Let’s say that a researcher conducts a study with 3 groups, each with 6 participants. Fill in the degrees of freedom in the following ANOVA table. Source df MS F Between 34.43 4.95 Within 6.95 Total 17 Use the following Distributions tool to find the boundary for the critical region at α = .05 and α = .01. To use the tool to find the critical F value, set both the numerator and the denominator degrees of freedom; this will show you the appropriate F distribution. Move the orange line until the area in the tail is equivalent to the alpha level you are investigating. F Distribution Numerator Degrees of Freedom = 26 Denominator Degrees of Freedom = 26 012345678F.5000.50001.000 At the α = .05 level of significance, the boundary of the critical region for this ANOVA is: 6.359 4.938 3.098 3.682 At the α = .01 level of significance, the boundary of the critical region for this ANOVA is: 3.098 3.682 6.359 4.938 Now use the tool to evaluate the F-ratio. (Hint: Select the icon with one orange line and one purple line. You can set the orange line at the critical boundary and move the purple line to the F-ratio in the ANOVA table given.) To use the tool to find the F-ratio, set both the numerator and the denominator degrees of freedom; this will show you the appropriate F distribution. Move the orange line until the area in the tail is equivalent to the alpha level you are investigating. At the α = 0.01 level of significance, your conclusion is: The null hypothesis can be rejected
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.
|
Source
|
df
|
MS
|
F
|
|---|---|---|---|
| Between | 34.43 | 4.95 | |
| Within | 6.95 | ||
| Total | 17 |
F Distribution
Numerator Degrees of Freedom = 26
Denominator Degrees of Freedom = 26
Trending now
This is a popular solution!
Step by step
Solved in 2 steps
