Descriptive Statistics Scenario Paper

Descriptive Statistics Scenario Paper Consider this scenario: You are on the Board of Directors of the National Parkinson’s Association. Two different research labs are working on medications to decrease the debilitating effects of the disease.

Your responsibility is to review and evaluate both of the results from the Descriptive Statistics Scenario Paper research studies and make recommendations for future funding. You can only fund one of the two. Patients and their families are anxiously waiting for the Board of Directors to make a decision on which of the two can be funded.
Part I.
Your first task it to do the following calculations for both of the results that have been submitted to you.
Mean
Median
Mode
Range
Standard Deviation (are there outliners you should consider?)
Number of months that patients went without experiencing a debilitating symptom:
Group I results = 4, 5, 3, 5, 6, 1, 2, 22, 3, 2, 5, 3
Group II results = 4, 5, 6, 8, 10, 5, 18, 1, 7, 4, 5, 6
Part II.
After you have done this comparison, write a letter to the rest of the members of the board stating which group you are recommending to receive additional funding to support their research. This should be done in Memo Format and you need to discuss your findings and compare them. You can include a table to show the comparison if you want to. Do not be concerned with the small number used in each group in the study.
You need to present and compare the finding and explain WHY you have chosen the one you have. There is not really a right or wrong answer to Descriptive Statistics Scenario Paper. I know which one I would recommend and why – but that does not mean you would interpret the findings as I did. Your statistics should be the same – math is math, but your decision might be different than mine or your peers’.
So, explain why based upon the statistics you have calculated. There are many good on-line Central Tendency calculators and some of you may be a proficient with a spreadsheet.

Descriptive Statistics Scenario Paper Assignment Expectations

Please read before completing Descriptive Statistics Scenario Paper assignments.
Copy the actual Descriptive Statistics Scenario Paper assignment with instructions and questions from this page onto the cover page of your paper (do this for all papers in all courses).
Assignment should be 1 – 2 pages in length (double-spaced).
Please use major sections corresponding to the major points of the assignment, and where appropriate use sub-sections (with headings).
Remember to write in a Scientific manner (try to avoid using the first person except when describing a relevant personal experience).
Quoted material should not exceed 10% of the total paper (since the focus of these Descriptive Statistics Scenario Paper assignments is on independent thinking and critical analysis). Use your own words and build on the ideas of others.
When material is copied verbatim from external sources, it MUST be properly cited. This means that material copied verbatim must be enclosed in quotes and the reference should be cited either within the text or with a footnote.
Credible professional sources are used (for example, government agencies, nonprofit organizations, academic institutions, scholarly journals). Wikipedia is not acceptable.
Cite all references in APA style.

Statistical Analysis on Sets of Sports Data

Statistical Analysis on Sets of Sports Data I’m looking for a statistician to do analysis on sets of sports data.
Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required. Mostly work leading up to and during the World Cup.

Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required. Statistical Analysis on Sets of Sports Data
Mostly work leading up to and during the World Cup. Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required.
Mostly work leading up to and during the World Cup. Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required. Statistical Analysis on Sets of Sports Data
Mostly work leading up to and during the World Cup. Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required.
Mostly work leading up to and during the World Cup. Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required. Statistical Analysis on Sets of Sports Data
Mostly work leading up to and during the World Cup. Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required.
Mostly work leading up to and during the World Cup. Statistical Analysis on Sets of Sports Data Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required.
Mostly work leading up to and during the World Cup. Statistical Analysis on Sets of Sports Data Deep knowledge of football/soccer and previous experience with sports data, such as shots on goal, offsides, free kicks etc. is required. Statistical Analysis on Sets of Sports Data
Mostly work leading up to and during the World Cup.

Quantitative Reasoning Descriptive Statistics

Quantitative Reasoning Descriptive Statistics Consider this scenario: You are on the Board of Directors of the National Parkinson’s Association.

Two different research labs are working on medications to decrease the debilitating effects of the disease. Quantitative Reasoning Descriptive Statistics Your responsibility is to review and evaluate both of the results from the research studies and make recommendations for future funding. You can only fund one of the two. Patients and their families are anxiously waiting for the Board of Directors to make a decision on which of the two can be funded.

Quantitative Reasoning Descriptive Statistics Part I

Your first task it to do the following calculations for both of the results that have been submitted to you.
Mean
Median
Mode
Range
Standard Deviation (are there outliners you should consider?)
Quantitative Reasoning Descriptive Statistics Number of months that patients went without experiencing a debilitating symptom:
Group I results = 4, 5, 3, 5, 6, 1, 2, 22, 3, 2, 5, 3
Group II results = 4, 5, 6, 8, 10, 5, 18, 1, 7, 4, 5, 6

Quantitative Reasoning Descriptive Statistics Part II

After you have done this comparison, write a letter to the rest of the members of the board stating which group you are recommending to receive additional funding to support their research. Quantitative Reasoning Descriptive Statistics This should be done in Memo Format and you need to discuss your findings and compare them. You can include a table to show the comparison if you want to. Do not be concerned with the small number used in each group in the study.
You need to present and compare the finding and explain WHY you have chosen the one you have. There is not really a right or wrong answer. I know which one I would recommend and why – but that does not mean you would interpret the findings as I did. Your statistics should be the same – math is math, but your decision might be different than mine or your peers’.
So, explain why based upon the statistics you have calculated. Quantitative Reasoning Descriptive Statistics There are many good on-line Central Tendency calculators and some of you may be a proficient with a spreadsheet.

Probability and Statistics for Engineers

Probability and Statistics for Engineers This is an individual assignment. No collaboration among students is allowed.

Probability and Statistics for Engineers Use Excel, Minitab, R or any other software of your choice to analyze the data provided in excel file.
Minitab tutorials are available in the MeetMinitab file on the course homepage in webcourses. Probability and Statistics for Engineers Use Excel, Minitab, R or any other software of your choice to analyze the data provided in excel file.
Minitab tutorials are available in the MeetMinitab file on the course homepage in webcourses. If you decide to use Excel, ensure that the Analysis ToolPak add-in is present.
Visit: https://www.excel-easy.com/data-analysis/analysis-toolpak.html if you don?t have it.
Every graph must have appropriate titles and subtitles (if necessary).
Numerical answers must be given to 3 decimal places.
Show all workings clearly and logically. Probability and Statistics for Engineers
No hardcopy of the project will be accepted. Only MS World and .PDF file submissions will be accepted and all submissions must be made in the webcourses.? The questions are worth 98 points in total and remaining 2 points will be awarded to those with professional presentation.
This is an individual assignment. No collaboration among students is allowed.

Probability and Statistics for Engineers Use Excel, Minitab, R or any other Software

analyze the data provided in excel file.
Minitab tutorials are available in the MeetMinitab file on the course homepage in webcourses. If you decide to use Excel, ensure that the Analysis ToolPak add-in is present.
Visit: https://www.excel-easy.com/data-analysis/analysis-toolpak.html if you don?t have it. Probability and Statistics for Engineers
Every graph must have appropriate titles and subtitles (if necessary).
Numerical answers must be given to 3 decimal places.
Show all workings clearly and logically.
No hardcopy of the project will be accepted. Only MS World and .PDF file submissions will be accepted and all submissions must be made in the webcourses.? Probability and Statistics for Engineers The questions are worth 98 points in total and remaining 2 points will be awarded to those with professional presentation.
This is an individual assignment. No collaboration among students is allowed. Probability and Statistics for Engineers

Optional Activity Data Set Statistics Study

Optional Activity Data Set Statistics Study Use the Optional Activity Data Set to list the various statistical tests that can be used to compare the groups based on gender or age.

Answer the following question:
•What statistical analysis can be done to see the effect of both age and gender on time spent completing homework?
Justify your answer, remembering the sample size and data type
Read the University of Phoenix Material: Ballard Integrated Managed Services, Inc., Part 2. Your team acts as a consultant group that analyzes and interprets this second set of data. The intent is to increase senior management’s understanding of the sources of employee dissatisfaction and to create a model that predicts employee resignation.
Combine your Week Two Learning Team assignment and Week Three findings with Week Five findings and make a recommendation to BIMS.
Use the statistical tables given in the appendices of the textbook and a statistical analysis application: a Microsoft® Excel® spreadsheet, Minitab® statistical software, or SPSS™ software.
Prepare a 1,050- to 1,750-word written report along with a 7- to 9-slide Microsoft® PowerPoint® presentation for the senior management team to present your findings (see Exhibit D for the data set of the second survey).
Note. As consultants to BIMS, your Learning Team is expected to prepare and deliver a professional product addressing the client’s needs. Optional Activity Data Set Statistics Study Use the Optional Activity Data Set to list the various statistical tests that can be used to compare the groups based on gender or age. Optional Activity Data Set Statistics Study

Optional Activity Data Set Statistics Study Questions

Answer the following question:
•What statistical analysis can be done to see the effect of both age and gender on time spent completing homework?
Justify your answer, remembering the sample size and data type
Read the University of Phoenix Material: Ballard Integrated Managed Services, Inc., Part 2. Your team acts as a consultant group that analyzes and interprets this second set of data. The intent is to increase senior management’s understanding of the sources of employee dissatisfaction and to create a model that predicts employee resignation. Optional Activity Data Set Statistics Study
Combine your Week Two Learning Team assignment and Week Three findings with Week Five findings and make a recommendation to BIMS.
Use the statistical tables given in the appendices of the textbook and a statistical analysis application: a Microsoft® Excel® spreadsheet, Minitab® statistical software, or SPSS™ software.
Prepare a 1,050- to 1,750-word written report along with a 7- to 9-slide Microsoft® PowerPoint® presentation for the senior management team to present your findings (see Exhibit D for the data set of the second survey).
Note. As consultants to BIMS, your Learning Team is expected to prepare and deliver a professional product addressing the client’s needs. Optional Activity Data Set Statistics Study

Analyzing Data Results using Descriptive Statistics

Analyzing Data Results using Descriptive Statistics http://www.coursesmart.com/SR/2314643/9780135003329/209?__hdv=6.8
This paper is about how you might analyze the data of the topic of my paper. For example, I would collect survey data from the participants and analyze the
results using descriptive statistics.

Describe which descriptive statistics you would use specifically and give examples using the textbook I linked, look at the address I gave. Describe in detail why you selected this method and specifically why you chose these types of descriptive statistics. Use your text to support your ideas. Citations are expected in this assignment from your textbook, as well as a reference page. Use correct APA formatting in your paper.
Remember that you need to use quantitative analysis for this assignment with either descriptive statistics or inferential statistics.
The topic of my research will be uploaded as a document so you can see what kind of research I would like to talk about. It is based on English Language
Learners.
Also I meant to choose 1 source, not 2. So please use the textbook as a source.

SPSS Hypothesis Testing Assignment Available

SPSS Hypothesis Testing

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please do my assignment by nest week please.

SAMPLE ANSWER

Module 6 Application Assignment Worksheet

SPSS Hypothesis Testing

Instructions

For this assignment, you perform a two-sample independent t-test, an ANOVA, and a correlation analysis related to the data set that has been utilized in the previous two modules. Import the data into SPSS or, if you correctly saved the data file from the Module 4 and 5 Assignments, you may open and use that saved file to complete this Assignment. Type your answers to all questions directly into the worksheet, and paste the required output at the end of this document.

Submit this Application Assignment by Day 7 of Week 11.

Research Scenario

A researcher is interested in the effect of a new medication on serum cholesterol, HDL cholesterol, and glycosylated hemoglobin of adults. The researcher randomly selects a sample of 40 (20 male and 20 female) participants who have been diagnosed with high cholesterol. Assuring equal distribution of males and females, the participants are randomly assigned to one of two conditions (or groups): Following pretest measures of serum cholesterol (chol), high-density lipoprotein cholesterol (HDL), and glycosylated hemoglobin (glyhb), the experimental group (Group 1) is given the medication for a period of 6 months while the control group (Group 2) is given a placebo. After the 6 months,chol, HDL, and glyhb are again measured.

The post test data for each participant are provided in the Module 4 Application Assignment Data Set Excel file and can be found in the Module 6 Learning Resources. The codebook for the data provided is as follows:

AGE Age in years

SEX 1 =male, 2=female

GROUP 1 =medication, 2=placebo

CHNG_CHOL change in cholesterol from pretest to posttest

HDL High-density lipoprotein at posttest

GLYHB Glycosylated hemoglobin at posttest

———————————————————————————————————————

Step 1: Import the Microsoft Excel data file into SPSS or use the correct saved SPSS data file as noted in the instructions above.

Step 2: Conduct an independent samples t-test to determine if there is a difference between Group 1 (medication) and Group 2 (placebo) in terms of changes in cholesterol values. Note that the independent variable is GROUP, and the dependent variable is CHNG_CHOL .For this analysis, choose a two-tailed test of significance. (Be sure to save your output.)

Step 3: Conduct a between-subjects ANOVA to determine if there is a difference between sex (males vs. females) and HDL. Note that the independent variable is SEX, and the dependent variable is HDL. For this analysis, choose a two-tailed test of significance. (Be sure to save your output.)

Step 4: Conduct a Pearson correlation to determine if there is a relationship between HDL and GLYHB. For this analysis, choose a two-tailed test of significance. (Be sure to save your output.)

Step 5: Review your SPSS output and answer each of the following questions:

From the independent samples t-test output:

What is the mean CHNG_CHOL for Group 1? _________-5.95
What is the CHNG_CHOL standard deviation for Group 1? _________095
What is the mean CHNG_CHOL for Group 2? _________-.45
What is the CHNG_CHOL standard deviation for Group 2? _________395
What is the calculated t-score (equal variances assumed)? _________– 5.376
What is the probability that the obtained t-score was simply due to chance as opposed to actual gender differences [see “Sig (two-tailed)” on output]? _________00
If the probability associated with the obtained t-score is <0.05, we assume the results (difference in mean CHNG_CHOL between groups) are much more likely due to the effects of the medication than to chance. In other words, we would say the results are statistically significant. Are the results statistically significant (yes or no)? _________No

From the ANOVA output:

What is the mean HDL for group males? _________85
What is the HDL standard deviation for males? _________788

What is the mean HDL for group females? _________30

What is the HDL standard deviation for females? _________477
What is the calculated F-value? __________251
What is the probability (noted as “Sig” on output) that the obtained F-value was simply due to chance as opposed to actual gender differences? __________01

From the correlation output:

What is the Pearson correlation score for HDL and GLYHB? _________– 587
What is the direction of the correlation value and what does this mean? _____ The direction of the correlation is negative. This implies that as HDL increase, GLYHB decreases and vice versa is true
What is the probability for the obtained Pearson correlation score [see “Sig (two-tailed)” on output]? _significant at 0.01 (2-tailed)
If the probability associated with the Pearson correlation is <05, we assume a significant relationship. Is there a significant relationship between HDL and GLYHB? _________No

Step 6: Paste all required SPSS output below.

T test

Group Statistics
	GROUP	N	Mean	Std. Deviation	Std. Error Mean
CHNG_CHOL	1	20	-5.65	4.095	.916
CHNG_CHOL	2	20	-.45	1.395	.312

Independent Samples Test
		Levene’s Test for Equality of Variances		t-test for Equality of Means
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
CHNG_CHOL	Equal variances assumed	13.247	.001	-5.376	38	.000	-5.200	.967	-7.158	-3.242
	Equal variances not assumed			-5.376	23.349	.000	-5.200	.967	-7.199	-3.201

One way ANOVA

Descriptive
HDL
	N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean		Minimum	Maximum
	N	Mean	Std. Deviation	Std. Error	Lower Bound	Upper Bound	Minimum	Maximum
males	20	40.85	7.788	1.742	37.20	44.50	28	56
females	20	49.30	7.477	1.672	45.80	52.80	39	69
Total	40	45.08	8.666	1.370	42.30	47.85	28	69

ANOVA
HDL
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	714.025	1	714.025	12.251	.001
Within Groups	2214.750	38	58.283
Total	2928.775	39

Correlations
		HDL	GLYHB
HDL	Pearson Correlation	1	-.587^**
	Sig. (2-tailed)		.000
	N	40	40
GLYHB	Pearson Correlation	-.587^**	1
	Sig. (2-tailed)	.000
	N	40	40
**. Correlation is significant at the 0.01 level (2-tailed).

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SPSS Summary Statistics Assignment Help

SPSS Summary Statistics

Order Instructions:

Kindly review the attached

SAMPLE ANSWER

Module 5 Application Assignment Worksheet

SPSS Summary Statistics

Instructions

For this assignment, you create and compute summary statistics for a dataset provided as a Microsoft Excel file. Import the data into SPSS and then calculate the summary statistics, including the mean, median, mode, range, range, and the standard deviation as instructed below. Note: If you correctly saved the data file from Module 4 assignment, you may open and use that saved file to complete this assignment. Type your answers to all questions directly into the worksheet, and paste the required summary statistics output at the end of this document.

Submit this Application Assignment by Day 7 of Week 9.

Research Scenario

A researcher is interested in the effect of a new medication on serum cholesterol, HDL cholesterol, and glycosylated hemoglobin of adults. The researcher randomly selects a sample of 40 (20 male and 20 female) participants who have been diagnosed with high cholesterol. Assuring equal distribution of males and females, the participants are randomly assigned to one of two conditions (or groups): Following pretest measures of serum cholesterol (CHOL), High density lipoprotein cholesterol (HDL), and glycosylated hemoglobin (GLYHB), the experimental group (group 1) is given the medication for a period of six months while the control group (group 2) is given a placebo. After the six months, CHOL, HDL and GLYHB are again measured.

The post-test data for each participant are provided in the data set “Module 4, 5, and 6 applic assign data_Cholesterol etc” and can be found in the module learning resources. The codebook for the data provided is as follows:

AGE Age in years

SEX 1 =male, 2=female

GROUP 1 =medication, 2=placebo

CHNG_CHOL change in cholesterol from pre-test to post-test

HDL High density lipoprotein at post-test

GLYHB Glycosylated hemoglobin at post-test

———————————————————————————————————————

Step 1: Import the Microsoft Excel data file into SPSS or use the correct saved SPSS data file as noted in the instructions, above.

Step 2: Run descriptive/summary statistics (mean, median, mode, standard deviation, and range) for both groups 1 and 2 (medication and placebo), combined for each of the following: AGE, CHNG_CHOL,HDL, and GLYHB (be sure to save your output)

Step 3: Separate the data file by group

Step 4: Run descriptive/summary statistics for each group separately for each of the following: AGE, CHNG_CHOL,HDL, and GLYHB (be sure to save your output)

Step 5: Review your SPSS output and answer each of the following questions:

QUESTION	type answers below
For groups 1 and 2 (medication and placebo) combined:
1. What is the mean for AGE?		_____37.18___________
2. What is the median for AGE?		______37.50__________
3. What is the standard deviation for AGE?		_12.262_______________
4. What is the mean for CHANGE_CHOL?		-3.05________________
5. What is the median for CHANGE_CHOL?		-2.0________________
6. What is the standard deviation for CHANGE_CHOL?		_4.006_______________
7. What is the mean for HDL?		__45.08______________
8. What is the median for HDL?		___45_____________
9. What is the standard deviation for HDL?		____8.66____________
10. What is the mean for GLYHB?		____4.96____________
11. What is the median for GLYHB?		___4.79_____________
12. What is the standard deviation GLYHB?		____0.88____________
For group 1 (medication) only:
13. What is the mean for CHANGE_CHOL?		___- 5.65_____________
14. What is the median for CHANGE_CHOL?		_- 5.00_______________
15. What is the standard deviation for CHANGE_CHOL?		____4.095____________
16. What is the range for CHANGE_CHOL?		____17____________
For group 2 (placebo) only:
17. What is the mean for CHANGE_CHOL?		______- 45__________
18. What is the median for CHANGE_CHOL?		____________- 50____
19. What is the standard deviation for CHANGE_CHOL?		__________1.395______
20. What is the range for CHANGE_CHOL?		_____________5___

Step 6: Paste all required SPSS output below.

Data output for combined group 1 & 2

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
AGE	40	17	65	37.18	12.262
SEX	40	1	2	1.50	.506
CHNG_CHOL	40	-16	2	-3.05	4.006
GLYHB	40	3.41	7.72	4.9665	.87109
HDL	40	28	69	45.08	8.666
Valid N (listwise)	40

Data output for CHNG_CHOL by GROUP Case Processing Summary
	Cases
	Included		Excluded		Total
	N	Percent	N	Percent	N	Percent
CHNG_CHOL * GROUP	40	100.0%	0	0.0%	40	100.0%

Report
CHNG_CHOL
GROUP	N	Mean	Median	Std. Deviation	Range
Experimental	20	-5.65	-5.00	4.095	17
Control	20	-.45	-.50	1.395	5
Total	40	-3.05	-2.00	4.006	18

Data output for all variables in Control and Experimental group separately

Case Processing Summary
		Cases
		Included				Excluded				Total
		N		Percent		N		Percent		N		Percent
AGE * GROUP		40		100.0%		0		0.0%		40		100.0%
SEX * GROUP		40		100.0%		0		0.0%		40		100.0%
CHNG_CHOL * GROUP		40		100.0%		0		0.0%		40		100.0%
HDL * GROUP		40		100.0%		0		0.0%		40		100.0%
GLYHB * GROUP		40		100.0%		0		0.0%		40		100.0%
Report
GROUP			AGE		SEX		CHNG_CHOL		HDL		GLYHB
Experimental	N		20		20		20		20		20
	Mean		41.45		1.50		-5.65		50.15		4.4085
	Median		43.00		1.50		-5.00		50.00		4.4950
	Std. Deviation		12.630		.513		4.095		7.300		.36357
Control	N		20		20		20		20		20
	Mean		32.90		1.50		-.45		40.00		5.5245
	Median		33.50		1.50		-.50		40.00		5.2000
	Std. Deviation		10.518		.513		1.395		6.829		.87742
Total	N		40		40		40		40		40
	Mean		37.18		1.50		-3.05		45.08		4.9665
	Median		37.50		1.50		-2.00		45.00		4.7900
	Std. Deviation		12.262		.506		4.006		8.666		.87109

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Inferential statistical analysis Assignment

Inferential statistical analysis

Order Instructions:

Conducting inferential statistical analysis

In examining the different inferential statistical methods, consider their fundamental characteristics, underlying assumptions, strengths, weakness and suitability for producing generalisations regarding an unbeknown population.

SAMPLE ANSWER

Inferential Statistical Analysis

Inferential statistical methods are the critical component of testing and estimating different parameters of statistical hypotheses. Primarily, unlike descriptive statistics that mainly focus on analyzing data and summarizing the result in a meaning way, inferential statistical methods distinguish between a study population and a sample thereby assessing the strength and weakness of the relationship between dependent variables and independent variables (Dien, 2010. p. 142). Based on unknown population or statistical data that do not comply with inferential statistical criteria, different inferential statistical methods enable determination of the strength and weakness of sample relationships. According to Fernández and Hermida (1998), inferential statistical methods enable characteristics probability exploration of the population under study based on sample characteristics.

On a broad front, the fundamental characteristics of inferential statistics rely heavily on the means difference tests (t-tests), analysis of variance and sophisticated statistical model application. However, the primary characteristics of inferential statistics are reflected on the individual type of inferential statistic methods such as multiple-variate regression, bivariate regression, confidential interval, one sample hypothetical test, t-test or ANOVA, Chi-Square Statistic and contingency table as well as the Pearson correlation. Kalbfleisch and Prentice (2011.p.147) states that as ANOVA or t-test provide continuous and categorical variables characteristics of the unknown population data generation, confidence interval provide basic features of estimated scores or values in that population sample.

Comparatively, the underlying assumptions of different inferential statistical methods during the production of data generation in the unknown population are usually based on three different conditions. Before analysis inferential statistics, a complete list of the participants of the unknown population has to be provided for analysis. Besides, assumptions are made that a random sample has been drawing from the unknown population. Similarly, it’s pre-assumed that the sample size of the unknown population is large enough advocated by Box and Tiao (2011.p.167) theoretical arguments.

The suitability of applying different inferential statistical methods in data generation of unknown populations is multiple. Inferential statistical methods, unlike descriptive statistical methods, provide reliable and validity of the unknown population study findings and results since they can determine and demonstrate the strength of the relationships of the study samples by assessing the impacts and outcomes of the study variables (Huitema, 2011.p. 49). Besides, the inferential statistical methods are suitable for determining the unknown population characteristics probabilities, generalizing findings and the results of larger and unknown population and comparing the responses of the unknown population participants (Fernández and Hermida, 1998.p.198).

The strength of applying different inferential statistical methods in data generation of unknown populations is that they can analyze and describe with data transformation provides larger predictions for an unknown set of data population. Besides, inferential statistical methods can provide confident of the predicted outcome within a specified range based on categorical and continuous variables as described by Kalbfleisch and Prentice (2011.p.39). Therefore, more mathematical standardization is applied in inferential methods to provide valid and reliable data for unknown population compared to descriptive statistical methods. The weakness of inferential statistical methods in producing the generalization of an unknown population is that the fundamental conditions and assumptions have to be made for the inferential statistical methods to be applicable (Hayes and Scharkow, 2013.p.63). However, inferential statistical methods needs basic mathematical skills for the method to be utilized effectively since multiple mathematical manipulations may expose the findings to multiple statistical errors (Dien, 2010.p.146).

Bibliography

Box, G.E. and Tiao, G.C., 2011. Bayesian inference in statistical analysis (Vol. 40). John Wiley & Sons.

Dien, J., 2010. The ERP PCA Toolkit: An open source program for advanced statistical analysis of event-related potential data. Journal of neuroscience methods, 187(1), pp.138-145.

Fernández, J.R. and Hermida, R.C., 1998. Inferential statistical method for analysis of nonsinusoidal hybrid time series with unequidistant observations. Chronobiology international, 15(2), pp.191-204.

Hayes, A.F. and Scharkow, M., 2013. The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis does method really matter?. Psychological Science, p.0956797613480187.

Huitema, B., 2011. The analysis of covariance and alternatives: Statistical methods for experiments, quasi-experiments, and single-case studies (Vol. 608). John Wiley & Sons.

Kalbfleisch, J.D. and Prentice, R.L., 2011. The statistical analysis of failure time data (Vol. 360). John Wiley & Sons.

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Statistics for the Life Sciences Essay Paper

Statistics for the Life Sciences Essay Paper Order Instructions: This is my order I just talked about.

Statistics for the Life Sciences Essay Paper Sample Answer

	Treadmill	Exercise Bike	Total
Total sold	185	123	308
Service contract	67	55	122
Total	252	178

The confidence interval (C.I) for the difference in proportion is obtained using the formula:

C.I _diff = , where , and

The proportion of service contract sold on the treadmill is 67/122, whereas the proportion of those sold the exercise bike is 55/122.

Statistics for the Life Sciences Essay Paper

Therefore, to construct the confidence interval we need p1, p2, n1, n2, standard error of the difference, and the z_α.

Thus, let p₁= 67/122, n₁= 122, and p₂ = 55/122, n₂ = 122

D = =

SE _p1– p₂ =

CI = .

Key to note, the confidence interval is as effective as the test of hypothesis (ANOVA or T-test), for it can be used in determining whether two averages or proportions are statistically significant. The constructed confidence interval implies that we are 95% confident that the proportion difference will lie between -0.0265, and 0.2232. Notably, the constructed confidence interval contains zero, which means that the difference between the proportion of service contract sold on the treadmill and the proportion of those sold on the exercise bike is not significantly different from zero. In other words, the two sales are not statistically different at the 95% level of significance. In simple terms, this deduces that in the past six months, the Service contract sold using the treadmill and exercise bike was not significantly different. That is to say, the two channels of distributions are equally effective in the sale of the service contract. Therefore, when the firm is advertising, they need to emphasize about the two selling techniques equally.

Statistics for the Life Sciences Essay Paper References

Samuels, M. L., Witmer, J. A., & Schaffner, A. (2012). Statistics for the life sciences. Pearson Education. From http://law.qu.edu.qa/artssciences/mathphysta/stats/syllabi/fall2012/Stat_151-kassim.pdf