Measurement Scales/Variables Paper

Measurement Scales/Variables, Measurement, and Statistics

Order Instructions:

SECTION A (1.5pages)
Measurement Scales
Quantitative analysis requires the use of numeric data to describe and interpret the results. The types of numeric data collected will determine what statistics can be utilized.
1. Please provide a definition of the nominal, ordinal, interval, and ratio scales and develop a simple chart with an example of each and specifying the types of statistics that might be used with each as follows:
Type of Data Example Statistical Procedure
Nominal (provide definition)
Ordinal (provide definition)
Interval (provide definition)
Ratio (provide definition)
2. Conclude your discussion with a reflection on how statistics might be utilized in your own evidence-based practice.
3. Provide at least three citations with full references to credible nursing scholarly articles supporting your definitions and discussion.

SECTION B (1.5pages minimum)

Variables, Measurement, and Statistics
A nurse has decided to research the following PICOT question: “Adult clients who are admitted to the cardiac unit with congestive heart failure are more likely to develop nosocomial infections than other cardiac clients admitted to the cardiac unit.” A quantitative research design is planned for this Project.
1. From the PICOT question, determine the following:
a. Identify the variables
b. Identify the levels of measurement of each variable
c. Identify the statistical test(s) to be used.
2. Provide at least three citations with full references to credible nursing scholarly articles supporting your definitions and discussion.

Resources to be used for each of the sections

• Coughlan, M., Cronin, P., & Ryan, F. (2007). Step-by-step guide to critiquing research. Part 1: quantitative research. British Journal Of Nursing, (BJN), 16(11), 658–663.

• Giuliano, K., & Polanowicz, M. (2008). Interpretation and use of statistics in nursing research. AACN Advanced Critical Care, 19(2), 211–222

• Ingham-Broomfield, R. (2008). A nurses’ guide to the critical reading of research. Australian Journal of Advanced Nursing, 26(1), 102-109.

From your textbooks, read:
Introduction to Nursing Research Incorporating Evidence-Based Practice
• Chapter 8: “Quantitative Design”
• Chapter 12: “Data Analysis”
• Chapter 13: “Critique Process”

SAMPLE ANSWER

Measurement Scales/Variables, Measurement, and Statistics

Section A

Types of data	Example	Statistical procedure
Nominal	Hair color	Mode for central tendency
Ordinal	How do you feel?	Mode and median
Interval	Celsius temperature, time	Arithmetic mean, median, mode, standard deviation, and range
Ratio	Height and weight	Arithmetic mean, median, mode, harmonic mean, geometric mean, studentized range, and coefficient of variation.

Nominal scales are utilized for labeling variables in the absence of quantitative value. They can simply be referred to as labels or names. The scales have no overlap or are mutually exclusive and none has numerical significance.

Ordinal scales; the order of values is more significant and important. However, the variation between each one is unknown. These are typically non-numeric concepts’ measures such as discomfort, happiness, and satisfaction. These can be easily remembered through the word ‘order’. The mode and median are the best when determining the central tendency is an ordinal data set (Moorhead, 2013).

Interval scales; there are numeric scales where the order as well as the exact variations between values is known. The statistical analysis realm on the data sets opens since the mean, median, statistical deviation, and mode can be calculated. These scales have no true zero and this makes it impossible to calculate the ratios (Moorhead, 2013). For instance, there is no time or no temperature. With the interval data, one can subtract and add but cannot divide or multiply. The key thing to remember in interval scales is the interval, which implies the space in between.

Ratio scales are acknowledged as the overall as far as the measurement scales are concerned. This is because they tell about the order, exact values between units, and possess an absolute zero that permits the application of a wide array of both inferential and descriptive statistics. Ratio scales also have a vivid definition of zero. Ratio scales offer a wealth of possibilities as far as statistical analysis is concerned. With these variables, it is possible to add, subtract, multiply, and divide. It is also possible to measure the mean, median, mode, coefficient of variation, and measures of dispersion (Ingham-Broomfield, 2008).

How statistics can be used in evidence-based practice

Recently, evidence-based practice has become very essential in healthcare. In this regard, there is a need for healthcare professionals to be aware with the practice, how to use it, and its significance in guarding patient safety. More specifically, the Obama Administration is dedicated to policy decisions that are guided by evidence. In this regard, there should be a greater promotion of statistics use as well as the statisticians’ role in making proper decisions that are founded on objective evidence. Evidence-based medicine refers to the explicit, conscientious, and judicious use of the current best evidence when making decisions on individual patient care. The practice of using evidence-based medicine implies integrating personal clinical experience with proper available clinical evidence from external sources (systemic research). In this regard, statistics is fundamental to the evidence-based medicine (Moorhead, 2013).

It is worth pointing out that that evidence-based practice emerges from the most recent evidence. This implies the deep connection between recent research and the decisions being made in the healthcare practice. The statistics that are used in the research form the foundation of the strategies that will be implemented as well as the decisions that will be made. In this regard, the statistics and data should be trustworthy. This brings in the aspect of validity and consistency of data. If possible, only statisticians should be allowed to handle the data so as to ensure that the right procedures are being followed and that the data collected is proper. As a result, the evidence-based practice becomes more productive and applicable (Coughlan, Cronin & Ryan, 2007).

References

Coughlan, M., Cronin, P., & Ryan, F. (2007). Step-by-step guide to critiquing research. Part 1: quantitative research. British Journal Of Nursing, (BJN), 16(11), 658–663.

Ingham-Broomfield, R. (2008). A nurses’ guide to the critical reading of research. Australian Journal of Advanced Nursing, 26(1), 102-109.

Moorhead, S. (2013). Nursing outcomes classification (NOC): Measurement of health outcomes. St. Louis, Mo: Elsevier/Mosby.

Section B

The PICOT format in clinical questions aims at ensuring that researchable and answerable questions are developed (Melnyk & Fineout-Overholt, 2011). P (patient population), I (issue of interest or intervention), C (comparison issue of interest or intervention), O (outcomes of interest), and T (the time needed for outcomes to be achieved in the intervention) (Giuliano & Polanowicz, 2008).

‘Adult clients who are admitted to the cardiac unit with congestive heart failure are more likely to develop nosocomial infections than other cardiac clients admitted to the cardiac unit.’

Variables

Other cardiac patients and congestive heart failure patients. There are also variables of the cardiac unit and nosocomial infections.

Each variable’s levels of measurement

Cardiac patients- ordinal

Congestive heart failure patients- ordinal

Cardiac unit- nominal

Nosocomial infections- ordinal

Statistical tests

Based on the fact that there are only ordinal and nominal levels of measurement, it is impossible to have statistical tests such as mean, mode, and median. Therefore, there are high chances that the study being conducted was qualitative where no kinds of inferential and statistical tests are required (Boswell, Boswell & Cannon, 2014). The Ch-square, Fisher’s exact, and Wilcoxon- Mann Whitney tests will be used since there are independent variables.

Basically, the use of PICOT questions when carrying out evidence-based practice is very use. This promotes the development of answerable and researchable questions. This ensures that the evidence gathered is more applicable in the practice (Aveyard & Sharp, 2013).

References

Aveyard, H., & Sharp, P. (2013). A Beginner’S Guide To Evidence-Based Practice In Health And Social Care. Maidenhead: McGraw-Hill Education.

Boswell, C., Boswell, C., & Cannon, S. (2014). Introduction to nursing research: Incorporating evidence-based practice. Burlington, MA: Jones & Bartlett Learning

Giuliano, K., & Polanowicz, M. (2008). Interpretation and use of statistics in nursing research. AACN Advanced Critical Care, 19(2), 211–222

Melnyk, B. M., & Fineout-Overholt, E. (2011). Evidence-based practice in nursing & healthcare: A guide to best practice. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins.

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Using probability in public health practice

Order Instructions:

Description and original example of how probability is used in public health practice. Then, explain why using statistics and probabilities derived from a population (as is the practice in public health) could cause problems when applied to individuals in a clinical setting. Finally, differentiate between the focus of clinical practices, such as those of a therapist, pharmacist, RN, or MD, and the focus of public health practitioners. How can probability be applied in public health?

SAMPLE ANSWER

Using probability in public health practice

Probabilities are basically understood as the numbers which reflect the chance that a certain event will take place. Probability is used in public health practice by making inferences or generalizations regarding unknown population parameters (Nikulin, Commenges & Huber, 2009). When a sample from the population of interest has been selected, the characteristic being studied is measured. This characteristic in the sample is then summarized and then inferences would be made about the population basing upon what was observed in the sample. For example, researchers can conduct a study to explore the prevalence of trichomoniasis, also known as T. vaginalis infection which a widespread and curable sexually transmitted disease (STD). The study can be done for a period of 3 years amongst a probability sample of young adults, N= 3,000 in Madison, Wisconsin. From the results obtained from the sample, inferences would be made about the prevalence of trichomoniasis in the general population in the state of Wisconsin and/or the entire United States.

Using statistics and probabilities obtained from a population can cause problems whenever applied to patients in a hospital setting primarily because the public health professionals obtain their results by studying large numbers of patients and their results cannot be used in the clinical setting for a specific individual patient. For instance, public health practitioners make a number of declarations such as: the 6-year survival rate for stage one cervical cancer is 87% in the United States. Public health professionals calculated this figure by observing large numbers of women who were diagnosed with stage one cervical cancer. They then divided the number of survivors at 6 years by the number of those diagnosed. This will allow public health practitioners to compare to survival rates of the other sorts of cancers. Nonetheless, it is not useful in predicting a particular patient’s likelihood of survival for 6 years (Nikulin, Commenges & Huber, 2009).

The focus of public health practitioners is to protect the health of everyone in the community or entire populations; a community could be a town, a state, a neighborhood or even the whole country. Public health practitioners improve and protect the health of communities by means of education, research for injury and disease prevention and promoting healthy lifestyles. Conversely, clinical professionals focus chiefly on treating individuals when they have become injured or ill (Nikulin, Commenges & Huber, 2009).

Reference

Nikulin, M. S., Commenges, D., & Huber, C. (2009). Probability, Statistics and Modelling in Public Health. Cleveland, OH: Springer Publishers.

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Graphical Descriptive Statistics Research Paper

Graphical Descriptive Statistics

Order Instructions:

Analyze the use and graphical representation of statistics reported in a published study on a public health topic

Produce appropriate numerical and graphical descriptive statistics for both categorical and continuous variables using SPSS

Interpret different descriptive statistics outputs, including what distribution they may reflect.

For this week’s Discussion, select two journal articles and analyze how the researchers used graphical descriptive statistics to communicate their findings. Also, look closely at the relationship between levels of measurement and types of graphical descriptions presented.

Brief summary of the two articles you selected. Then, describe the types of graphical descriptive statistics used in these articles. Finally, analyze how the level of measurement and the distribution of the data influenced the type of graphical descriptions presented. Be sure to use APA style when referencing your articles in your posting.

Article “A” Engebretsen, I., Tylleskär, T., Wamani, H., Karamagi, C., & Tumwine, J. (2008). Determinants of infant growth in Eastern Uganda: a community-based cross-sectional study. BMC Public Health, 8, 418–429.

Article “B” Riccardo, F., Khader, A., & Sabatinelli, G. (2011). Low infant mortality among Palestine refugees despite the odds. Bulletin of the World Health Organization, 89(4), 304–311.

SAMPLE ANSWER

Graphical Descriptive Statistics

Some of the two articles that have used descriptive statistics in reaching a conclusion are from Riccardo et al., (2011) and Engebretsen et al., (2008). The publication by Riccardo et al., (2011) was an infant mortality survey, which revealed that high quality perinatal and maternal services should be improved in Palestine. Similarly Engebretsen et al., (2008) examined child morbidity and mortality in sub-Saharan Africa, which revealed that poor household wealth, gender, family size, and age were associated with growth among Ugandan infants. This paper analyzes how the aforementioned publication used descriptive statistics to communicate their ideas.

To begin with, Riccardo et al., (2011) used bar charts, a pie chart, and line graphs to communicate his ideas. For the pie chart, the independent variable was the causes of infant deaths among Palestine refugees (ordinal type of measurement) while the independent variable was the frequency of occurrence (scale type of measurement). For the line graph, the independent variable was years (nominal type of measurement) while the dependent variable was the infant mortality rate (scale type of measurement). For the bar graphs, scale types of measurement acted as the dependent variables while nominal type of measurement acted as the independent variables.

The main graph used by Engebretsen et al., (2008) was a box plot. This owes to the reality that the box plot was meant to identify any outliers of the data used. Descriptive statistics such as the mean and variance were also used in analyzing the data. For instance, the variance was used to check the deviation of the observed variables from the mean. Ultimately, the mean was also used to compare the anthropometric indices according to sex.

References

Engebresten, S, M, I., Tylleskar, T., Wamani, H., Karamagi, C., Tumwine, K. J. (2008).

Determinants of Infant Growth in Eastern Uganda: A community Based Cross-Sectional

Study. BMC Public Health. 8: 418

Riccardo, F., Khader, A., Sabatinelli, G. (2011). Low infant Mortality among Palestine refugees

Despite the Odds. Bulletin of the World Health Organization 2011; 89: 304-311

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Statistics project Assignment Available

Statistics project

Order Instructions:

For this project you are to conduct an Experiment. You will need to provide a detailed description
of your project, highlighting how the data were obtained, the variables used (including units), and
your objective. Your project needs to be typed and plots can be made using any software of your
choice. Only one project (with each member’s name) per group needs to be submitted. After
providing the description, please answer in detail the four questions below. Your project should
include all the observations used.
1. (25%) State your experiment’s objective in terms of the Null and Alternative Hypotheses.
2. (25%) Test your Null hypothesis at the 5% level of significance. Make sure to check that all the
conditions/assumptions have been met.
3. (25%) Construct a 90% confidence interval for the parameter of your null hypothesis.
4. (25%) What is the probability that you observe a value of 0 that is 1 standard deviation greater
than your estimated value (based on your sample) of the parameter.

SAMPLE ANSWER

Statistics project

Introduction

Following the recent trend on the global systolic blood pressure, it became imperative to conduct an experiment with an aim of identifying the factors that cause blood pressure. As a result, an experiment was conducted to identify whether age in years and weight in pounds are significant predictors of an individual’s systolic blood pressure. It is notable that it was important to use data collected from secondary sources because of the difficulties involved in collecting data for some variables. Simply put, it is difficult to collect data on the systolic blood pressure (manually) of individuals because of the instruments used in measuring the same variable. It is undeniable that only professionals in the health and medical industry have the capacity to collect data on systolic blood pressure. It follows that data used for the upcoming analysis was retrieved from (http://college.cengage.com, n.p). This paper uses multiple regression analysis to identify whether age in years and weights in pounds are significant predictors of an individual’s systolic blood pressure.

State your experiment’s objective in terms of the Null and Alternative Hypotheses

In order to complete the experiment successfully, the null and alternative hypotheses were used. In simple terms:

H₀: Age in Years and weight in pounds are not significant predictors of systolic blood pressure.

Against

H₁: Age in Years and weight in pounds are significant predictors of systolic blood pressure.

Test your Null hypothesis at the 5% level of significance. Make sure to check that all the
conditions/assumptions have been met.

It is crucial to point out that the following assumptions were made.

Assumption 1: for i=1, 2…N. this assumption could be understood as the expectation

for the error terms or deviations is assumed to be zero.

Assumption 2: for i=1, 2…N. That is, the variance of the error terms is constant.

This assumption is termed as homoscedasticity, or homogeneity of variances.

Assumption 3: . This could be explained as the error terms have a normal distribution with mean zero and variance.

Assumption 4: There is a linear relationship between the independent variable and the

independent variables.

The following graphs were used to check for the assumptions made in the analysis.

Assumption 1 and 2

It can be seen that the number of observations are balanced in both graphs i.e. the number of observations above and below tend to balance. This implies that taking the average of the error terms will almost be equal to zero (Montgomery, 78). It is also evident the distance from the line Zero does not have any outliers in both graphs. This is an indication that the data has a constant variance.

Assumption 3

The normal probibility plot above clearly indicates that both age in years and weight in pounds follows a normal distribution (Montgomery, 78). Thus the third assumption is fullfilled.

Assumption 4

The plots indicate that both age in years and the weight in pounds have a linear relationship with systolic blood pressure (Montgomery, 79). This is highlighted by the reality that the observations fit on a straight line.

Construct a 90% confidence interval for the parameter of your null hypothesis.

The null hypothesis for the 90 percent confidence interval is that the y intercept minus the coefficient for age minus the coefficient for weight is equal to zero at α=0.10. The following output was obtained after running a multiple linear regression on age and weight on the systolic blood pressure.

Regression Statistics
Multiple R	0.988356
R Square	0.976847
Adjusted R Square	0.971059
Standard Error	2.318211
Observations	11

ANOVA
	df	SS	MS	F	Significance F
Regression	2	1813.916	906.9581	168.7646	2.87357E-07
Residual	8	42.99282	5.374103
Total	10	1856.909

	Coefficients	Standard Error	t Stat	P-value	Lower 90.0%	Upper 90.0%
Intercept	30.9941	11.9438	2.5950	0.0319	8.7841	53.2041
Age in Years	0.8614	0.2482	3.4702	0.0084	0.3998	1.3230
Weight in Pounds	0.3349	0.1307	2.5627	0.0335	0.0919	0.5778

What is the probability that you observe a value of zero that is one standard deviation greater than your estimated value (based on your sample) of the parameter.

From the output above it is clear that the probability of observing zero for age in years is 0.0084. The probability of observing zero for the y intercept is 0.0319 and for weight in pounds is 0.0919. Considering, the level of significance of all the variables, it is evident to conclude that both age and weight are significant predictors of systolic blood pressure. This owes to the reality that both variables have p values, which are less than 0.05.

In conclusion, this paper uses multiple linear regression analysis to identify whether age in years and weights in pounds are significant predictors of an individual’s systolic blood pressure. It is evident that both age and weight are significant predictors of systolic blood pressure.

Works Cited

Montgomery, Douglas C. Introduction to Linear Regression Analysis. Oxford: Wiley-Blackwell, 2011 Print.

Systolic Blood Pressure Data. (n.d). Web. 9 June 2014.

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Statistics project Writing Service Available

Statistics project

Order Instructions:

Using any data of interest to your group, compile a data set comprised of one predictor and one
response variable with at least 20 observations (data points), and answer the questions below.
Your project needs to be typed and plots can be made using any software of your choice. Only one
project (with each member’s name) per group needs to be submitted. Your project should include
all the observations used.

Provide a brief description of your project. Make sure to identify the predictor and response
variables, as well as discussing the objective of your regression model.

1. (20%) All your answers must be in the order in which the questions are asked, otherwise you will be
deducted 20%. Note: Even if only one answer is out of order you will still be deducted 20%.

2. (15%) For your predictor and response variables:
(a) compute the range and IQR.
(b) make a histogram of your data.
(c) make a boxplot of your data.

3. (25%) Make a scatterplot of your data and describe the:
(a) Direction
(b) Form
(c) Strength
(d) Correlation
(g) Outliers

4. (40%) Based on your data, construct a linear regression model of your response variable as a function
of your predictor variable following the steps below:
(a) Compute ¯x and ¯y
(b) Compute sx and sy
(c) Compute r
(d) Compute a and b
(e) Construct the respective Least Squares line and plot it over your scatter plot.
(f) Compute the respective R2 and interpret your results.
(g) For your model, compute and plot the residuals vs x. Describe what you observe from
this plot.
(h) Are there any outliers? If so, are they high leverage and/or influential.
(i) Based on your model, make 3 predictions for your response variable (i.e., use 3 different
values of x that are not in your data, and compute the respective y value

SAMPLE ANSWER

Statistics project

Question One

The data below was obtained from an organization that wanted to estimate the cost of leasing a building given the contract value for constructing the building. It follows that the contract value was the predictor variable while the estimated cost is the response variable.

Estimated cost		Contract value
85,000	310,000	100,000	360,000
70,000	305,000	120,000	370,000
110,000	180,000	150,000	200,000
90,000	170,000	80,000	250,000
130,000	160,000	180,000	300,000
160,000	110,000	190,000	160,000
160,000	150,000	200,000	210,000
280,000	180,000	350,000	230,000
130,000	175,000	180,000	250,000
320,000	180,000	380,000	270,000

Question Two

compute the range and IQR.

Range

Constructed value =380,000-80,000

=300,000

Estimated cost = 320,000-70,000

=250,000

Quartile Range

Constructed value = 300000- 175000

=125000

Estimated cost = 197500- 125000

= 72500

(b) Make a histogram of your data.

(c) make a boxplot of your data.

Question Three

(a) Direction

The direction of a relationship tells whether the values on two variables go up

and down together. The nature of the plot indicates direction. If two variables have a positive direction, then as the values on one variable go up, so do the values on the other variable. The data used has a positive direction because the points of the scatter plots run from the lower left to the upper right. This implies that as the vales of the contract value go up so does the value of the estimated cost and vice versa.

(b) Form

The shape of the plot could explain the form of the scatter plot. This is because there are instances where the plot has a curved shape. In other instances, the plot could have a straight line plot. If there is a linear relationship, then the plot will appear to swarm or cloud in a generally straight and consistent form. The plot above indicates that the data points are straight and consistent. I.e. there is a linear relationship between the estimated cost and the contract value.

Strength

The strength of the relationship between variables is determined by how close the plotted points are from one another. Closely placed points indicate a strong relationship between the variables. In this case, the points are neither close nor far from each other. Therefore, there is a moderate relationship between the variables.

Correlation

The correlation between two variables measures the strength and direction of the relationship between the variables. The strength and direction of the variables have already been established in the previous paragraphs. Therefore, we conclude that there is a moderate positive relationship between the variables.

(g) Outliers

The extreme points in a scatter plot identify outliers. In this case, there are four outliers. The box plot has also demonstrated this.

Question Four

(a) Compute ¯x and ¯y

Mean for estimated cost is given by the sum of all the observations divided by the number of observations.

¯x = 3,455,000/20

=172750

The mean for the contract value is given by the sum of all the observations divided by the number of observations.

¯y =4,530,000/20

=226,500

Compute sx and sy

The standard deviation of the variables is given by taking the square root of the sum of all the deviations from the mean and dividing by the number of observations less by one.

The standard deviation for the estimated cost is

Sd = (107,323,750,000/19) ^1/2

= 75157.2912

The standard deviation for the contract value is

Sd = (209,836,250,000/19) ^1/2

= 105090.4998

Compute r

The correlation coefficient is given by the following formula.

Estimated cost (Y)	Contract value (X)	XY	X²	Y²
85,000	100,000	8500000000	7,225,000,000	10,000,000,000
70,000	120,000	8400000000	4,900,000,000	14,400,000,000
110,000	150,000	16500000000	12,100,000,000	22,500,000,000
90,000	80,000	7200000000	8,100,000,000	6,400,000,000
130,000	180,000	23400000000	16,900,000,000	32,400,000,000
160,000	190,000	30400000000	25,600,000,000	36,100,000,000
160,000	200,000	32000000000	25,600,000,000	40,000,000,000
280,000	350,000	98000000000	78,400,000,000	122,500,000,000
130,000	180,000	23400000000	16,900,000,000	32,400,000,000
320,000	380,000	121600000000	102,400,000,000	144,400,000,000
310,000	360,000	111600000000	96,100,000,000	129,600,000,000
305,000	370,000	112850000000	93,025,000,000	136,900,000,000
180,000	200,000	36000000000	32,400,000,000	40,000,000,000
170,000	250,000	42500000000	28,900,000,000	62,500,000,000
160,000	300,000	48000000000	25,600,000,000	90,000,000,000
110,000	160,000	17600000000	12,100,000,000	25,600,000,000
150,000	210,000	31500000000	22,500,000,000	44,100,000,000
180,000	230,000	41400000000	32,400,000,000	52,900,000,000
175,000	250,000	43750000000	30,625,000,000	62,500,000,000
180,000	270,000	48600000000	32,400,000,000	72,900,000,000
3,455,000	4,530,000	903,200,000,000	704,175,000,000	1,178,100,000,000

= 0.94439147

Compute a and b

a = -6958.173

b = 0.793

(e) Construct the respective Least Squares line and plot it over your scatter plot.

Estimated Cost = -6958.173 + 0.793 contract value

(f) Compute the respective R2 and interpret your results.

= 0.89187525

This implies that 89 percent of the variation in expected cost is explained by the variation in the contract value.

(g) For your model, compute and plot the residuals vs. x. Describe what you observe from this plot.

The residual plot above indicates that the data has a constant and independent variance because the plots are consistent regardless of the contract value. It is also clear that the data follows a normal distribution form the normal probability plot below.

(h) Are there any outliers? If so, are they high leverage and/or influential?

There are outliers in the data but they are neither high leveraged or influential.

Based on your model, make 3 predictions for your response variable

Using the following equation Estimated Cost = -6958.173 + 0.793 contract value

The predicted value for three values is indicated in the table below.

Contract Value	276000	302000	144000
Predicte Estimated Cost	212023.9716	232652.7243	107293.3807

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Statistics for Health Care Research

Complete Exercise 23 and 24 in Statistics for Health Care Research: A Practical Workbook.

Instructions

In order to receive full credit on calculated answers, please show your work. (Use Word’s equation editor, etc., and/or provides a short written description
as to how you obtained the final result.)

There is no minimum requirements for the number of sources you use however as a general guideline an academic paper can have 1 source per hundred words. In regards to the currency of the references, it is generally expected that sources are within 5 years published age. However if you have sourced a reference that is older than this you must demonstrate how it is relevant in your writing.

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Statistics for Health Care Research Paper

Statistics for Health Care Research

Complete Exercise 23 and 24 in Statistics for Health Care Research: A Practical Workbook.
In order to receive full credit on calculated answers, please show your work. (Use Word’s equation editor, etc., and/or provides a short written description
as to how you obtained the final result.)

Your assignment must follow these formatting requirements:

Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.

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