TTest and ChiTest
T-Test and Chi-Test
In the T-Test, the first hypothesis is expected that the mean of tenures between 1 and 2 will be similar. But a found out its different and that difference is very significant. In which in this kind of Test we determine the difference or the varying relationship to each other.
The total number of compromising group 1 is equal to N1=120.
The mean is equal to 1.47 while the variance is equal to 0.4989.
The total number of members in group 2 is N2=186.
The mean is equal to 1.67 while the variance is equal to 0.649.
Therefore in the given hypothesis the only inference that can only be made suggests is that the difference between the two means is fairly and statistically significant.
In the Chi-Square Test, we compared the expected and observed frequencies of the particular data set. In here our null hypothesis is that the numbers of male and female are equal. While the alternate hypothesis states that the number of male and female is different.
From the result on the degrees of freedom, we can analyze that the null hypothesis is unacceptable. It clearly states that there is a wide difference between males and females.
It is a significant thing to have analyzed the difference between this two.
It is scientifically proven by Ridley (1999) the fact that men and women are different from each other. And they also differ in their motivational styles. Still employment agencies will have to disregard thegender when it comes to job working, so the aim of the employers are to motivate them using different tools that are available for them to use their abilities.
The research that was carried out by Wiedner (1998) regarding personality type, etch. presented as determinants of job satisfaction has scrutinized the other factors such as age, educational level, sex, and the part time and full-time status of the worker.
And the one-way analysis of variance has been computed to determine if there are significant differences among the pay satisfaction levels. As the null hypothesis believed that there is no significant difference among the three shift categories. While on the other hand, the alternative hypothesis states that there is significance that exists on at least one of pair.
This is just the same with the study in Unit 1, but the variance indicates that there is an insignificant difference that the level of education did not have the critical impact on their perception of equity on their perception of equity on pay.
For Question No. 3
X= No. of Tenures
bx bx+a y/1=bx+a
P=f/n q=1-p pq=p x q
Rxx=30/29 x 494.20- (-134.250)/494.90
=1.04 x 629.15
=1.27 perfect Correlation/reliable
Question No. 4
Here our finding is that there is a significant relation in the differences of men and women regarding job satisfaction. As we observe from the first regression. And on the table we made, we have come up with a perfect correlation indicating that we have a positive result.
There came the difference between the two regressions. When x and y switch, the result was changed. As the formula was: y = bx+a/y to get the y we cross multiply the equation this way y = bx + a.
Their both sums came to became different and they both contrast with each other since their composition was changed. They now represent another equation. The conclusion somewhat came to be undecided since it’s different to explain both regressions with different data in it. The both call for a formula that is really suited for them.