KIN 4310 1nd Edition Lecture 11Outline of Last Lecture I. DefinitionII. Z-ScoresIII. Interpreting Z ScoresIV. Normal DistributionV. Z Score FormulasVI. Example: Male StatureVII. Percentile and QuartileVIII. DefinitionsIX. QuartilesX. PercentilesXI. Excel FunctionsXII. The Normal DistributionXIII. ExampleXIV. ExampleXV. Normal DistributionsXVI. DefinitionXVII. DefinitionXVIII. Important PrinciplesThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.XIX. ExampleOutline of Current Lecture I. WorkshopII. ProbabilityIII. Probability ExampleIV. ProbabilityV. Probability and StatisticsVI. Introduction to ProbabilityVII. Probability LimitsVIII. Rare Event RuleIX. Challenger DisasterX. Formal HypothesesXI. Formal HypothesesXII.Current LectureI. Workshopa. We did a validity study to see if resting HR and time held breath and SRPA are correlated. We found that it looks like there is no relation.II. Probabilitya. 0 < or equal to p less than or equal to 1 b. Represents how likely a specific event is to occurc. For random events:i. Number of desired outcomes divided by total number of possible outcomesd. Probability is the number of desired outcomes, divided by the total number of possible outcomese. If you flip a fair coin, and you want to get tails, you have one outcome that will satisfy that even, and you have two possible outcomesi. The probability is ½ or .50 or 50%III. Probability Example TQ*a. You flip a fair coin five times, and you want to get at least four tailsi. The probability is 6/32 or 0.188 or 18.8%IV. Probabilitya. In Texas Holdem, what is the probability of being dealt pocket pairs?b. The first card dealt can be any card.c. It has three ‘partners’ in the deckd. There are 51 unknown cards in the deck.e. The probability is 3/51 or 1/17.V. Probability and Statisticsa. Probability (p) is used to state how confident we can be about the existence of a particular statistical relationshipb. A smaller probability value means that we can be less confident that the observed statistical relationship is realVI. Introduction to Probabilitya. Consider the probabilities of the following statements:i. These slides will be included in Exam 2. 1. 1ii. I have no idea if these slides will be on the exam.1. .5iii. There is not a snowball’s chance in hell that these slides will be on the exam.1. 0b. Probability is important for knowing the confidence we have in event.VII. Probability Limitsa. The probability of an impossible even is 0b. The probability of an event that is certain to occur is 1c. For any unknown event A, A’s probability can be expressed as: 0 < or equal to P(A) < or equal to 1VIII. Rare Event Rulea. When data occurs that is extremely improbable, we must question the assumption that it occurred randomlyb. Example: An all-male jury is selected for a controversial case involving women’s issues.i. What are the odds of 12 male jurors being randomly selected from the community?ii. P = (0.5)12 = 0.00024IX. Challenger Disastera. January 28, 1986, the shuttle explodedb. From the data collected from o-ring incidents below 65 degrees F and above 65 degrees F, it is likely that temperature has something to do with o-ring failure.X. Formal Hypothesesa. Inferential statistics always starts with a claim, like a hypothesisb. Examplei. “You can lose weight by switching to a high fat/low carb diet”ii. “People who attend church/temple/mosque regularly have lower cancer rates”iii. My uncle is psychic”XI. Formal Hypothesesa. A null hypothesis is the negation of a claim i. Its always negativeii. Whatever you claimed, it’s the oppositeb. HO is the skeptical choicec. HO says i. “The claim is false”ii. “There is no effect”iii. “Your uncle is NOT psychic”XII. Formal Hypothesesa. The research hypothesis is a formal statement of the claimb. H1 is assertive, positivec. H1 saysi. “The claim is true”ii. “A has an effect on B”iii. “There is a relationship between A and