Class Notes (807,723)
United States (312,768)
ST 260 (5)
Cochran (4)
Lecture 3

ST 260 Lecture 3: Stats Chapter 3 Notes

5 Pages
Unlock Document

University of Alabama
ST 260

Stats Chapter 3 Notes Numerical Summaries Measures of Locations Introduction • Population of interest: things we wish to learn about • Key characteristics: typical value of variation • Parameters: true values for a population Key Features of Data Distributions • Shape • Typical Value • Spread • Outliers First Principles • Numerical summaries should quantify key characteristics of a data set Measures of Location/Center • Mean: works best with symmetric distributions ➢ Sum of the data values divided by the number of data values • Median: skewed distributions or distributions with outliers ➢ Middle value in the ordered data set • Mode: categorical variables ➢ The most frequently occurring value • Trimmed Mean: skewed distributions or distributions with outliers ➢ Average of data values omitting the extremes Key Concepts • Relationship between statistics and parameters • Select appropriate numerical methods • Calculate common numerical summaries of data Measures of Variation Objectives • Calculate common numerical summaries of data • Select appropriate numerical methods • Describe the relationship between statistics and parameters Measures of Variance • Sample Variance: symmetric distributions ➢ Total of all (X – average of X)^2 / (# of X’s – 1) • Sample Standard Deviation: symmetric distributions • Data: One variable – continuous quantitative • Range: distributions without outliers ➢ Maximum value – minimum value • Interquartile Range: distributions with outliers Five-Number Summary The Five Number Summary • Minimum (min): Smallest data value • First Quartile (Q1): Upper boundary for lowest 25% of data values • Second Quartile (Q2 or median): 50 percentile • Third Quartile (Q3): Lower boundary for largest 25% of data values • Maximum (max): Largest data value Measures of Association Objectives • Calculate sample variance and correlation • Relate numerical summaries with graph Correlation • Correlation is a measure of linear association • Not necessarily causation – cause and effect • Just because two variables are highly correlated, it does not mean that one variable is the cause of the other Correlation Coefficient • Takes on values between -1 and +1 • Values near -1  strong negative linear correlation • Values near +1  strong positive linear correlation • Correlation near 0  weak linear relationship
More Less

Related notes for ST 260

Log In


Don't have an account?

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.