One Way Analysis of Variance
One-way Analysis of Variance
(Abbreviated one-way ANOVA) is a technique used to compare means of two or more samples (using the F distribution). This technique can be used only for numerical data.
It consists of a single factor with several levels and multiple observations at each level. With this kind of layout we can calculate the mean of the observations within each level of our factor. The residuals will tell about the variation within each level. It can also average the means of each level to obtain a grand mean. And then look at the deviation of the mean of each level from the grand mean to understand something about the level effects. Finally, can compare the variation within levels to the variation across levels. Hence the name analysis of variance.
Used to determine whether there are any significant differences between the means of three or more independent (unrelated) groups. It tests the null hypothesis that samples in two or more groups are drawn from populations with the same mean values. And compares the means between the groups you are interested in and determines whether any of those means are significantly different from each other.
Formula
F= q MSBMSW Where: F = Fisher’s Ratio
K = Number of Columns
N = Total Number of items MSB= SSBK-1
MSW= SSWN-K
Attitudes of the 1st, 2nd, 3rd and 4th year EHS students towards their computer subject
Attitude | Year | Average Weighted Mean | Rank | | 1st | 2nd | 3rd | 4th | | | Study regularly | 3.44 | 3.37 | 3.50 | 3.56 | 3.47 | 9 | Regularly exercise the skills | 3.73 | 3.88 | 3.65 | 3.58 | 3.71 | 6.5 | Listen attentively during class discussion | 3.62 | 3.71 | 3.67 | 3.85 | 3.71 | 6.5 | Always do the assignment without hesitation | 3.63 | 3.73 | 3.83 | 3.75 | 3.75 | 5 | Always work on projects without hesitation | 3.94 | 4.10 | 4.12 | 4.00 | 4.04 | 2 | Enjoy the hands-on activity | 4.13 | 4.38 | 4.06 | 4.25 | 4.21 | 1 | Search and use other
(Abbreviated one-way ANOVA) is a technique used to compare means of two or more samples (using the F distribution). This technique can be used only for numerical data.
It consists of a single factor with several levels and multiple observations at each level. With this kind of layout we can calculate the mean of the observations within each level of our factor. The residuals will tell about the variation within each level. It can also average the means of each level to obtain a grand mean. And then look at the deviation of the mean of each level from the grand mean to understand something about the level effects. Finally, can compare the variation within levels to the variation across levels. Hence the name analysis of variance.
Used to determine whether there are any significant differences between the means of three or more independent (unrelated) groups. It tests the null hypothesis that samples in two or more groups are drawn from populations with the same mean values. And compares the means between the groups you are interested in and determines whether any of those means are significantly different from each other.
Formula
F= q MSBMSW Where: F = Fisher’s Ratio
K = Number of Columns
N = Total Number of items MSB= SSBK-1
MSW= SSWN-K
Attitudes of the 1st, 2nd, 3rd and 4th year EHS students towards their computer subject
Attitude | Year | Average Weighted Mean | Rank | | 1st | 2nd | 3rd | 4th | | | Study regularly | 3.44 | 3.37 | 3.50 | 3.56 | 3.47 | 9 | Regularly exercise the skills | 3.73 | 3.88 | 3.65 | 3.58 | 3.71 | 6.5 | Listen attentively during class discussion | 3.62 | 3.71 | 3.67 | 3.85 | 3.71 | 6.5 | Always do the assignment without hesitation | 3.63 | 3.73 | 3.83 | 3.75 | 3.75 | 5 | Always work on projects without hesitation | 3.94 | 4.10 | 4.12 | 4.00 | 4.04 | 2 | Enjoy the hands-on activity | 4.13 | 4.38 | 4.06 | 4.25 | 4.21 | 1 | Search and use other