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Basic Statistics Study Guide Exam 1

by: Elizabeth Schnarr

Basic Statistics Study Guide Exam 1 Stat 190-01

Marketplace > Truman State University > Math > Stat 190-01 > Basic Statistics Study Guide Exam 1
Elizabeth Schnarr
Truman State

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About this Document

This study guide cover what's going to be on our next exam. It covers chapters 1, 2, and 5. Hope this helps!
Basic Statistics
Sunghoon Chung
Study Guide
Math, Stats, basic, Statistics, stats190, Probability, Table, graph, variables
50 ?




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This 5 page Study Guide was uploaded by Elizabeth Schnarr on Wednesday September 14, 2016. The Study Guide belongs to Stat 190-01 at Truman State University taught by Sunghoon Chung in Fall 2016. Since its upload, it has received 26 views. For similar materials see Basic Statistics in Math at Truman State University.


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Date Created: 09/14/16
Statistics Study Guide  Exam 1      Chapter 1:    Definitions to know:  ● Statistics is the science of collecting, organizing, summarizing, and analyzing  information to draw conclusions or answer questions. In addition, statistics is  about providing a measure of confidence in any conclusions  ● Data is a fact or proposition used to draw a conclusion or make a decision  ● The entire group of individuals to be studied is called the population  ● An individual is a person or object that is a member of the population being  studied  ● A sample is a subset of the population that is being studied  ● Descriptive statistics consist of organizing and summarizing data  ● Statistic is a numerical summary based on a sample  ● Inferential statistics uses methods that take results from a sample, extends them  to the population, and measures the reliability of the result  ● Parameter is a numerical summary of a population  ● Variables are the characteristics of the individuals within the population  ● Discrete variable is a quantitative variable that either has a finite number of  possible values or a countable number of possible values  ● Continuous variable is a quantitative variable that has an infinite number of  possible values it can take on and can be measured to any desired level of  accuracy  ● Confounding in a study occurs when the effects of two or more explanatory  variables are not separated  ●  Lurking variable is an explanatory variable that was not considered in a study,  but that affect the value of the response variable in the study  ● Simple random sampling is if every possible sample of size n has an equally  likely chance of occurring          Other topics to know:  1. Observational Study vs. Designed Experiment  An observational study measures the value of the response variable without attempting  to influence the value of either the response or explanatory variables and a designed  experiment is like the experiments we’ve all learned about in our science classes    2. What are the steps for obtaining a simple random sample?  1) Obtain a frame that lists all the individuals in the population of interest  2) Number the individuals in the frame 1 ­ N   3) Use a random number table, graphing calculator, or statistical software to randomly  generate n numbers where n is the desired sample size    3. What are the three different types of bias?  Sampling, Response, and Nonresponse          Chapter 2:    Definitions to know:  ● Frequency distribution lists each category of data and the number of occurrences  for each category of data  ● Relative frequency is the proportion (or percent) of observations within a  category and is found using the formula  ● Relative frequency distribution lists the relative frequency of each category of  data  ● Lower class limit of a class is the smallest value within the class  ● Upper class limit of a class is the largest value within the class  ● Class width is the difference between consecutive lower class limits              Other topics to know:    1. Know what a relative frequency table?      2. Know what the different kinds of graphs and charts look like?  3. Know if a graph is uniform, skewed right, skewed left, or a bell­curve?  4. Know the difference between unimodal, bimodal, or multimodal?        Chapter 5    Definitions to know:  ● Experiment is an act or process of observation that leads to a single outcome  that cannot be predicted with certainty  ● Probability is a measure of the likelihood of a random phenomenon (experiment)  or chance behavior  ● Sample Space, S, of a probability experiment is the collection of all possible  outcomes  ● Event is any collection of outcomes from a probability experiment  ● A probability model lists the possible outcomes of a probability experiment and  each outcome’s probability  ● Probability of an event is the sum of the probabilities of the sample points in the  sample space for the event  ● Complement of E, denoted EC, is all outcomes in the sample space S that are  not outcomes in the event E      Other topics to know:  1. What are the rules of probability?  The probability of any event must be greater than or equal to 0 and less than or equal to  1. The sum of the probabilities must also be equal to 1.    2. Understand the Empirical Approach  The probability of an event equals the frequency of E divided by the number of trials of  the experiment    3. Understand the Classical Method  Probability equals number of ways that E can occur divided by the number of possible  outcomes    4. What does it mean for two events to be disjoint?  Two events are disjoint if they have no outcomes in common    5. What is the General Addition Rule?  P(E or F) = P(E) + P(F) ­ P(E and F)      6. What is the complement of an event?  The complement is if anything other than the event occurs (Ex: Toss an even number,  and the complement would be toss an odd number)    7. What does it mean for two events to be independent?  If the occurrence of event E in a probability experiment does not affect the probability of  event F    8. What does it mean for an event to be dependent?   If the occurrence of event E in a probability experiment affects the probability of event  F.    9. What is the multiplication rule for independent events?  P(E and F) = P(E) X P(F)    10. WHat is the Conditional Probability Rule?  P( A∣B)= P( A and B)/P( B)        .         


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