Learning objectives for Stat 370 A. Introduction to types of studies, data collection (Reading: Chapters 1 and 2) A1. Given a study, identify the population, sample, parameters, and statistics A2. Given a variable, determine whether it is qualitative or quantitative A3. Given a quantitative variable, determine whether it is discrete or continuous A4. Explain the difference between an observation study and a designed experiment A5. Determine the appropriate conclusions from an observational study A6. Given a study, determine whether it is observational or experimental A7. Explain the difference between a simple random sample and a convenience sample A8. Given a sample design, determine whether it is a simple random sample B. Graphical and numerical summaries (Reading: Chapter 3; relevant software http://statcrunch.stat.ncsu.edu/) B1. Given a set of discrete data, make frequency table and histogram by hand B2. Given a set of continuous data, make classes, frequency table for classes, and then draw histogram by hand B3. Given a set of raw data, make histogram using software B4. Given a histogram, determine the number of individuals in a particular range B5. Given a histogram, describe the distribution s shape (left skewed, right skewed, symmetric, unimodal or multimodal) B6. Identify outliers for a given dataset B7. Given side-by-side boxplots, compare and contrast key features of the groups represented by the boxplots B8. Given a boxplot, determine the shape of the distribution (skewed right, skewed left, symmetric) B9. Given a boxplot, determine the 5-number summary B10. Given a five-number summary, create the corresponding boxplot B11. Explain the impact of adding an outlier on summary statistics such as mean, median and standard deviation B12. Given a raw data set with not many data points, determine the five-number summary and draw the corresponding boxplot B13. Given a data set, determine the min, max, quartiles, mean, standard deviation (using software, and also by hand if data set is small) B14. Explain how the mean and median are related for different shapes of a distribution (skewed left, skewed right or symmetric) B15. List the following characteristics of the standard deviation a. The standard deviation must be greater than or equal to zero b. When the standard deviation is zero, there is no spread every number of the data set is the same B16. Describe how linear transformations affect median, mean, and standard deviation B17. Given sample mean and standard deviation of a unimodal dataset, be able to apply the empirical rule. Some notes on histograms: For discrete data, the center of the rectangle is located at the value of the category For continuous data, the rectangle covers the class interval Some notes on finding the 5-number summary and boxplots: The first step for boxplots is to find the 5-number summary (min-Q1-Q2-Q3-max). First order the data, e.g. 56, 67, 68, 72, 74, 75, 88, 90, 97, 99 Min and max have obvious meanings. The median splits the data into 2 parts. We want the same amount of observations to both sides of the median. So for this data set, median is between 74 and 75 (take the average 74.5). To find Q1 and Q3, find median of the two halves of the data, the 5 data points to each side of the median. So Q1 is 68, Q3 is 90. If we add 100 to the data, so that the data set is now: 56, 67, 68, 72, 74, 75, 88, 90, 97, 99, 100 then the median is 75, Q1 is 68, Q3 is 97. If we have 12 observations, 56, 67, 68, 72, 74, 75, 88, 90, 97, 99, 100, 100, the median is (75+88)/2=81.5, Q1=70, Q3=98 Drawing the box o Can be drawn vertically or horizontally (only one axis indicating the data values) o Box ends are at Q1 and Q3 o Length of box is Interquartile Range = IQR o Line across box at median Upper fence at 1.5 IQR from Q3, lower fence at 1.5 IQR from Q1 (aren t really needed on graph) o Whiskers to last actual data value within the fences o Might be at min or max o May need to indicate outliers with individual data points. C. Design of experiments (Reading: Chapter 2) C1. Given a designed experiment, identify the treatments, response variables, and experimental units C2. List reasons for variability of responses (treatment effect, experimental error) C3. List sources of experimental error C4. Explain what it would mean to control for a variable C5. Explain why we would want to control for a variable C6. List two ways to deal with experimental error that may remain after controlling variables (randomization, blocking) C7. Define replication C8. Give reasons for replication in designed experiments C9. Define randomization C10. Explain why one wants to randomize in a designed experiment C11. Define a block (homogenous subset of experimental units) C12. Explain why one would want to block a subset of experimental units C13. Describe two ways to randomize (completely randomized design, <a href="/keyword/randomized-complete-block-design/" ><a href...