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# Intro & Data

### Lesson 3 Quantitative Analysis (Statistics)

TermDefinition
Population Total number of some entity
Sample A subset of the population Population of interest
Descriptive Statistics Describes the characteristics of a population
Inferential Statistics Determines characteristics of a population based on observations made on a sample from the population
Mean Average of a distribution
Median The middle number of a ranked distribution
Mode Most frequent number in a distribution
Nominal Data Classified into mutually exclusive groups that lack intrinsic order
Ordinal Data Has values that are ranked so that inferences can be made regarding the magnitude
Nominal Data Examples Race, social security number, and sex
Ordinal Data Examples Letter grade or scale of 1 to 10
Interval Data Data that has an ordered relationship with a magnitude (0 exists within interval data)
Interval Data Examples Test scores, temperature, or time on a clock
Ratio Data Has an ordered relationship and equal interval (0 does not exist in ratio data)
Ratio Data Examples Weight on a scale, ruler measurements, or salary earned
Qualitative Variable Also called a categorical variable, are variables that are not numerical (nominal or ordinal)
Quantitative Variable Variables that are measured on a numeric scale (interval or ratio)
Continuous Variable Can have an infinite number of values
Continuous Variable Examples Persons weight or age
Discontinuous Variable Can only have two possible values
Discontinuous Variable Examples Employed or unemployed
Hypothesis Test Allows for a determinations of possible outcomes and the interrelationship between variables
Null Hypothesis Ho, no statistical significance between the two variables in the hypothesis The reference, a statement one want to reject
Alternative Hypothesis H1, proposes the relationship Research hypothesis, a statement one wants to find support for Main purpose is to reject the null NEVER accept the alternative, ALWAYS reject the null
Normal Distribution One that is symmetrical around the mean (bell curve)
Skew to the Right Has few high numbers (out liars), that pull to the right (negative)
Skew to the Left Has a few low numbers (out lairs), pulls to the left (positive)
Range Difference between highest and lowest scores in a distribution
Variance Average squared difference of score from the mean of a distribution How far the numbers lie from the mean Squaring deviation from the mean/# of observations
Standard Deviation Square root of the variance
Coefficient of Variation Is a measure of relative variability Measured by taking the standard deviation and dividing by the mean
Standard Error The standard deviation of a sampling distribution Indicates the degree of sampling fluctuation
Confidence Interval Gives an estimated range of values which is likely to include an unknown population parameters Width of the interval gives us an idea of how uncertain we are about the unknown parameter
Chi Squared Test Provides a measure of the amount of difference between two frequency distributions Determines is there is a significant difference between expected and observed frequencies Commonly used for probability distribution and inferential statistics
Z-Score Measure of the distance, in standard deviation units from the mean Allows one to determine the likelihood or probability that something will happen
Z-Score, Typically used if... Know the population standard deviation Sample size is above 30
T-Score Allows the comparisons of the means of two groups to determine how likely the difference between the tow means occurred by change
T-Score, typically used if... Do not know the population standard deviation Sample size is under 30
ANOVA Analysis on variance Studies the relationship between two variables, the first variable must be nominal and the second is interval
Correlation Tests the strength of the relationship between variables
Correlation Coefficient Indicates the type and strength of the relationship between variables, ranging from -1 to 1 Closer to 1 the stronger the relationship between variables
Regression Test of the effect of independent variables on a dependent variables
Sampling Error Occurs when one has taken a sample from a larger population The sample is not representative of the population as a whole. creating a sampling error
R2 Squaring the correlation coefficient
Non-Sampling Error One that cannot be explained by the representatives of the sample Can occur as a result of respondents misunderstanding a question or misreporting their answer
Probability Sampling Subset from overall population Most reliable, defensible and rigorous method Random, systematic, stratified or cluster
Non-Probability Sampling Convenience (snowball survey) Volunteer Implementation
Discrete Variable Only a finite number of values Special case; binary or dichotomous (only two values)
Distribution Way to formalize values that are likely to be observed Represent a distribution graphically or mathematically
Reject the Null Find evidence in the data = a statistic If the values of a statistic is very different from what it would be under the null hypothesis - then reject
Type 1 Error Probability of making the wrong decision (chance)
Created by: amshinn18