was selected by random. 2) 10% Assumption: We can assume that the sample size (n=27) is less than 10% of the population. 3) Nearly Normal: n = 27‚ which is less than n=30. We need to look at the boxplot and histogram to determine if the sample is normal. The diagram shows that the sample data is skewed to the left‚ with an outlier at 200. The sample is not normal because the diagram is not unimodal‚ symmetrical‚ and it contains an outlier. D) E) Since the p-value is less
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University of Phoenix Material Week Four Quiz 1. Please indicate whether each of the statements below is true or false. a. A normal distribution is any distribution that is not unusual. False b. The graph of a normal distribution is bell-shaped. True c. If a population has a normal distribution‚ the mean and the median are not equal. False d. The graph of a normal distribution is symmetric. True Using the 68-95-99.7 rule: 2. Assume that a set of test scores is normally distributed
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the lack of symmetry. A distribution‚ or data set‚ is symmetric if it looks the same to the left and right of the center point. Types of Skewness: Teacher expects most of the students get good marks. If it happens‚ then the cure looks like the normal curve bellow: But for some reasons (e. g.‚ lazy students‚ not understanding the lectures‚ not attentive etc.) it is not happening. So we get another two curves. Positive Skewness Negative Skewness The first one is known as positively skewed
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with an increase in body temperature. Data Collection: The data was obtained from the article “What’s Normal? -- Temperature‚ Gender‚ and Heart Rate” (Shoemaker‚ Allen 1996) as found in the Journal of Statistics Education. Its author derived data from an article in the Journal of the American Medical Association entitled "A Critical Appraisal of 98.6 Degrees F‚ the Upper Limit of the Normal Body Temperature‚ and Other Legacies of Carl Reinhold August Wunderlich" (Mackowiak‚ Wasserman‚ and Levine
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5.1 Z is the standard normal random variable it has a mean= 0 and sd = 1 (-10‚000 is lower bound‚ 10‚000 is upper bound if not given otherwise) Normal curve: The mean‚ median and mode are equal total areas=1 Symmetric and bell shaped never touches the x axis Z= Value-Mean/ standard deviation Properties of the standard normal distribution: cumulative area is close to 0 for z scores close to z= -3.49 cumulative area increases as the z scores increase cumulative area for z=0 is
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1. The heights of boys at a particular age follows a normal distribution with mean 150.3 cm and standard deviation 5 cm. Find the probability that a boy picked at random from this age group has height (a) less than 153 cm (b) less than 148 cm (c) more than 158cm (d) more than 144 cm (e) between 147 cm and 149.5cm (f) between 150 cm and 158 cm 2. The mean mark on a final examination was 72 and the standard deviation was 9. The top 10% of the students are to receive A’s. What is the minimum mark a
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1. Use the sales forecaster’s predication to describe a normal probability distribution that can be used to approximate the demand distribution. Sketch the distribution and show its mean and standard deviation. Let’s assume that the expected sales distribution is normally distributed‚ with a mean of 20‚000‚ and 95% falling within 10‚000 and 20‚000. We know that +/- 1.96 standard deviations from the mean will contain 95% of the values. So‚ we can get the standard deviation by: z = (x -
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send my child to deaf school three days a week and send him to a normal school two days a week. Why? Because‚ the deaf school can teach him how to commutate with others and the normal school can give him a chance to speak with others and become sociability with the normal world. More communicate he have‚ more confidence he can built. Fourthly‚ I will let him to learn lip language. By doing this‚ he can understand the words from normal friends and communicate with them more easily. Fifthly‚ I will
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force necessary to overcome friction depends on the nature of the materials in contact‚ on their roughness or smoothness‚ and on the normal force but not on the area of contact or on the speed of the motion. We find experimentally that the force of friction is directly proportional to the "normal force." When an object is sitting on a horizontal surface the normal force is just the weight of the object. However‚ if the object is on an incline then it is not equal to the weight but is calculated
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Poisson distribution with a parameter λ‚ where E(X) = λ and Variance(X) = λ. 3) We can reasonably approximate the Poisson distribution via a Normal distribution‚ with the same expected value and variance (according to Central Limit Theorem‚ for which you don’t need to know the details). 4) You will also need to use the critical value for the standard Normal distribution (i.e.‚ z-score) associated with the 95% confidence interval. You can look it up in a z-table in any statistics book or online.
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