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In Unit 7, you considered the t-test for free means, and you utilized this test to think about two example bunches from the autonomous variable called video access. In Unit 7, my examination inquiry was whether making video addresses for my understudies will altogether influence aggregate class focuses.
In Unit 7, I had a class of 30 understudies. To research this inquiry, I gave 15 understudies access to video instructional exercises every week, and the other 15 did not have entry.
My needy variable was aggregate class focuses.
My inquiry was whether the mean aggregate class focuses for Group1 (understudies WITH video access) was fundamentally not the same as Group 2 (understudies WITHOUT video access).
Ho: mean aggregate focuses for Group1 = mean aggregate focuses for Group2
Ha: mean aggregate focuses for Group1 ≠ mean aggregate focuses for Group2
We can extend this idea into having an autonomous variable that is isolated into more than two gatherings.
Assume that rather than looking at just two means (Group 1 and Group 2), I need to think about three unique gatherings:
Bunch 1: Watches all recordings (1 every week)
Bunch 2: Watches half of the recordings (1 each other week)
Bunch 3: Watches none of the recordings (no entrance to recordings)
Once more, my single free variable is video access. In any case, for this situation, it is currently isolated into three gatherings (in some cases called levels or classes).
Since I need to think about the mean aggregate focuses for each of the three gatherings, I must utilize an ANOVA test.
Ho: mean aggregate focuses Group1 = mean aggregate focuses Group2 = mean aggregate focuses Group3
Ha: mean aggregate focuses Group1 ≠ mean aggregate focuses Group2 ≠ mean aggregate focuses Group3
Assume I imagine that I am going to dismiss the invalid speculation Ho. This implies that no less than one of my three video access gatherings is fundamentally not quite the same as the others. At the end of the day, utilizing alpha is .05, the p-esteem (Sig of the F test for ANOVA) is not as much as alpha and I can dismiss the invalid.
Next, I can utilize the Post Hoc to take a gander at an examination between each of the three gatherings. The Post's consequences Hoc will help me to figure out which gatherings are essentially unique in relation to one an
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