Unit VIII PowerPoint Presentation Research Methods

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Unit VI Scholarly Activity Research Methods

Jovan Maires

Columbia Southern University

Research Methods

Dr. Renee Norris-Jones

19 September 2022

Data Analysis: Hypothesis Testing

When utilizing data from the Sun Coast, several approaches to sampling and testing are used to validate a hypothesis. The t-test and the analysis of variance (ANOVA) are kinds of statics that may be used to test data. The t-test can be used for correlation analysis, while ANOVA can be used for regression analysis. Testing data across categories such as PM, safety training, lost-time hours, sound frequency, and so on, where regression techniques were used, showed variances in each presented data set. To differentiate between a large number of variables within a group using t-tests and ANOVA testing, predictions are expressed in the form of two hypotheses (null or alternative) (Creswell & Creswell, 2018).

Independent Samples t-Test: Hypothesis Testing

Ho4: The efficiency of the updated training program is not statistically correlated with that of the previous training program.

Ha4: The efficacy of the updated training program is significantly correlated with that of the previous training program.

	Group A Training Scores	Group B Reviewed Scores
Mean	69.89273839	85.062194
Variance	123.004	27.0
Observation	61	61
Hypothesized Mean Difference	0
df	87
tStat	-9.70020802563
P(T<=t) one-tail	9.700145E-16
t Critical one-tail	1.7625596
P(T<=t) two-tail	1.9478E-15
t Critical two-tail	1.975082

According to the findings, the mean values for Group A with the original training scores are significantly lower than those for Group B with the updated training ratings. The average score for Group A comes in at 69.79, but the average score for Group B is 84.77. Results show that the p-value of 9.67E-16 is less than the alpha value of 0.05. Hence the alternative hypothesis that there is a statistically significant difference between the means of the dependent variables for Groups A and B is accepted, and the null hypothesis is rejected. This signifies acceptance of the alternative hypothesis and rejection of the null hypothesis.

Dependent Samples (Paired Samples) t-Test: Hypothesis Testing

Ho5: We found no statistically significant relationship when examining the correlation between employee blood lead levels and the amount of lead in their workplace.

Ha5: A correlation may be statistically significant between increasing lead exposure at workplaces and elevated blood lead levels in workers.

t-Test: Paired two samples for means

	Pre-Exposure µ/dl	Post-Exposure µ/dl
Mean	31.85714286	33.30571429
Variance	151.458333	155.5
Observation	49	49
Pearson Correlation	0.912836043
Hypothesized Mean Difference	0
df	47
tStat	-1.920802563
P(T<=t) one-tail	0.030776357
t Critical one-tail	1.76774196
P(T<=t) two-tail	0.059552714
t Critical two-tail	2.010634758

Based on the findings, the mean values for Group A with pre-exposure and Group B with post-exposure are slightly lower. The mean score for Group A, which was pre-exposed to the agent, was 31.86, whereas the mean score for Group B, which was exposed to the agent, was 33.30. The findings also reveal that the p-value is 0.0626, slightly higher than the alpha level of 0.05. Because of this, the null hypothesis is accepted, while the alternative hypothesis is rejected. This suggests no significant link exists between the mean values for pre-exposure and post-exposure. This conclusion can be drawn from the data presented here.

ANOVA: Hypothesis Testing

Ho6: Statistical evidence can show no correlation between the return on investment and the many different service lines.

Ha6: The correlation between return on investment and the number of service lines offered is statistically significant.

ANOVA: Single Factor

Summary

Group	Count	Sum	Mean	Variance
A= Air	15	177	9.0	9.102678
B= Soil	15	183	8.1	3.536829
C= Water	15	142	6	6.673920
D= Training	15	109	5.4	1.35472

ANOVA

Source of variation	ss	df	MS	F	p-value	Fcri
Between groups	180.2	3	60.9222	10.9231	1.759E-06	2.7249439
Within groups	380.4	76	6.83739
Total	560.6	79

According to the findings, the return on investment (ROI) for Group D, which focuses on training, has the lowest mean value of all groups, coming in at 5.4. The return on investment (ROI) for Group C, which is water, has a mean value of 6. Return on investment has the greatest mean values for Group A, which refers to the air, and Group B, which refers to the soil, with Group A having 9.0 and Group B having 8.1, respectively. The findings also show that the p-value of 1.76 E-06 is smaller than the alpha level of 0.05, which means that the null hypothesis cannot be accurate. However, the alternative theory suggests a link between the return on investment and the various service offerings.

References

Creswell, J. W., and Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approach (5th ed.). SAGE. http://online.vitalsource.com/#/books/9781506386690