The calculations for a factorial experiment involving four levels of factor A, three levels of factor B, and three replications resulted in the following data: ST = 286, SSA = 28, SSB = 23, SSAB = 178. Set up the ANOVA table. (Round your values for mean squares and F to two decimal places, and your p-values to three decimal places.) Source Sum Degrees of Freedom Mean p-value of Variation of Squares Square Factor A Factor B Interaction Error Total Test for any significant main effects and any interaction effect. Use a = 0.05. Find the value of the test statistic for factor A. (Round your answer to two decimal places.) Find the p-value for factor A. (Round your answer to three decimal places.) p-value = [ State your conclusion about factor A. O Because the p-value sa = 0.05, factor A is significant. Because the p-value sa = 0.05, factor A is not significant. O Because the p-value > a = 0.05, factor A is not significant. Because the p-value > a = 0.05, factor A is significant. Find the value of the test statistic for factor B. (Round your answer to two decimal places.) Find the p-value for factor B. (Round your answer to three decimal places.) p-value = [ State your conclusion about factor B. Because the p-value s a = 0.05, factor B is significant. O Because the p-value > a = 0.05, factor B is not significant. O Because the p-value s a = 0.05, factor B is not significant. O Becauce the oe O 05 factor B is sianificant
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.
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