9) More and more weight loss "miracle" drugs have been showing up on the market lately and the FDA has a challenge to determine what drugs are safe in a variety of different categories. The drugs all target parts of the brain involved with appetite and satiety, and interfere with hunger signals; so they can reduce the chances that you'll overeat. All of these drug companies are making huge pushes in the pharmaceutical world for patients to ask for these prescriptions from their doctors. The makers of a new drug, Saxenda wish to make an injectable treatment specifically for people with a Body Mass Index (BMI) of at least 30. The makers of Saxenda are making the claim patients will lose an average of at least 10.15 pounds within one month even with no change in their dietary or exercise routine. The following data shows the weight loss (in pounds) from volunteers who participated in some initial medical studies: | 4.75 9.98 11.12 10.13 8.7 9.74 12.4 6.4 8.62 7.15 4.8 9.0 12.1 5.2 11.56 a) Create the claims Saxenda is making? b) Is this data significant? Justify your response! c) What would a Type I error mean in context? Who would be hurt from this mistake? d) Doctors are skeptical, because they feel this new drug might help kick start the mentality about eating, but are also worried about the idea that no change to dietary or exercise routine is dangerous. The doctors also want to know about the subjects in the study. Pre-human studies with this drug found minuscule change in rodent weight... proportional speaking the doctors believe Saxenda has a real weight loss potential of 7.5 pounds at the 5% significance level. What's the probability of making a Type Il error? Does this hurt the patient more or Saxenda (don't elaborate too much here)?
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.
AP Stats Practice ASSIGNMENT
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