What impact does fast-food consumption have on various dietary and health characteristics? A research article reported the accompanying summary statistics on daily calorie intake for a representative sample of teens who do not typically eat fast food and a representative sample of teens who do eat fast food. Sample Size Sample Mean Sample Standard Deviation Sample Do not eat fast food 666 2,256 1,514 Eat fast food 414 2,631 1,138 Is there convincing evidence that the mean calorie intake for teens who typically eat fast food is greater than the mean intake for those who don't by more than 150 calories per day? (Test the relevant hypotheses using a significance level of a = 0.05. Use u, for teens who typically eat fast food and u, for teens who don't typically eat fast food.) State the appropriate null and alternative hypotheses. O Ho: H1 - H2 = 0 Hạ: H1 - 42 > 150 O Ho: H1 - H2 > 150 Hgi Hy - H2 = 0 Ho: H1 - H2 = 150 H2i H1 - H2> > 150 Ho: H1 - H2 = Hại H1 - H2>0 O Ho: H1 - H2 > 150 Hai H1- H2 = 150 Find the test statistic and P-value. (Use a table or technology. Round your test statistic to one decimal place and your P-value to three decimal places.) t = P-value = State the conclusion in the problem context. We fail to reject Ho. There is not convincing evidence that the mean calorie intake for teens who typically eat fast food is greater than the mean intake for those who don't by more than 150 calories per day. We reject H.: There is not convincing evidence that the mean calorie intake for teens who typically eat fast food is greater than the mean intake for those who don't by more than 150 calories per day. O We fail to reject Ho. There is convincing evidence that the mean calorie intake for teens who typically eat fast food is greater than the mean intake for those who don't by more than 150 calories per day. We reject Ho. There is convincing evidence that the mean calorie intake for teens who typically eat fast food is greater than the mean intake for those who don't by more than 150 calories per day.
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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