Innis E. Bryant, Sr. School of Business, Northcentral University BUS 7105: Signature Paper-Presentation for Statistical Analysis in Research Dr. Riyad Abubaker September 18, 2022   .

extreme close up of line chart graphic. Innis E. Bryant, Sr. School of Business, Northcentral University BUS 7105: Signature Paper-Presentation for Statistical Analysis in Research Dr. Riyad Abubaker September 18, 2022 ..

[Audio] A hypothesis can be defined as a sensible, measurable inference about the state of a subject in question that is based on behavioral outcomes data. Moreover, it can either be accepted or provisional in kind and never both at the same time. An example of a quantitative hypothesis would be: student achievement on standardized tests is negatively impacted by the level of anxiety they feel ante-neo-and post-examination. Several variables are observed when deconstructing the hypothesis: 1- negative and 2- impact. Negative impact inherently carries inversely proportional values that have to be analyzed in order to confirm or rule out the stated hypothesis..

[Audio] Testing decisions are made using procured data; Testing blends the notion of the theoretical framework to the question of the study; Tabulates the values of validity and reliability of the investigative processes; Supplies the foundation on which the validity of the study is based so that it can either be proven or ruled out..

[Audio] The Pearson Correlation helps a statistician identify the linear associations between to distinctive variables. With the values of negative one and positive one, the negative one stands for the negative correlation , while the zero stands for no correlation whatsoever. The positive one stands for the positive correlation..

[Audio] In the Pearson Correlation of the introverted vs extroverted personality type. Of the 504 samples, 73 were housed in the extroverted frequency domain, while the remainder 431 belonged in the introverted slants. Of the sampled data, valid percent utilized represents the total percent when all, if any, data were excluded the calculation. This value is to be reported unless there are substantive missing data that present as outliers. In this instance, the percent and valid percent of samples are identical..

[Audio] In the job satisfaction correlation, there were 504 frequencies, with 39 belonging to the very satisfied domain, while 465 scaled up form dissatisfied status. Of the frequencies reported, the ratio of percent and valid percent are symmetrical, and the cumulative percent values across the set added to 100%. Of those, over half was found midway between dissatisfied and very satisfied..

[Audio] In looking at the Pearson Correlation of job satisfaction and personality, there was no discrepancy in missing data, while the mean of the two were only difference in 0.69 between the two, 1.0 between their medians, and 2.0 in the relationship of their modes. While bootstrapping can be performed for mean and median, it is not necessary to do that in this instance. Another way to mitigate this problem is to use Mood's Median Test. Either metric will lead to another query which will lead a statistician to widening the sample size otherwise called resampling..

[Audio] In this correlation test, the intent was to evaluate the null hypothesis against its natural alternative where the mean does not equate to 50. In the instance where the p value is small, a statistician can concluded that they have a strong likelihood of rejecting the null hypothesis..

[Audio] The next step sis to evaluate the association between the job satisfaction and engagement. As exemplified earlier, of the 504 value, close to 2/3 of the samples were in the low to medium engagement level response, with only 27 recurrences in the high domain and the remainder of 477 bellowing in the ascending scale from low to moderate levels..

[Audio] Correlation shows no missing data that impacted the validity between the two values of job satisfaction and engagement, with the mean being nearly 1.0 lesser in engagement domain, while the median aided with the job satisfaction domain. The modality of values shows 1 one point difference respectively..

[Audio] In this instance, Pearson Correlation of 1 stands for the two variables that are absolutely linearly associated. In the second instance, the Pearson Correlation of 0.412 is not perfect, but it is relatively strong..

[Audio] In the next evaluation the leader trust and engagement are looked at where H0 stands for the trust in leadership and H1 stands for dissociation..

[Audio] In this example missing data were considered, which made up 1.2% of samples. Nearly 77% of the derivations showed that people generally have some trust in their leaders in order for them to be engaged in satisfied at work..

[Audio] In the ratio of Leader Trust and Engagement levels, Leader trust had missing data. The Mean difference was nearly one point on the side of the engagement domain, Median on the side of the engagement domain, and one point on the side of the engagement domain. It is evident that trust in leader is impacting the work place..

[Audio] In Pearson correlation study, the engagement was below the threshold of 1 for perfect association..

[Audio] In reviewing the dependent and independent variables, personality is a dependent variable and gender is an independent variable..

[Audio] The sample test procured frequency of 195 form male and 390 from female particiapnts..

[Audio] In comparison to the gender and personality, there were no missing data identified; however, gender varied starkly from the personality by over 3 points in the mean difference, on the side of the personality, 4 points on the side of the personality, and 5 points on the side of the mode..

[Audio] The standard deviation differed by 0.020-siding with females and mean differed by 0.12-siding with females..

[Audio] The standard error difference is trivial between assumed and not assumed variances, equal in mean difference, and only 2 thousandths difference in the not assumed domain..

[Audio] In the ANOVA, dependent variable was job satisfaction and independent variable was level of education..

[Audio] Of the 504 samples, nearly 2/3 of the samples had a degree, and 1/3 holding a high school diploma. As such, 95% confidence interval houses true values of the mean per educational domain of the population. The standard deviation between each domain measures the spreading cast of the dataset in relation to the mean..

[Audio] In the homogeneity test, one compares the proportions of the response between two or more population sources and takes into consideration dichotomous values ( 0- 1, yes-no, male female, no degree-has degree, etc.) To see if there are significant differences between the groups, ANOVA helps delineate those variance by looking at the within-group and between-group values and the spans the difference of the means between these groups..

-. -. CONCLUSION. Statistics provides a solid room for examining the factors that add or deduct from a Desired end state goal ( Adnikari , 2020). Following are the ways this can be accomplished: -Developing appropriate statistical confidence is key ( Ad nikari , 2020) -Widening the cast of the net, a statistician can procure a greater scope of data s amples that can add value to the topic at hand analysis. -Maximize efforts by confining variability interferons as this can add value When approximating the population parametric scale. -Utilization of mono-sided confidence intervals and diminishing confidence level ( Chukrova , 2021).

Adnikari, S. (2020). Hypothesis in Research: Definition, Types And Importance ! Retrieved from https://www.publichealthnotes.com/hypothesis-in-research-definition-types- and- importance/#:~:text=Importance %20of%20Hypothesis%3A&text=It%20helps%20to%20provide%20link,the%20validity%20of%20the %20research Choudhary, R. (2018). Application of “independent t-test” by using SPSS for conducting physical education researches. International Journal of Physical Education, Sports and Health, 5(1): 237- 241. Retrieved from https://www.kheljournal.com/archives/2018/vol5issue1/PartD/5- 4- 24-510.pdfw Chukrova, N. (2021). Fuzzy hypothesis testing: Systematic review and bibliography. Applied Soft Computing 106 . Retrieved from https://doi.org/10.1016/j.asoc.2021.107331.

Dubois, S. (2021). The Importance of Hypothesis Testing. Retrieved from https://sciencing.com/the- importance- of- hypothesis-testing-12750921.html Weiers, R. (2011). Introduction to Business Statistics. (7. Ed.). Mason, OH: South- Eastern Cengage Learning Yunke, Z.,Yongli,L, Jinzhao S, Xiaolong, CH., Yan,Lu., and Weikang, W. (2019). Pearson correlation coefficient of current derivatives based pilot protection scheme for long-distance LCC- HVDC transmission lines. International Journal of Electrical Power & Energy Systems, 116 . Retrieved from https://doi.org/10.1016/j.ijepes.2019.105526.