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    MM207 Final Project Name: Eddie S. Jackson 1. Using the MM207 Student Data Set: a) What is the correlation between student cumulative GPA and the number of hours spent on school work each week? Be sure to include the computations or StatCrunch output to support your answer. My answer : 0.27817234 (from StatCrunch): Correlation between Q10 What is your cumulative Grade Point Average at Kaplan University? and Q11 How many hours do you spend on school work each week? is: 0.27817234 b)

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    the items included in the sample. Required 1. Identify the audit objectives that are accomplished by this test. 2. Determine sample size based on the following audit judgments. a. Tolerable misstatement is assessed at $325‚000. b. The risk of incorrect acceptance is assessed at 37 percent. c. Anticipated misstatement is assessed at $100‚000. 3. Develop a scenario that is consistent with setting the risk of incorrect acceptance at 37 percent. 4. Select a PPS sample of the above inventory population

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    Chapter 9 Case Study

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    Statistics – Case Chapter 9 1. It is not proper to multiply the average order size by the number of addresses (1.3 million people = population) in the target mailing because the sample is representative of the 600‚000 people in the database‚ not the 1.3 million target population‚ thus you cannot use the average of the sample as an estimate for the population. Also‚ multiplying by 1.3 million would suggest that the entire 1.3 million people would be purchasing. 2. It is better to multiply the endpoints

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    Qnt 561 Week2

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    zero‚ what would this mean? * Sampling error is the difference between the statistic estimated from a sample and the true population statistic. It is not impossible for the sampling error to not be zero. If the sampling error is zero then the population is uniform. For example if I were evaluating the ethnicities of a populations and everyone is the population was Black then taking any sample would give me the true proportion of 100% Black. 22. List the reasons for sampling. Give an example of

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    No Yes Improvements in any process start Statistical process control‚ which is the random testing of a sample of output from the process to determine whether the process is producing items within a preselected range (Chase‚ Jacobs‚ & Aquilano‚ 2006). These samples may be either fit or unfit for example taking random samples from a batch of newly made Motorola I Pad. Testing may discover that some will work accordingly whereas other will not power on. Companies

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    Week Three Homework

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    Collegiate Athletic Association (NCAA) reported that the mean number of hours spent per week coaching and recruiting by college football assistant coaches during the season was 70. A random sample of 50 assistant coaches showed the sample mean to be 68.6 hours‚ with a standard deviation of 8.2 hours. A. Using the sample data‚ construct a 99% confidence interval for the population mean. The confidence interval is as follows: 68.6- 2.58*8.2/ã50‚ 686+ 2.58*8.2/ã50 CI(65.6-71.5) B. Does the 99%

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    CPHypothTest4

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    $52 with a standard deviation of $4.50. A group of smart statistics students thinks that the average cost is higher. In order to test the bookstore’s claim against their alternative‚ the students will select a random sample of size 100. Assume that the mean from their random sample is $52.80. Perform a hypothesis test (6 step procedure outlined in class) at the 5% level of significance and state your decision. 2. A certain chemical pollutant in the Genesee River has been constant for several years

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    while still being able to include the full six year history. The team will take six years’ worth of beverage sales data‚ 2‚191 data points‚ from the flagship restaurant‚ satisfying the minimum sample size requirement of 385 using a 95% confidence level with a 5% margin of error (see Appendix 1.0‚ Sample Size Calculation). This data will then be matched with the publically accessible temperature highs by applicable region available from United States National Weather Service. This data will be stored

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    Hypothesis Tecsting

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    distribution when the null hypothesis is true‚ or any in which this is asymptotically true‚ meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by making the sample size large enough. The Chi-Square ( ) test is the most popular non-parametric test/methods‚ to test the hypothesis. The symbol is the Greek letter “chi”. Like other hypothesis testing procedures‚ the calculated value of -test statistics is compared

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    The Logic of Anova

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    ANOVA. Many statisticians think of ANOVA as an extension of the difference of means test because it’s based‚ in part‚ on a comparison of sample means. At the same time‚ however‚ the procedure involves a comparison of different estimates of population variance—hence the name analysis of variance. Because ANOVA is appropriate for research involving three or more samples‚ it has wide applicability. Imagine for a moment that we want to know if scores on an aptitude test actually vary for students in different

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