Stats 1023 Midterm exam review.docx
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Department
Statistical Sciences
Course
Statistical Sciences 1023A/B
Professor
Jennifer Waugh
Semester
Fall

Description
Stats 1023 Midterm exam Textbook notes Chapter 1 To conduct a statistical study properly, one must: 1. Get a representative sample 2. Get a large enough sample 3. Decide whether the study should be an observational study or randomized experiment Chapter 2 Data: information collected through numbers or other pieces which has meaning attached The two most common sources for statistical studies are academic conferences and scholarly journals Academic conferences: held annually, in which researchers share information with others. Used to catch up on news stories - Downfall: there is unlikely to be a corresponding written report by researchers, so it is diffilcult for researchers to obtain further information Scholarly journal: reporters routinely read these journals when they are published, and get press releases. Seven Critical Components 1. The source of the research and its funding 2. The researchers who had contact with the participants 3. The individuals studied and how they were selected 4. The exact nature of the measurements made and questions asked 5. The setting in which the measurements were taken 6. Differences in the groups being compared, in addition to the factor of interest 7. The extent or size of any claimed effects or differences Chapter 3 Pitfalls encountered when asking questions in a survey: 1. Deliberate bias Phrasing questions to obtain a certain answer 2. Unintentional bias Questions worded where the meaning is misinterpreted 3. Desire to please Desire to please the person asking the question. Understate responses about undesirable social habits 4. Asking the uninformed People do not like to admit that they don’t know what you are talking about when you ask them a question 5. Unnecessary complexity Questions need to be simple 6. Ordering of questions Requiring respondents to think about something that they may have not considered, and then order another question to obtain those results 7. Confidentiality of anonymity Sometimes people answer questions based on the degree to which they think they are anonymus Open Question: answer in their own words Closed Questions: list of answers to choose from Categorical variables: are those we can place into a category but may not have any logical ordering. (male and female) Ordinal variable: categories that have natural order. (strongly agree – strongly disagree) Nominal variable: do not have natural order Measurement variables (quantitative variables): are those which we can record a numerical value and then order respondents accordingly. (IQ) Interval variable: a measurement where it makes sense to talk about differences, but not as ratios. (weather. 20 degrees last night, 40 degrees today) Ratio variable: has a meaningful measure of 0, and makes sense to talk about as a ratio. (pulse rate at 60 before you start, and 120 after. You doubled it) Validity: a measurement is one that actually measures what it claims to measure Reliability: something that will give you the same result time after time Bias measurement: prejudice in one direction Measurement error: amount by which each measurement differs from the true value Natural variability: natural changes across time in the system (unemployment rate) Chapter 4 Randomized experiments Experiment: measures the effect of manipulating the environment in some way Randomized experiment: manipulation is assigned in a random basis Explanatory variable: measure the result of the feature being manipulated (independent variable) Response variable: the outcome of a manipulation (dependent variable) Observational study: resembles an experiment except that the manipulation occurs naturally and is observed Meta Analysis: a quantitative review of a collection of studies all done on a similar topic (a bunch of studies on the same thing- mammogram results) Case study: an indepth examination of a small number of individuals Margin of error: the measure of accuracy in numbers. The sample proportion differs from the population proportion by more than the margin of error less than 5% of the time, or 1 in 20 surveys. Advantages of sample surveys: - When a census isn’t possible (blood concentration) - Speed (amount of times it takes to conduct) - Accuracy (devote your resources to most accurate info possible from the sample selected) Simple random sampling: the ability of a relatively small sample to accurately reflect the population Probability sampling plans: everyone in the population must have a specified chance of making It into the sample Simple random sampling: every conceivable group of people of the required size has the same chance of being selected Need two thing: - Random numbers: a source of random numbers generated to help calculate - A list of units in the population Stratified random sampling: collected by dividing the population into units groups called strata and taking a random sample from each. (example political parties) Cluster sampling: population is divide into clusters,
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