In the Project Assignment, you will solve a set of problems by applying the following three important correlation and regression methodologies

Week 4: Project Assignment due September 10, 2019. You already have the models via emails. Please remember the plagiarism level should be Less than 21%. Thank you Regression and Correlation Methods: Correlation, ANOVA, and Least Squares This is another way of assessing the possible association between a normally distributed variable y and a categorical variable x. These techniques are special cases of linear regression methods. The purpose of the assignment is to demonstrate methods of regression and correlation analysis in which two different variables in the same sample are related. The following are three important statistics, or methodologies, for using correlation and regression:

• Pearson's correlation coefficient
• ANOVA
• Least squares regression analysis

In this assignment, solve problems related to these three methodologies. Part 1: Pearson's Correlation Coefficient For the problem that demonstrates the Pearson's coefficient, you will use measures that represent characteristics of entire populations to describe disease in relation to some factor of interest, such as age; utilization of health services; or consumption of a particular food, medication, or other products. To describe a pattern of mortality from coronary heart disease (CHD) in year X, hypothetical death rates from ten states were correlated with per capita cigarette sales in dollar amount per month. Death rates were highest in states with the most cigarette sales, lowest in those with the least sales, and intermediate in the remainder. Observation contributed to the formulation of the hypothesis that cigarette smoking causes fatal CHD. The correlation coefficient, denoted by r, is the descriptive measure of association in correlational studies. Table 1: Hypothetical Analysis of Cigarette Sales and Death Rates Caused by CHD

State	Cigarette sales	Death rate
1	102	5
2	149	6
3	165	6
4	159	5
5	112	3
6	78	2
7	112	5
8	174	7
9	101	4
10	191	6

Using the Minitab statistical procedure:

• Calculate Pearson's correlation coefficient.
• Create a two-way scatter plot.

In addition to the above:

• Explain the meaning of the resulting coefficient, paying particular attention to factors that affect the interpretation of this statistic, such as the normality of each variable.
• Provide a written interpretation of your results in APA format.

Refer to the Assignment Resources: Dot Plots and Correlation and Resources: Performing Regression Analysis to view an example of Pearson's correlation coefficient. This same resources are also available under lecture Correlation and Regression Methods. Submission Details:

• Name your Minitab output file mtw.
• Name your document SU_PHE5020_W4_A2b_LastName_FirstInitial.doc.
• Submit your document to the Submissions Areaby the due date assigned.

Part 2: ANOVA Let's take hypothetical data presenting blood pressure and high fat intake (less than 3 grams of total fat per serving) or low fat intake (less than 1 gram of saturated fat) of an individual. Table 2: Blood Pressure and Fat Intake

Individual	Blood Pressure	Fat Intake
1	135	1
2	130	1
3	135	1
4	128	0
5	121	0
6	133	0
7	145	1
8	137	1
9	148	1
10	134	0
11	150	0
12	121	0
13	117	1
14	128	1
15	121	0
16	124	1
17	132	0
18	121	0
19	120	0
20	124	0

  Using the Minitab statistical procedure:

• Calculate a one-way ANOVA to test the null hypothesis that the mean of each group is the same.
• Use different variables as grouping variables (fat intake high 1; fat intake low 0) and compare the results.
• Calculate an F-test for an overall comparison of means to see whether any differences are significant.

In addition, in a Microsoft Word document, provide a written interpretation of your results in APA format. Visit the media Resources: One-Way ANOVA on lecture Correlation and Regression Methods to view an example of ANOVA. Submission Details:

• Name your Minitab output filemtw.
• Name your document doc.
• Submit your document to the Submissions Areaby the due date assigned.

Part 3: Least Squares The following are hypothetical data on the number of doctors per 10,000 inhabitants and the rate of prematurely delivered newborns for different countries of the world. Table 3: Number of Doctors Verses the Rate of Prematurely Delivered Newborns

Country	Doctors per 100,000	Early births per 100,000
1	3	92
2	5	88
3	5	85
4	6	86
5	7	89
6	7	75
7	7	70
8	8	68
9	8	69
10	10	50
11	12	45
12	12	41
13	15	38
14	18	35
15	19	30
16	23	6

Using the Minitab statistical procedure:

• Apply least squares analysis to fit a regression line to the data.
• Calculate an F-test and a t-test to test for the significance of the regression.
• Test for goodness of fit using R2.

In addition, in a Microsoft Word document, provide a written interpretation of your results in APA format. Submission Details:

• Name your Minitab output file mtw.
• Name your document doc.
• Submit your document to the Submissions Areaby the due date assigned.

Additional Materials Dot Plots and Correlation Performing Regression Analysis   RESSOURCES In Week 4, you will learn about correlation and regression methods. Correlation and linear regression are the most commonly used techniques for investigating the relationship between two quantitative variables. You will have the opportunity to discuss the use of the correlation coefficient (R2) statistic in regression analysis. You will also practice biostatistical problem-solving skills by applying least squares regression analysis to fit a regression line to the data for a public health scenario. In addition, you will apply an F-test and a t-test for significance of the regression, test for goodness of fit using R2, and give a substantive interpretation of the results. In the Discussion Assignment, you will research and discuss the use of R2 in regression analysis. You will also explain how R2 is substantively interpreted and how interpretation can be affected by sample size. In the Project Assignment, you will solve a set of problems by applying the following three important correlation and regression methodologies:

• Pearson's correlation coefficient
• ANOVA
• Least squares regression analysis

Your Learning Objectives for the Week: At the end of this week you should be able to:

• Apply the ANOVA technique for single and multiple comparison models.
• Calculate post hoc comparisons from an ANOVA analysis.
• Make inferences about the linear correlation coefficient.
• Understand and apply linear regression analysis techniques.
• Understand and apply multiple linear regression analysis.
• Understand ANOVA for multiple linear regression.
• Apply multiple and nonlinear regression analysis techniques.
• Apply critical-thinking skills to the analysis and interpretation of biostatistical techniques.

 

ANSWER. PAPER DETAILS
Academic Level	Masters
Subject Area	Nursing
Paper Type	Essay
Number of Pages	4 Page(s)/1100 words
Sources	0
Format	APA
Spacing	Double Spaced

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