The manager of a large resort's main hotel has been receiving complaints from some guests that they are not being provided with prompt service upon approaching the front desk. In particular, she is concerned that desk staff might be providing female guests with less prompt service than their male counterparts. In observing a sample of 34 male guests (first sample), and 36 female guests (second sample). Assuming the population standard deviations to be equal, use the 0.05 level of significance in examining whether the population mean time for serving female guests might actually be no greater than that for serving male guests (Data is in Sheet 1). Sheet 1 Male guests Female guests 8,95 23,18 20,87 6,15 19,18 12,47 18,04 20,69 14,22 4,07 9,39 24,79 17,21 9,42 30,17 20,57 21,57 18,54 8,03 11,08 10,87 22,72 14,90 23,82 18,45 23,65 10,70 5,06 10,41 21,86 19,59 20,69 18,48 24,56 24,17 21,66 21,38 14,64 17,83 11,79 26,05 16,19 23,79 23,91 18,16 8,19 14,06 23,15 19,49 9,47 19,95 22,56 6,86 13,47 7,23 10,51 6,23 16,09 18,53 27,40 19,39 21,14 25,02 19,05 23,19 4,42 12,62 25,96 20,48 22,17 Select one: a. t_stat=-0.30 and female serving time is significantly lower than for serving male guests b. t_stat= 0.30 and there is significant difference between serving times c. t_stat=-0.30 and female serving time is significantly greater than for serving male guests d. t_stat= 0.30 and there is no evidence to support the alternative hypothesis
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.
|
Male guests |
Female guests |
|
8,95 |
23,18 |
|
20,87 |
6,15 |
|
19,18 |
12,47 |
|
18,04 |
20,69 |
|
14,22 |
4,07 |
|
9,39 |
24,79 |
|
17,21 |
9,42 |
|
30,17 |
20,57 |
|
21,57 |
18,54 |
|
8,03 |
11,08 |
|
10,87 |
22,72 |
|
14,90 |
23,82 |
|
18,45 |
23,65 |
|
10,70 |
5,06 |
|
10,41 |
21,86 |
|
19,59 |
20,69 |
|
18,48 |
24,56 |
|
24,17 |
21,66 |
|
21,38 |
14,64 |
|
17,83 |
11,79 |
|
26,05 |
16,19 |
|
23,79 |
23,91 |
|
18,16 |
8,19 |
|
14,06 |
23,15 |
|
19,49 |
9,47 |
|
19,95 |
22,56 |
|
6,86 |
13,47 |
|
7,23 |
10,51 |
|
6,23 |
16,09 |
|
18,53 |
27,40 |
|
19,39 |
21,14 |
|
25,02 |
19,05 |
|
23,19 |
4,42 |
|
12,62 |
25,96 |
|
20,48 |
|
|
22,17 |
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