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PSTAT MIDTERM EXAM STUDY GUIDE | QUESTIONS AND ANSWERS | COMPLETE SOLUTIONS REVIEW | UPDATED 2026/2027 | INSTANT DOWNLOAD

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This PSTAT Midterm Exam study guide provides a comprehensive review of introductory statistics concepts, including populations and samples, parameters and statistics, experimental and observational studies, random sampling, random assignment, probability, distributions, and hypothesis testing. The material is organized in a clear question-and-answer format to help students strengthen their understanding of core statistical principles and exam concepts. Key topics include quantitative and categorical variables, skewed and symmetric distributions, measures of center and spread, p-values, significance testing, sampling methods, and statistical notation commonly assessed in introductory statistics courses. This resource serves as an effective review tool for midterm preparation and statistics coursework.

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PSTAT midterm EXAM QUESTIONS AND ANSWERS

WITH COMPLETE SOLUTIONS VERIFIED




parameter a numerical characteristic of a population


statistic a numerical characteristic of a sample


quantitative variable takes numerical values for which it makes

sense to find an average


categorical variable a variable that names categories

(whether with words or numerals)


symmetric distribution uses the mean and SD for center and

spread, affected by outliers


skwed distrubution uses the median and IQR for center and

spread, mean follows tail, resistant to

outliers


skewed left mean < median, ski left


skewed right mean > median

, population A group of were interested in studying


experiment the rearscher determines participant

groups, used to see causation, random

assignment


observational study reaershcer doesnt make changes or assign

groups


random sampling Samples naturally vary, likely

representative ofthe population, avoiding

bias, meaning we can generalize to the

whole population, GENERALIZE


random assignemnt How participants get assigned to treatment

groups in experiments, used for

causation, CAUSATION

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