two at forty-eight and two at forty-nine. There are fifteen patients in their fifties, two at fifty, one fifty-two, one fifty-three, one fifty-four, four at fifty-five, one fifty-six, one at fifty-eight and four at fifty-nine. There are twenty-three patients in their sixties, five at sixty, one at sixty-two, one at sixty-three, two at sixty-four, one at sixty-five, three at sixty-eight and seven at sixty-nine. Finally, we have fifteen infected patients in their seventies, six at seventy, three at seventy-one, three at seventy-two, one at seventy-three, one at seventy-four and one at seventy-six. From the graph in Figure 1 below, the horizontal axis depicts the age group of patients infected with the disease and the vertical axis depicts the number of patients in the age group infected with the disease.
Figure 1
Data Classification
The qualitative variables in our data analysis would be the names of the patients infected with the disease while the quantitative data would be their ages, number of patients in each age category or age bracket that are infected with the disease and the number of patients in each specific age that are affect ...
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The document discusses biostatistics and its importance in research and data analysis. It defines key biostatistics concepts like population and sample, parameter and statistic, and measures of central tendency and dispersion. It also discusses hypothesis testing and provides an example analyzing body mass index data from a student population to compare means between gender groups and examine sampling error.
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The document discusses data distribution and presentation. It covers topics like the normal distribution curve, calculating probabilities using the standardized normal distribution table, and presenting data through tables and graphs. Specifically, it provides details on creating frequency distribution tables for qualitative and quantitative variables. It also discusses cross tabulation and different types of graphs like pie charts, simple bar charts, and multiple bar charts for presenting categorical data.
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Data Presentation and Slide Preparation
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A set of values recorded on one or more observational units i.e. Object, person etc
Types of data:
Qualitative/ Quantitative data
Discrete/ Continuous data
Primary/ Secondary data
Nominal/ Ordinal data
Research methodology3
Measures of disease frequency include rates, ratios, and proportions. A ratio expresses the relation between two quantities where the numerator is not part of the denominator. A proportion indicates the relation of a part to the whole, with the numerator included in the denominator. A rate measures the occurrence of an event in a population during a time period. Other concepts discussed include incidence, prevalence, measures of central tendency (mean, median, mode), and measures of variation (range, standard deviation). Factors that can affect study outcomes include various types of biases such as selection, response, information, and confounding variables.
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How are financial statements used to evaluate business activities?
What is managerial accounting and how does it help businesses create a competitive advantage
What skills must be developed to evaluate company performance?
How are investment and operations alternatives evaluated and selected?
2pages
.
How are Japanese and Chinese Americans similar How are they differe.docx
How are Japanese and Chinese Americans similar? How are they different? After exploring your course material and outside sources, please share the differences and similarities on such topics as immigration patterns, family life, economic picture, etc. Be sure to cite your sources properly. At a minimum, students should include summarized (no direct quotes) information from the course text. Do not forget to include page numbers in your in-text citations!
.
Hot Spot PolicingPlace can be an important aspect of crime and.docx
Hot Spot Policing
"Place" can be an important aspect of crime and crime prevention. Behaviors occur across space and time. In the 1990's hot spot policing became a very popular topic.
In this paper, you need to address the following:
Define the concept of "Hot Spot" from the context of law enforcement,
Define the concept of "Hot Product" from the context of law enforcement,
Describe VIVA and CRAVE (as they relate to hot spots and hot products),
Identify a few products or objects that might be prime targets for crime, and
Indicate how these products or objects fit the ideas of VIVA and CRAVED.
.
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This document provides instructions for the first assignment of a brand analysis paper. Students must choose a hotel brand and write a paper analyzing the brand using at least four sources. The paper should be organized using provided topic headings, including: brand overview and history; target markets; brand position; and brand elements. For each section, the document provides guidance on what information should be included. For example, for the brand overview and history section, it instructs students to provide a brief history and timeline of the brand, as well as information on the brand's geographic locations and parent company. MLA format is required.
Hou, J., Li, Y., Yu, J. & Shi, W. (2020). A Survey on Digital Fo.docx
Hou, J., Li, Y., Yu, J. & Shi, W. (2020). A Survey on Digital Forensics in Internet of Things IEEE Internet of Things Journal, I(1),1-15,.
Chen, J. & Zhu, Q. (2019). Interdependent Strategic Security Risk Management With Bounded Rationality in the Internet of Things. IEEE Transactions on Information Forensics and Security, 14(11), 2958-2971.
Borek, A. (2014). Total Information Risk Management: Maximizing the Value of Data and Information Assets (Vol. First edition). Amsterdam: Morgan Kaufmann
The readings this week discusses broad context of risk and investigative forensics. Part of risk management is to understand when things go wrong, we need to be able to investigate and report our findings to management. Using this research, or other research you have uncovered discuss in detail how risk and investigate techniques could work to help the organization. ERM helps to protect an organization before an attack, where as forensics investigate technique will help us after an attack - so lets discus both this week.
Please make your initial post and two response posts substantive. A substantive post will do at least two of the following:
Ask an interesting, thoughtful question pertaining to the topic
Answer a question (in detail) posted by another student or the instructor
Provide extensive additional information on the topic
Explain, define, or analyze the topic in detail
Share an applicable personal experience
Provide an outside source that applies to the topic, along with additional information about the topic or the source (please cite properly in APA 7)
Make an argument concerning the topic.
.
How (Not) to be Secular by James K.A. SmithSecular (1)—the ea.docx
How (Not) to be Secular? by James K.A. Smith
Secular (1)—the earthly plane of domestic life as distinguished from the sacred.
Secular (2)—areligious, neutral, unbiased, “objective”
Secular (3)—a world in which it is possible to imagine not believing in God; religious belief is no longer axiomatic.
Cross-pressured—The simultaneous pressure of various spiritual options or the feeling of being caught between an echo of transcendence and the drive toward immanentization.
Immanent frame—A constructed social space that frames our lives entirely within a natural order, an order “whose working could be systematically understood and explained in its own term, leaving open the question whether this whole order had a deeper significance....” (Taylor, A Secular Age, p. 15)
Exclusive humanism—A worldview that is able to account for meaning and significance without any appeal to the divine or transcendence.
Self-transcendence—a turning of life toward something beyond ordinary human flourishing. (Taylor, p. 44)
Fullness—The human impulsion to find significance, meaning, value—even within an entirely immanent frame.
Spin—A construal of life in an immanent frame that does not recognize itself as construal. Does not grant plausibility to the alternative,
Take—A construal of life in an immanent frame that is open to appreciating the viability of other takes.
Modern Moral Order (MMO)—Understanding of morality that focuses on the organization of society for mutual benefit rather than obligation to higher or eternal norms.
Faith
Faith Development Theory and a Look at Faith Today
Definitions of Faith
Faith (in general) = one’s ultimate concern (Paul Tillich)
Religious Faith = a relationship with God which engages a person’s total personhood; (Fischer and Hart reading); personal knowledge of God (Richard McBrien)
Faith is not primarily belief in ideas but in God
Difference between faith and “the faith”—the latter usually refers to a collection of “beliefs”
Misunderstandings of the Meaning of Faith
1. Having faith is believing things, “assenting to truths”—the rationalist misunderstanding
2. Having faith is behaving morally—the moralist misunderstanding
3. Having faith is feeling something —the emotionalist misunderstanding
These are all aspects of faith but faith cannot be reduced to any one of these.
Key Points about Faith
1. “Faith seeks understanding and is a friend of reason.” (The United States Catholic Catechism for Adults) In the words of Vatican I, faith is “consonant with reason.” Faith and reason are compatible. Faith is not “blind faith.”
2. Although faith has a content (beliefs), what Christians believe in are not the formulas of faith but in the realities they express.
3. Faith is a commitment of the whole person, not just the intellect of the person.
4. Faith is both personal and communal.
Faith Development Theory
Fowler’s Stages of Faith
Similar to Lawrence Kohlberg’s Stages of Moral Development
Overview of the Stages.
Hopefully, you enjoyed this class on Digital Media and Society.Q.docx
Hopefully, you enjoyed this class on Digital Media and Society.
Question #1:
Has your impression of digital media and society changed after taking this class? How? What in your opinion is the future of digital media and the Internet?
Topic 2: One Takeaway
There are no readings assigned this week. Reflect on the weekly Read & Watch content you have been exposed to throughout the semester to craft your response.
Discussion:
There are many interesting concepts, ideas, and theories presented in this class. We talked Curly Fries, Filter Bubbles, Global Collaboration, Digital Divide, Privacy, Big Data...
Question #1:
What is the most important thing you have learned from this class? What is your Number One Takeaway?
.
hoose (1) one childhood experience from the list provided below..docx
hoose
(1) one
childhood experience from the list provided below. This list has been generated from the Australian Institute of health and Welfare (2020) Australia’s Children report.
Asthma in children aged 5-14
Type 1 diabetes in children aged 0-14
Brain cancer in children up to 14 years
Anxiety disorders in children aged 5-14
Dental decay in children over 12
Overweight/Obesity in children 5-14
School aged child living with an intellectual disability
Children experiencing homelessness
Low birthweight babies
Children who are exposed to or a victim of family violence
After researching the prevalence of your chosen childhood experience, develop a brief hypothetical case study (200 words or so) about a child and their family highlighting the health care setting in which you (the nurse) meet the family;
the case study helps to guide your essay and provide context for your reader.
To explore the impact of your chosen childhood experience on the child and their family in the case study you have developed, read widely and address the following prompts informed by
contemporary and relevant
developmental, nursing and family care theory:
Outline the prevalence in Australia of the childhood experience chosen and highlight the short term, medium term and potential long term health outcomes/impact on the child
Discuss the impact (protective or otherwise) of family, culture and environment on the identified health outcomes for children identified from prompt 1
Explore the impact of the chosen childhood experience on the learning and development of the child in the case study, in light of their age/stage of development
When you (the nurse) meet the family (as outlined in your case study) how might you engage therapeutically with the child and family? Outline age/development appropriate communication strategies that you could adopt to support child/family flourishing
Based on the chosen childhood experience selected and the case study specifics, provide two priority nursing actions/interventions with rationales, that would advocate for positive child/family health outcomes
This essay will be developed with an introduction, body and conclusion, with correct grammar and spelling and acknowledging sources using APA 7th Edition referencing style. Word count of 2000 words +/-10% will be maintained; inclusive of intext references, excluding reference list.
Rubric
Assessment 3: Case study and literature review
Assessment 3: Case study and literature reviewCriteriaRatingsPtsThis criterion is linked to a learning outcome1.Case study developed is clear, informative and feasible
5
PtsExceeds expectationsVery well considered case study. The child/family circumstances are realistic and comprehensively presented in light of the chosen childhood experience; the context of the family and nurse interaction is clearly defined.4
PtsMeets ExpectationsWell considered case study. The child/family circumstances are realistic and thoroughly present.
honesty, hard work, caring, excellence HIS 1110 Dr. .docx
honesty, hard work, caring, excellence
HIS 1110 Dr. G. J. Giddings
ANALYSIS PAPER: GREAT MIGRATION HISTORY AS TACTIC
4 pages; 4 “works cited”/reference sources (at least 1 primary source)
Related Course Outcome: Identify and analyze connections between individual events and national
historic events.
OPTIONS I
Analysis Paper: Migration As Tactic/
Solution
(Long Interviews as main resource) (4 pages)
Millions of African Americans escaped or ran away from their homes in the South to seek refuge
in the North. Explore your individual interviewees’ descriptions of problems (lack of opportunities, racial
terrorism, personal reasons, etc.) they faced in the South or wherever, and how they attempted to
escape these problems by choosing migration as a tactic or solution. Explore how the individual
migration stories connect to or compare with the general Great Migration “push” and “pull” factors,
which motivated so many African Americans to leave the South. To help draw these connections,
identify and compare the “pull” and “push” factors of the Great Migration as well as other related
course themes such as: leadership tactics, African America socio-economic and culture status/state,
protest; demographic shifts/changes, etc.
OPTIONS II
Analysis Paper: Migration As Tactic/
.
hoose one of the four following visualsImage courtesy o.docx
hoose one of the four following visuals:
Image courtesy of: Nike® 2013 advertisement
Image courtesy of: Parents magazine June 2011
Image courtesy of: Harley Davidson® advertisement
Image courtesy of: Bank of America advertisement
In a
2-3-page APA formatted paper with an additional reference page
(
template here
), analyze the strategic use of perceptual visual communication:
Analyze how specific semiotic visuals in your chosen image affect different cultural perceptions (age, ethnicity, social group, etc.).
Describe how each culture’s cognitive memories and experiences may affect how they perceive this image.
Explain why cultural perception is important to consider when working with international or global cultures.
Discuss why it is important to consider cultural perception when interacting with different age cultures and different social groupings.
Support the items above by including relevant quotes and paraphrases from academic/scholarly sources.
.
HomeworkChoose a site used by the public such as a supermark.docx
Homework
Choose a site used by the public such as a supermarket, doctor's office, library, post office, or department store and observe one or more key processes, the associated suppliers, inputs, process steps, outputs, customers, the measurement systems, and how the measurements are used to manage and improve the process. Submit a Word document in which you include the following:
Company visited.
Process observed.
SIPOC elements.
Process measurements.
Process management systems used.
.
Homework 2 Please answer the following questions in small paragraph.docx
The document contains homework questions asking about the conditions in Europe that led to the conquest of America, the effects of the conquest on native peoples and Europeans, the motives and backgrounds of Spanish conquistadors, and factors explaining how a small number of Spaniards conquered large indigenous empires.
HomeNotificationsMy CommunityBBA 2010-16J-5A21-S1, Introductio.docx
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BBA 2010-16J-5A21-S1, Introduction to Business
Unit VI
Upload Assignment: Unit VI Case Study
BBA 2010-16J-5A21-S1, Introduction to Business
COURSE INFORMATION
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HomeAnnouncementsSyllabusDiscussionsQuizzesGra.docx
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Course Resource Booklet (Final Submission Due)
Child Welfare Advocates have many resources available to them to assist those in need. The key is to know what resources are available. This assignment will require students to create resources on local and child welfare social services and present in a booklet format. This will be an ongoing course project as you learn about new resources in each module. As you continue with your classes, you may even choose to add additional resources.
Timeline
Module 1
— Course Resource Project assigned
Module 2
— Submit for instructor feedback – non-graded.
Students will submit the title page, introduction and one agency description in Module 2 for purposes of non-graded feedback.
Module 5
— Final Course Resource project due
Directions
Students will create a booklet that compiles information for twelve different agencies.
The twelve agencies must represent the following categories of general services. No category can be eliminated.
Adult Assistance — include one agency that specializes in helping the homeless
Child Development — include one agency that specializes in helping those in poverty
Family Support Services — include one resource on family violence
Health — include one agency that specializes in helping those with addiction
Legal — include the Guardian Ad Litem program
Mental Health
Each category should include:
A maximum of two agency resources for each category.
No more than one state agency may be included in each category.
Do not use the same agency more than once.
Agencies should be alphabetized by category.
Students should work on the Course Resource Booklet each module so they have a cumulative booklet ready for submission. Do not wait until the last minute.
Each local agency resource should include the following information. Use headings for components, as needed.
(See sample page attached.)
Name of Agency
Agency address (Domestic Violence shelters may use outreach center)
Website URL
Hours of Operation
Agency Description of 150 to 175 words
. This part of the resource booklet must be written and paraphrased in paragraph format and must include the following information:
(1.5 spacing for submission to instructor)
Mission/Purpose of the resource
Populations Served
Specific Type of Services
Eligibility Requirements
One strength and one limitation of the agency or its services
The final booklet should include:
A Title Page
Table of Contents
An introduction with a statement of purpose and definition of child welfare
Use paragraph form for agency description, strength and limitations of resource
Agency information should be alphabetized and spaced at 1.5
Use Times New Roman, size 12 font
Include page numbers
Be visually appealing and professional in appearance
Submit your document to Turnitin®. Turnitin® will tell you if you have copied text from o.
Homeless The Motel Kids of Orange CountyWrite a 1-2 page pa.docx
Homeless: The Motel Kids of Orange County
Write a 1-2 page paper double spaced using 12 point Calibri/Times New Roman font.
Your paper should be written using the “conflict” and “symbolic interactionism” perspective. 1) What was the premise of the video? 2) Give two examples of how the children understood the situation they are in. 3) Why or why not do you see hope for the children. 4) How did the video make you feel?
.
Home work 8 Date 042220201. what are the different between.docx
The document discusses two California government websites, waterboards.ca.gov and water.ca.gov and asks what the differences are between the agencies that run each site. It instructs the reader to visit both websites to learn more.
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hoose one of the four following visualsImage courtesy o.docx
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Descriptive Statistics and Interpretation ExampleQNT561 Versi.docx
- 1. Descriptive Statistics and Interpretation Example QNT/561 Version 8 1 University of Phoenix Material Descriptive Statistics and Interpretation Example Interpretation Phrases Central Tendency: Mean = average of a set of data Median = half or equal number of data is above and half or equal number of data is below. It is a midpoint in an ordered (sorted) set of data, a physical location Mode = most frequent value in a set of data Dispersion: Standard deviation = variation Interquartile range (IQR) = the middle 50% of the data Range = the difference between the largest and smallest value of the data Confidence Interval: (data must be normal) There is 95% confidence that the population average is between _____ and ____ units. Normal or significantly skewed data: MegaStat: Descriptive statistics Normal curve goodness of fit p- value · Normal, p-value > .05 · Significantly Skewed, p-value < .05
- 2. Histogram: Eyeball the histogram. · Normal data will have a symmetrical or slightly skewed shape. · Significantly Skewed shape will have extreme skewness Use phrase combinations: Normally distributed: Mean and Standard Deviation, Not normally distributed: Median and IQR Descriptive Statistics Body Weight (Lbs.) Central Tendency: Mean = 149 Lbs. Dispersion: Standard deviation = 30 Lbs. Count: 100 Min/Max: 99 pounds and 234 Lbs. Confidence Interval: 144 to 155 Lbs. See the histogram in Appendix A, and descriptive statistics in Appendix B. Age Distribution is not normally distributed Central Tendency: Median = 36 years Dispersion: Interquartile Range = 20.5 years / 2 = ± 10 years Count: 100 Min/Max: 18 years and 74 years Confidence Interval: Not applicable (data is not normally distributed)
- 3. See the histogram in Appendix A, and descriptive statistics in Appendix B. A scatter plot is in Appendix C. Education Level Thirteen percent of the subjects have no high school degree while 44% have high school degree. Forty three percent have a college or college graduate degree. See the bar chart in Appendix D. Descriptive Statistics Interpretation Interpretation Body Weight One hundred subjects were randomly selected. Their body weight was observed between 99 and 234 pounds. Their average weight was 149 pounds, with a variation of plus or minus 30 pounds. One half or more were above 149 pounds. There is 95% confidence that the population body weight average is between 144 and 155 pounds. Age The data was significantly skewed. One hundred subjects were randomly selected. Their ages were between 18 and 74 years, with a variation of plus or minus 10 years. One half or more subjects were 36 years of age or older. The middle half of the subjects’ ages fell between 27 and 47 years. The most frequent age was 36 years. APPENDIX A Body Weight and Age Histograms
- 4. APPENDIX B Descriptive Statistics Body Weight and Age APPENDIX C Scatterplot Body Age versus Weight APPENDIX D Bar Chart Education Level Copyright © 2015 by University of Phoenix. All rights reserved. Sheet1PriceBedroomsSizePoolDistanceTwnshipGarageBaths263 .142300117512182.442100019402242.132300012302213.62220 0016202.5139.922100028101.5245.422100112112327.26250001 5312271.82210009212.5221.132300118101.5266.642400013412 292.4421000143122092170008411.5270.86250007412246.1421 00018312194.422300011302281.332100016212172.7422001163 02207.552300121402.5198.932200110412209.36190011541225 2.34260008412192.941900114212.5209.352100020501.5345.38 260009412326.362100011513173.122200121511.518721900026 402257.2221000941223332200014311.5180.4220000115022342 1700019312207.122000011512247.752400016212166.23200011 6212177.121900010512182.742000114402.52164230001920231 2.16260007512.5199.832100019312273.2522000162132063210 019301.5232.231900116111.5198.342100119111.5205.1320001 20402175.642300124412307.832400121213269.2522000851322 4.832200017112.5171.632000116402216.832200015112192.662 200114102236.452200020312172.432200023302251.431900012
- 5. 2122466230007313147.46170011210217642200015112228.432 300017511.5166.531600119302.5189.442200024112312.172400 013313289.862000021313269.952200111412.5154.3220000132 02222.12210009512209.752200113212190.932200118312254.3 42500115312207.532100110202209.7422001192122942210001 3212.5176.322000117302294.3724000841222431900161121252 1900018401.5236.842600117512164.142300019402217.832500 012302192.222400016202.5125.922400028101.5220.922300112 112294.562700015312244.62230009212.519932500118101.524 042600013412263.242300014312188.12190008411.5243.76270 007412221.54230001831217522500011302253.2323000162121 55.442400116302186.752500121402.517932400110412188.362 100115412227.14290008412173.642100114212.5188.35230002 0501.5310.88290009412293.7624000115131793240008412188. 362100114212.5227.142900020501.5173.64210009412188.3523 00011513 SHORT TITLE OF PAPER 1 Running head: DESCRIPTIVE STATISTICS 1Descriptive StatisticsNameQNT/561 Date Instructor’s NameDescriptive Statistics Determine the appropriate descriptive statistics. Note: If the data was normally distributed, use the mean and standard deviation. If the data was skewed significantly, use the median and interquartile range. Numeric Variable Name1 Distribution: State if not normally distributed Central Tendency: Dispersion:
- 6. Number: Min/Max: Confidence Interval: (if distribution is normal) Numeric Variable Name2 (if applicable) Distribution: State if not normally distributed Central Tendency: Dispersion: Number: Min/Max: Confidence Interval: (if distribution is normal) Attribute Variable Name (if applicable) Create a bar chart. Describe the proportions. Descriptive Statistics Interpretation Numeric Variable Name1 Describe the variable in laymen terms. Numeric Variable Name2 (if applicable) Describe the variable in laymen terms. Appendix A Raw data used in the analysis Fit data to one page. Appendix B Charts and Tables This part of the paper will include items that are then cited in the body of the paper. Usually, large items are placed here not to distract from reading the paper. Appendix C Descriptive Statistics This part of the paper will include descriptive statistics.