lany fast-food restaurants have soft drink dispensers with preset amounts, so that when the operator merely pushes a button for the desired rink the cup is automatically filled. This method apparently saves time and seems to increase worker productivity. A researcher randomly selects 9 workers from a restaurant with utomatic dispensers and 9 works from a restaurant with manual dispensers. At a 1% significance level, use the Mann-Whitney U Test to test whether workers with automatic dispensers are significantly more productive. utomatic (Group 1): 153, 128, 143, 110, 152, 168, 144, 137, 118 lanual (Group 2): 105, 118, 129, 114, 125, 117, 106, 92, 126 hat is the alternative hypothesis for this study? O Worker productivity is lower with automatic dispensers. O Automatic dispensers fill cups faster than manual dispensers. O Worker productivity is higher with automatic dispensers. There is no difference in worker productivity between restaurants with automatic and manual dispensers. that rank will be given to the observation value, 118 that is in both the automatic and manual groups? (Round answer to 1 decimal). 105 then rounding the U test statistic up to the next value, what is the p-value from the Mann Whitney Table of p-values? (Round to 4 decimal places) 1 that can be conduded from this study at a 1% significance level? O Automatic dispensers fill cups faster than manual dispensers. OWorker productivity is lower with automatic dispensers. There is no difference in worker productivity between restaurants with automatic and manual dispensers. Worker productivity is higher with automatic dispensers.
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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