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Basic Stats Notes: Weeks 1 and 2

by: Elizabeth Schnarr

Basic Stats Notes: Weeks 1 and 2 Stat 190-01

Marketplace > Truman State University > Math > Stat 190-01 > Basic Stats Notes Weeks 1 and 2
Elizabeth Schnarr
Truman State
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About this Document

These notes cover all the concepts that will be on our next exam. I have included Chapter one and what has been covered of Chapter 2 so far in class. Chapter 1 includes topics such as the process o...
Basic Statistics
Sunghoon Chung
Class Notes
Math, Stats, Statistics, Mathematics, Graphs, tables, frequency distribution, Pie, chart, histogram




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This 8 page Class Notes was uploaded by Elizabeth Schnarr on Friday September 2, 2016. The Class Notes belongs to Stat 190-01 at Truman State University taught by Sunghoon Chung in Fall 2016. Since its upload, it has received 15 views. For similar materials see Basic Statistics in Math at Truman State University.


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Date Created: 09/02/16
Statistics  Chapter 1: Data Collection  Section 1: Intro to the Practice of Stats        Definitions    ● Statistics is the science of ​collecting​, ​organizing​, ​summarizing​, and ​analyzing  information to draw conclusions or answer questions    ● Data­ fact or proposition used to draw a conclusion or make a decision  ● Population­ the entire group of individuals to be studied  ● Sample­ a subset of the population that is being studied    Descriptive stats­ consists of organizing and summarizing data     Inferential stats­ uses methods that take results from a sample, extends them to the  population, and measures the reliability of the result    ● S​tatistic­ numerical summary based on a s ​ ​ample  ● P​arameter­ statistical summary based on a p ​ ​opulation   ● Variables­ the characteristics of the individuals within a population      Qualitative or Categorical Variable:  ● Allow for classification of individuals based on some attribute or characteristic  ● Ex: eye color, gender, zipcode, etc.    Quantitative Variable:  ●  Provide numerical measures of individuals  ● Ex: income, height, etc.  ● If you can take the mean (average) then it’s a quantitative variable    Discrete Variable  ● A quantitative variable that either has a finite number of possible values or a  countable number of possible values  ● Ex: number of children  ● Using counting values: 1,2,3…      Continuous Variable  ● 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  ● Ex: daily intake of whole grains  ● If you need to use a decimal point it’s most likely a continuous variable                Statistics  Chapter 1: Data Collection  Section 2: Observational Studies vs. Designed Experiments      Variables  ● Response variable is whether or not something happened due to another  variable  ● The explanatory variable is what causes the response variable    Observational Study vs. Designed Experiment  ● Observational Study​­ researcher observes the behavior of the individuals in the  study without trying to influence the outcome of the study  ● Designed Experiment​­ when a researcher assigns the individuals in a study to a  certain group, intentionally changes the value of the explanatory variable, and  then records the value of the response variable       Confounding variable­ in a study when two or more explanatory variables are not  separated     Lurking variable­ an explanatory variable that was not considered in a study, but that  affects the value of the response variable     Census­ list of all the individuals in a population  along with certain characteristics of  each individual        Statistics  Chapter 1: Data Collection  Section 3: Simple Random Sampling      Sampling  ● Random sampling­ process of using chance to select individuals from a  population to be included in the sample  ● Simple random sampling​­ each sample has an equal chance of occurring    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              Statistics  Chapter 1: Data Collection  Section 4: Other Effective Sampling Methods       A Convenience Sample   ● The individuals in the sample are easily obtained          Statistics  Chapter 1: Data Collection  Section 5: Bias in Sampling      Types of Bias  ● Sampling Bias​­ the technique used to obtain the individuals to be in the sample  tends to favor one part of the population over another   ● Nonresponse Bias​­ when individuals selected to be in the sample who do not  respond to the survey have different opinions from those who do  ● Response Bias​­ when the answers on a survey do not reflect the true feelings of  the respondent    Errors  ●  Nonsampling errors­ errors that result from sampling bias, response bias, or  data­entry error (can be avoided)  ● Sampling errors­ error that results from using a sample to estimate info about a  population (can’t be avoided)                  Statistics  Chapter 2: Summarizing Data in Tables and Graphs  Section 1: Organize Qualitative Data in Tables      Definitions  ● Frequency Distribution: lists ​each category​ of data and the ​number of occurences  for each category of data  ● Relative Frequency: the ​proportion (or percent)​ of observations within ​a category  and is found using the formula:   Frequency  Sum of all    ● Relative Frequency Distribution: lists the ​relative frequency​ of ​each category​ of  data      Frequency Table:                      Types of Graphs:    Bar Graph            Pareto Chart (goes in decreasing order)                    Pie Chart              Statistics  Chapter 2: Summarizing Data in Tables and Graphs  Section 2: Organize Discrete and Continuous Data      Constructing Frequency:        Definitions:  ● Lower Class Limit: the smallest value within the class (Ex in chart: 25)  ● Upper Class Limit: the largest value within a class (Ex in chart: 44)  ● Class Width: the difference between consecutive lower class limits (Ex in chart:  10)    Ex:   Age  Number  25­34  2,132  35­44  3,928      Histograms of Continuous Data:        ● Usually has no gaps, while bar graphs usually have gaps and use discrete data  ● The boxes in the histogram are sometimes called ​bins 


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