Chapter 6, Problem 6.17AC

The table given shows the length, in feet, of dolphins at an aquarium. 7 15 10 18 18 15 9 22 Are there any outliers in the data?    There is an outlier at 22 feet. There is an outlier at 7 feet. There are outliers at 7 and 22 feet. There are no outliers.

Start by summarizing the key events in a clear and persuasive manner on the article Endrikat, J., Guenther, T. W., & Titus, R. (2020). Consequences of Strategic Performance Measurement Systems: A Meta-Analytic Review. Journal of Management Accounting Research?

The table below was compiled for a middle school from the 2003 English/Language Arts PACT exam.   Grade   6 7 8 Below Basic 60 62 76 Basic 87 134 140 Proficient 87 102 100 Advanced 42 24 21   Partition the likelihood ratio test statistic into 6 independent 1 df components. What conclusions can you draw from these components?

What is the value of the maximum likelihood estimate, θ, of θ based on these data? Justify your answer. What does the value of θ suggest about the value of θ for this biased die compared with the value of θ associated with a fair, unbiased, die?

Show that L′(θ) = Cθ394(1 −2θ)604(395 −2000θ).

a) Let X and Y be independent random variables both with the same mean µ=0. Define a new random variable W = aX +bY, where a and b are constants. (i) Obtain an expression for E(W).

The table below shows the estimated effects for a logistic regression model with squamous cell esophageal cancer (Y = 1, yes; Y = 0, no) as the response. Smoking status (S) equals 1 for at least one pack per day and 0 otherwise, alcohol consumption (A) equals the average number of alcohoic drinks consumed per day, and race (R) equals 1 for blacks and 0 for whites. Variable Effect (β) P-value Intercept -7.00 <0.01 Alcohol use 0.10 0.03 Smoking 1.20 <0.01 Race 0.30 0.02 Race × smoking 0.20 0.04 Write-out the prediction equation (i.e., the logistic regression model) when R = 0 and again when R = 1. Find the fitted Y S conditional odds ratio in each case.  Next, write-out the logistic regression model when S = 0 and again when S = 1. Find the fitted Y R conditional odds ratio in each case.

The chi-squared goodness-of-fit test can be used to test if data comes from a specific continuous distribution by binning the data to make it categorical. Using the OpenIntro Statistics county_complete dataset, test the hypothesis that the persons_per_household 2019 values come from a normal distribution with mean and standard deviation equal to that variable's mean and standard deviation. Use signficance level a = 0.01. In your solution you should 1. Formulate the hypotheses 2. Fill in this table Range (-⁰⁰, 2.34] (2.34, 2.81] (2.81, 3.27] (3.27,00) Observed 802 Expected 854.2 The first row has been filled in. That should give you a hint for how to calculate the expected frequencies. Remember that the expected frequencies are calculated under the assumption that the null hypothesis is true. FYI, the bounderies for each range were obtained using JASP's drag-and-drop cut function with 8 levels. Then some of the groups were merged. 3. Check any conditions required by the chi-squared…

Suppose that you want to estimate the mean monthly gross income of all households in your local community. You decide to estimate this population parameter by calling 150 randomly selected residents and asking each individual to report the household’s monthly income. Assume that you use the local phone directory as the frame in selecting the households to be included in your sample.  What are some possible sources of error that might arise in your effort to estimate the population mean?