Free plagiarism for this file

H C M : 5 0 6 – 2 4 4 0 7

S E U

Analysis and Testing of Hypotheses for the Mean of the Age of the Women Based on Gestation Demographics Dataset

Module 04: Critical Thinking

105 PTS

200000796

Abdulazeez Abdullah M Albaradei

Introduction

The data analysis phase is one of the most critical steps that the researcher goes through during his scientific research. It is through the analysis of the scientific research data that the researcher arrives at the findings on which he will rely. For the questions that the researcher asks during his scientific research. (Kruschke, J. K.2015)

The data analysis phase comes after the researcher has completed the data collection phase, and there are many reasons that lead the researcher to analyze data related and connected to his scientific research. The most prominent of these methods are:

- Choosing the right explanatory strategy builds the researcher's ability to decipher the factors that influence the hypothesis he is investigating.

- Analysis of the data enables the researcher to determine the basic extent of the effect of the variables on the phenomenon.

Stages of analysis

- Descriptive Factor Analysis: This method is considered one of the most prominent forms of data analysis. Through it, the researcher performs a logical and realistic analysis of the effect that the different variables have on a phenomenon that the researcher is studying. (Kruschke, J. K.2015)

- Statistical analysis: It is the conversion of individually worthless expressions into expressions of great value, and there is a large group of programs used for statistical analysis, the most prominent of which are SPSS, SAS, in addition to Excel. (Kruschke, J. K.2015)

- Qualitative analysis: By doing this, the researcher focuses on the phenomenon he is investigating so that he describes it as an in-depth description and then relies on the data to experiment again and reach the end on cause and effect. (Kruschke, J. K.2015)

Ordinal/precise cut factors analysis

In the critical thinking task, women's data are extracted and analyzed to examine the demographic variables of gestation.

- The necessary factors are extracted from the Gestation Demographics SEU dataset

- At this point, a chart is made for the age of the lady after the birth of the first child, and the results are as shown in the table and sketch below

From the past table, it appears that the most usual occasions for women considering progeny were at the age of 20-25 years, where they numbered 401, it is followed by the people raising progeny at the age of 25-30 years and the quantity of 370 ladies, followed by 30-35 and the quantity of 193 ladies, while 15-20 years added up to 133 ladies, and 35-40 years by 109 ladies. Less than 40 years of age were 26 ladies and only 15 years of age.

- We calculated the mean age of the ladies and came up with 27.24 years.

- To cheque the assumption that the mean age was more than 37 years, a test was conducted One-Sample T-test.

- H0 The average age of women giving birth is 37 years. (Null hypothesis)

- H1 The average age of childbearing women is not 37 years old. (Alternative Hypothesis)

- The probability value was less than 0.05%, then the null hypothesis is rejected and we accept the alternative hypothesis.

- The arithmetic normal for the test obtained in the table is 27.24, which is much slower than the mathematical normal. Also, the time of confidence between Upper and Lower does not have a digit with zero; this is the third piece of evidence for rejecting the null hypothesis.

Conclusion

- The results of the data analysis are consistent with several studies such as the study (Neggers, Y. H.2018) which confirms that most women give birth between 18-30 years.

- Also, a study (Frick, A. P.2020) confirmed that women in European nations start having offspring between the ages of 26-38, which is consistent with our study

- Which supports the result of the analysis and testing of the previous hypothesis.

References

Kruschke, J. K. (2015).Null Hypothesis Significance Testing. Doing Bayesian Data Analysis

Frick, A. P. (2020). Advanced maternal age and adversepregnancy outcomes.Best Practice & Research Clinical Obstetrics & Gynaecology.

Attali, E., & Yogev, Y. (2020). The impact of advanced maternal age on pregnancy outcome. Best Practice & Research Clinical Obstetrics &amp.

Neggers, Y. H. (2018). Gestational Age and Pregnancy Outcomes. Pregnancy and Birth Outcomes.