On a separate sheet of paper, create a chart similar to Table 16.4 to find many of the values necessary to evaluate the ANOVA.
Addition Rule of Probability
It simply refers to the likelihood of an event taking place whenever the occurrence of an event is uncertain. The probability of a single event can be calculated by dividing the number of successful trials of that event by the total number of trials.
Expected Value
When a large number of trials are performed for any random variable ‘X’, the predicted result is most likely the mean of all the outcomes for the random variable and it is known as expected value also known as expectation. The expected value, also known as the expectation, is denoted by: E(X).
Probability Distributions
Understanding probability is necessary to know the probability distributions. In statistics, probability is how the uncertainty of an event is measured. This event can be anything. The most common examples include tossing a coin, rolling a die, or choosing a card. Each of these events has multiple possibilities. Every such possibility is measured with the help of probability. To be more precise, the probability is used for calculating the occurrence of events that may or may not happen. Probability does not give sure results. Unless the probability of any event is 1, the different outcomes may or may not happen in real life, regardless of how less or how more their probability is.
Basic Probability
The simple definition of probability it is a chance of the occurrence of an event. It is defined in numerical form and the probability value is between 0 to 1. The probability value 0 indicates that there is no chance of that event occurring and the probability value 1 indicates that the event will occur. Sum of the probability value must be 1. The probability value is never a negative number. If it happens, then recheck the calculation.
On a separate sheet of paper, create a chart similar to Table 16.4 to find many of the values necessary to evaluate the ANOVA.

 alt="Table 16.4 Statistical Power Tables for Factorial ANOVA
Degrees of freedom for effect 1, alpha = .05
%3D
Population eta squared
Power
.01
.05
.10
15
.20
.25
.30
.40
.50
.60
.50
193
38
18
12
9.
7
4.
3.
.70
310
60
29
18
13
10
8.
6.
.80
393
76
36
23
17
13
10
7
.90
526
101
48
31
9.
17
13
6.
.95
651
125
60
39
21
16
11
8.
.99
920
176
84
53
38
29
22
15
10
Degrees of freedom for effect = 2, alpha = .05
Population eta squared
Power
.01
.05
.10
.15
.20
25
.30
.40
.50
.60
.50
165
32
16
10
8
6.
3
3.
.70
255
50
24
16
11
6.
.80
319
62
30
19
14
11
6.
.90
417
81
39
25
18
14
11
.95
511
99
47
30
22
16
13
6.
.99
708
137
65
41
29
22
18
12
8.
lo 1odmun ev oldeiney smoo100 adT.(lgid 8wol
m n01 baa (id
A
Source: Adapted from Jaccard and Becker (2009).
voda
16.10 FOLLOW-UP TESTS
16.10.1 Nature of a Two-Way Interaction
One of the most common ways to illustrate a significant interaction in a two-way facto-
rial ANOVA is to graph the cell means (as in Figure 16.2) or to present a table of cell means
2.
3.
4.
LO LO
2.
3.
3.
4.
4.
4.
LO
4.
LO
22
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