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# Exam Study Guides 1-3 MATH-10041-002

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This 4 page Study Guide was uploaded by Amy Turk on Saturday April 2, 2016. The Study Guide belongs to MATH-10041-002 at Kent State University taught by Dr. Joseph Minerovic in Spring 2016. Since its upload, it has received 63 views. For similar materials see Introductory Statistics in Math at Kent State University.

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Date Created: 04/02/16

Exam One Study Guide ● Population: the entire set of individuals we want to know about ○ sample = smaller set used to make generalizations about the population ○ parameter = numerical measure of the population ■ not an actual part of the population ○ statistic = numerical measure of the sample ■ not an actual sample ● variables in stats = characteristics of individuals ○ two main parts of stats: ■ data and variation ● random sample = allowing each individual in population to have an equal chance of being chosen for the sample ● data types: ○ categorical = qualitative data ○ numerical = quantitative data ○ some categorical data can include numbers ● stacked data = when each row in a table represents one person ○ coding ○ unstacked = when there is more than one person in a row ● sampling techniques ○ cluster = when you randomly select a group to be in your study, and then survey the entire group ○ stratified = when you divide the sample into 2 groups and then randomly select individuals separately in those two groups ○ systematic = ex. choosing every third person ■ you must find a place to randomly start ○ simple random sampling = pulling from a hat or using a random number table ○ convenience sampling = sampling the first couple people who pass by you ■ sometimes its used, but its inefficient because its not a random sample… not everyone had the same equal opportunity to be surveyed ● 2 way tables: use percents when the total number of the individual groups are different ○ percents are a more accurate generalization to the population than raw scores ● observational studies = when the researcher does not have a control group ○ observational studies show association, not causation, because there may be a confounding variable… a reason why the outcome variable occured ■ confounding variable is unrelated to the experiment ● controlled experiment ○ 4 key features: ■ treatment variable = predictor variable, independent ■ outcome variable = response variable, dependent ■ placebo = a fake pill or sugar pill given to the control group without them knowing ■ double-blind = when the researcher isn’t aware which group is the control group and which one is the treatment group ● placebo effect = when the control starts acting like they’ve taken the real pill ○ it’s all psychological ● categorical data displays ○ pie chart ○ bar graph ○ pareto ○ frequency distribution and relative frequency distribution ● numerical data displays ○ histogram ○ stem and leaf plot ○ frequency distribution and relative frequency distribution ● describing distributions ○ unimodal, bimodal, or multimodal ■ count the number of mounds ○ potential outliers ○ symmetric ■ uniform ■ bell shaped ■ u shaped ○ skewed ■ left skewed ■ right skewed ● changing a frequency to a relative frequency ○ divide the number by the combined total numbers (percentage) Exam 2 Review ● the mean represents the typical value in a set of data for symmetric distributions ● if an observation has a z-score of zero, then it is equal to the mean ● the interquartile range is the range of the middle 50% of the data ● when the distribution is skewed, the median and IQR are used ● the median is resistant to outliers and extreme values because it orders the data from lowest to highest and looks at the middle value ○ the highest value does not change the order, so it does not change the median ● potential outliers are observations that are a distance of more than 1.5 interquartile ranges below the first quartile or above the 3rd ● weak associations result in a large amount of scatter in the scatterplot ● the stronger the association, the better the model is for prediction ● when you have an influential point in your data, do the regression and correlation with and without these points and comment on the differences ● the empirical rule applies to distributions that are symmetric and unimodal ● the interquartile range is the said to be a resistant measure ● the coefficient of determination (r squared) measures how well the data fit the linear model ● extrapolating = when a researcher attempts to apply a regression equation to predictor values that extend beyond the range of data EXAM 3 REVIEW ● empirical probabilities are short-run relative frequencies based on an experiment ● simulations = experiments used to produce empirical probabilities ● the sample space = the set of all possible and equally likely outcomes of the experiment ● outcomes that are in A or B are outcomes that are only in A, only in B, or in both ● two events are associated when one depends on the other ● if events A and B are independent, to find the probability of event A and B, multiply the probability of A by the probability of B ● a probability distribution indicates the possible outcomes of a random experiment and the probability that each of those outcomes will occur ● the total area under a probability density curve is 1, because it represents the probability that the outcome will be somewhere on the x-axis

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