Listed below are the numbers of deaths from lightning on the different days of the week over a period of 35 years. Use a 0.01 significance level to test the claim that deaths from lightning occur on the different days with the same frequency. Day Sun Mon Tues Wed Thurs Fri Sat Number of deaths 547 445 429 479 428 422 466 a. State Upper H 0 . A. p Subscript Sun Baseline equals p Subscript Mon Baseline equals p Subscript Tue Baseline equals p Subscript Wed Baseline equals p Subscript Thur Baseline equals p Subscript Fri Baseline equals p Subscript Sat Baseline equals 1 divided by 7 B. At least one proportion is different from the others C. p Subscript Sun Baseline equals p Subscript Mon Baseline equals p Subscript Tue Baseline equals p Subscript Wed Baseline equals p Subscript Thur Baseline equals p Subscript Fri Baseline equals p Subscript Sat Baseline equals 1 divided by 2 D. p Subscript Sun Baseline not equals p Subscript Mon Baseline not equals p Subscript Tue Baseline not equals p Subscript Wed Baseline not equals p Subscript Thur Baseline not equals p Subscript Fri Baseline not equals p Subscript Sat b. Find the sum of the observed frequencies: nothing (enter a whole number) c. Calculate the expected frequency for each day: nothing (give 2 decimal places) d. Calculate the test statistic. Which of these options is closest to its value? A. chi squared equals 25.29 B. chi squared equals 27.77 C. chi squared equals 22.23 D. chi squared equals 14.93 e. State the value of the critical value: nothing (give 3 decimal places) f. State the technical conclusion. A. Reject Upper H 0 B. Do not reject Upper H 0 g. State the final conclusion. A. There is not sufficient evidence to warrant rejection of the claim. B. There is not sufficient sample evidence to support the claim. C. There is sufficient evidence to warrant rejection of the claim. D. The sample data support the claim.
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.
428 422 466
25.29
27.77
22.23
14.93
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