Question 1: Which statement about Ho: p = Po is true? (a) po is the true value of the population proportion. (b) po is the estimated value of the population proportion. (c) po is the hypothesized value of the population proportion under the null hypothesis. (d) po is the transformed value of the population proportion under the null hypothesis. Question 2: You have completed a hypothesis test with HA : p > 0.15. You computed the test statistic z = 1.35 and found the p-value = 0.0885. Which is the best interpretation of the p-value? (a) The p-value is the probability that the null hypothesis is true. (b) The p-value is the probability that the null hypothesis is false (c) The p-value is the probability of a random sample producing a test statistic as big (or bigger) than z = 1.35 when the null hypothesis is true. (d) The p-value is the probability of the null hypothesis being true given a test statistic as big, or bigger than z = 1.35. Question 3: Suppose that we are testing Ho: p = 0.05 and a random sample of size 200 yields 200 successes. What is SD(p) under the null hypothesis? (а) 0.0000 (b) 0.0158 c) 0.0154 (d) 0.0689 (e) 1.000 (f) 200 Question 4: Suppose that we are testing Ho: p = 0.70 with a sample p = 0.50 and n = 30. Based on the success-failure condition, should you carry out a hypothesis test about the proportion? (a) Yes, the samples are expected to have at least 10 successes and 10 failures. (b) Yes, the samples are expected to have at least 10 successes. (c) No, the samples are not expected to have the required minimum number of successes and failures. (d) Can't tell; there is not enough information.
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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