4. A ?2 test to determine whether the sample is significantly different from the expected distribution would be most appropriate. The data in this case yield ?2 = 2.51, below the critical value cutoff for ? = 0.05. We can assume that the sample is NOT significantly different from the general population.
5. In this data, there appears to be no gender-based difference between the way in which people commute to work. Since ?2 = 7.715, df = 3 and p > 0.1, this data fails to exceed the critical ?2 value for the analysis, 7.81.
6. To test this hypothesis I will use a t-test of means. Critical t-value for ? = 0.05 is (conservatively!) 1.98.
T = (M1 - M2) / SE (M1 - M2)
SE = ( (var1/n1) + (var2/n2) = ( (0.52/100) + (0.62/64) = 0.09
T = 1 / 0.09 = 11.11
According to this analysis it appears that there is a significant difference between loan repayment rates through Savings & Loan institutions and other institutions, at least in this sample.
7.
Region
Manager Rating
Region
Manager Rating
West1
74
81
West2
88
63
West3
78
56
West4
85
68
West5
8. These data do not indicate a significant difference between Variable 1 and Variable 2 unless a looser criterion for significance (? = 0.10) is used, even in the one-tailed case. One-tailed t-tests should only be used when they can be theoretically justified, i.e. If the values being measured could not possibly cause one group's scores to be lower than the other's, but only equal. However, there is a small-to-medium correlation between the variables, so they may not be independent. If not, this violates the assumptions underlying the t-test, and a different analysis (perhaps based on regression) should be used.
Chi-Square Test Study The focus of the research is to use the Chi-Square analysis to test whether the use of marijuana is less dangerous than alcohol. Over the years, there is high number of related deaths linked to alcohol in the United States, and comparably, there are no related deaths linked to the Marijuana use. A report from Center for Disease Control and Prevention (2013) reveals there is no category of
Chi-Square Analysis Chi square analysis is a way of comparing categorical responses from two or more different groups (Ryan & Eck, Unk.). This comparison can help reveal whether there is a relationship between the two different groups, and also whether real-world results are in line with anticipated results. Chi square analysis is what is known as a nonparametic test. "Parametric and nonparametric statistical procedures test hypotheses involving different assumptions. Parametric statistics
75 The standard value of 27.75 represents the distance of each score or frequency of representation of each employment category to the average or mean score or frequency for the distribution (i.e., employment categories. Looking at the gender-commuting relationship, a possible hypotheses that can be developed from these variables are the following: Ho: There is no significant relationship between commuting and gender. H1: There is a significant relationship between commuting and gender. SPSS results showed
motivation to learn, followed by an educated and informed research question. After the research question has been narrowed down, the researcher conceptualizes how the question may be answered feasibly, within the constraints of time and budget. Such constraints can help guide the research questions into a workable hypothesis that is testable, as well as being relevant and meaningful to healthcare stakeholders. From here, the researcher can glean a cogent
Statistical Analysis Reported in Two Journal Articles Research endeavors, albeit it clinical, empirical, descriptive, historical, or case study oriented, must at all times adhere to the rigors of effective or best-fit research practice. Without stringent controls placed on the area of investigation no research endeavor will advance any body of knowledge. To this end all research must be finely tuned and described as to intent or purpose, phenomenon to be
They hypothesis is rejected. The Chi-Square was significant X2 (1, 84) = 4.403, p < .05. The proportion of females alive at discharge was much greater than the proportion of males alive at discharge. This can be seen in the barchart below. alive discharge * gender Crosstabulation gender Total male female alive discharge no Count 7 5 12 Expected Count 3.9 8.1 12.0 yes Count 20 52 72 Expected Count 23.1 48.9 72.0 Total Count 27 57 84 Expected Count 27.0 57.0 84.0 4) Correlation & Regression Variables: Hours in Operating Room & Number of Preop Risk Factors Scatter Plot: Hypothesis: The population correlation
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