51 42 43 Method 2 48 46 47 50 42 43 ANOVA table (to whole number, but p-value to 4 decimals and F value to 2 decimal, if necessary). Do not round intermediate calculations. f Variation Sum of Squares Degrees of Freedom Mean Square p-value 88 00
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.
 alt="An amusement park studied methods for decreasing the waiting time (minutes) for rides by loading and unloading riders more efficiently. Two alternative loading/unloading methods have been
proposed. To account for potential differences due to the type of ride and the possible interaction between the method of loading and unloading and the type of ride, a factorial experiment was
designed. Use the following data to test for any significant effect due to the loading and unloading method, the type of ride, and interaction. Use a = 0.05. Factor A is method of loading and
unloading; Factor B is the type of ride.
Type of Ride
Roller Coaster
Screaming Demon
Long Flume
Method 1
49
50
47
51
42
43
Method 2
48
46
47
50
42
43
Set up the ANOVA table (to whole number, but p-value to 4 decimals and F value to 2 decimal, if necessary). Do not round intermediate calculations.
Source of Variation Sum of Squares Degrees of Freedom Mean Square
F
p-value
Factor A
Factor B
Interaction
Error
Total
The p-value for Factor A is
- Select your answer-
What is your conclusion with respect to Factor A?
- Select your answer-
수
The p-value for Factor B is
Select your answer -
What is your conclusion with respect to Factor B?
- Select your answer -
The p-value for the interaction of factors A and B is
- Select your answer -
What is your conclusion with respect to the interaction of Factors A and B?
- Select your answer -
What is your recommendation to the amusement park?
Select your answer -
00
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