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H1: There is a significant relationship between commuting and gender.
SPSS results showed that generally, respondents went to work by driving a car. Directionally, females are more likely to take the passenger train (73%), while more than the majority of male respondents work at home (62%). However, these findings are not significant, and the X2 asymp. sig value of 0.101 showed that p< 0.05, which means that Ho is retained -- that is, there is no significant relationship between commuting and gender.
To determine the significance of the relationship between the variables savings and loans and other financiers, an independent samples t-test will be conducted as the statistical analysis. The null hypothesis for this analysis is:
Ho: There is no significant difference between savings and loans and other financiers in the average payback period necessary to justify solar heating systems for residences.
To conduct the t-test, the following formula will be used:
observed difference between sample means / standard error of the difference between means
X1-X2 / S (x1-x2) where: S (x1-x2) = ?sx12 + sx22
Applying the given data to the formula above, we get:
Savings & Loans
Other Financiers
Sample mean
Standard error
S (x1-x2)
and soda both for non-directional (two-tailed) and directional (one-tailed) tests, as reflected in the p values of the variables, wherein p>.05 -- 0.06 and 0.13, respectively.
Garrett, H. 1962. Elementary Statistics. NY:McKay.
At t=0.41 and df=12, the difference between t between managers from the West and East and their evaluation ratings represent no real difference between the larger population of these two groups. Thus, null hypothesis is retained.
Looking at the Excel output, the results generated showed that there is no significant difference between beer and soda both for non-directional (two-tailed) and directional (one-tailed) tests, as reflected in the p values of the variables, wherein p>.05 -- 0.06 and 0.13, respectively.
Garrett, H. 1962. Elementary Statistics. NY:McKay.
Essentially, Pearson's formula translates qualitative data from a set of observations into a single number. Probability tables with corresponding numbers, with variances built in for different levels of significance and different degrees of freedom (the number of available data points used for the estimation/prediction of other data, the calculation of which in Chi Square analysis is provided for by another straightforward equation), provide the probability of dependence for any given
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
23343849 73 0.35009171 35-54 88.40378549 82 0.46387684 55+ 81.36277603 93 1.66445872 2 = 11.39 This value does exceed the critical ?2 value for df = 2 at ? = 0.05. Therefore, we can assume that one of the observed values is significantly different from the expected value for that group. Without post-hoc pairwise tests it is impossible to say exactly which group is different. We can make an educated guess, however, that the proportion of 55+ shoppers in store a is statistically
Nonparametric Tests Many interesting questions related to students are categorical. For instance, there is considerable interest in the different enrollment patterns of male and female students in the following majors: Science, technology, engineering, and mathematics (STEM). While the literature does provide robust data for the numbers of students enrolled in these majors, intriguing questions remain that may be better suited to more qualitative data collection. For instance, a research question is:
Chi-Square, T-Test and Correlation Research Methods in Psychology a (HPS201/HPS771) Crosstabs and Chi-Square Scenario Some researchers have suggested that there are two main types of personality: Type A and Type B. Individuals with Type A personality are characterized as being conscientious and competitive. They strongly desire success and typically present with higher levels of stress. Conversely, individuals with Type B personality are typically less stressed and tend to be more laconic, relaxed and less
Elaboration Model There is a gender difference in attitudes toward spending too much money on halting crime rate. There is a gender difference in attitudes toward spending too much money on law enforcement. There is a direct relationship between amount spend on halting crime rate and amount spent on law enforcement. There is a gender differencet in attitudes toward spending too much money on halting crime rate that is directly moderated by amount spent