Basic Stats Notes: Weeks 1 and 2
Basic Stats Notes: Weeks 1 and 2 Stat 190-01
Popular in Basic Statistics
Popular in Math
verified elite notetaker
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.
Reviews for Basic Stats Notes: Weeks 1 and 2
Report this Material
What is Karma?
Karma is the currency of StudySoup.
You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!
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 ● Statistic numerical summary based on a s ample ● Parameter 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
Are you sure you want to buy this material for
You're already Subscribed!
Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'