Chapter 13 Flashcards
Descriptive Statistics
Allow you to summarize the properties of an entire distribution of scores with just a few numbers
Exploratory Data Analysis (EDA)
Used to search for patterns in data
Dummy Codes
Identify category values as numbers to simplify data entry
Bar Graph
Presents your data as bars extending away from the axis representing your independent variable (usually the x-axis although this convention is not always followed)
Line Graph
Represents data as a series of points connected by a line
Scatter Plot
The data from two dependent measures shows correlations
Pie Graph
Shows data in the form of proportions or percentages
Frequency Distribution
Consists of a set of mutually exclusive categories (classes) into which you sort the actual values observed in your data, together with a count of the number of data values falling into each category
Histograms
Resembles a bar graph but shows frequencies
Stemplot
Displays distributions
Skewed Distribution
Has a long tail trailing off in one direction and a short tail extending in the other
Normal Distribution
The bell curve
Outliers
Gaps in the extreme range
Resistant measures
Tend to resist distortion by outliers
Measure of Center (Measure of Central Tendency)
Gives you a single score that represents the general magnitude of scores in a distributions
Measure of Spread (Variability)
How does the data vary from itself
Five-Number Summary
Provides a useful way to boil down a distribution into just a few easily grasped numbers, several of which are resistant to the effect of skew and outliers and all of which are based on the ranks of the scores
Boxplot
Shows the five number summary
Point-Biserial Correlation
Used when one variable is continuous and the other dichotomous (having two possible values)
Rho
Used either when your data are scaled on an ordinal scale (or greater) or when you want to determine whether the relationship between variables is monotonic
Phi Coefficient
Used when both of the variables being correlated are measured on a dichotomous scale
Linear Regression
Simple correlational techniques, establishes the direction and degree of relationship between two variables
Bivariate Linear Regression
Used to find the straight line that best fits the data plotted on a scatter plot
Least-Squares Regression Line
Measured from the y-axis to minimize the sum of the squared deviations
Standard Error Estimate
Estimate of the amount of error in prediction
Coefficient of Determination
The square of the correlation coefficient
Coefficient of Nondetermination
Subtracting Coefficient of Determination from 1.0
Correlation Martix
After computing all possible correlations, displaying them in a table to make it easier to read
