Describing Distributions Numerically Measures of Variation And Boxplots.

Slides:



Advertisements
Similar presentations
Statistical Reasoning for everyday life
Advertisements

DESCRIBING DISTRIBUTION NUMERICALLY
Describing Distributions with Numbers
Median Find the median of the following 9 numbers:
MEASURES OF SPREAD – VARIABILITY- DIVERSITY- VARIATION-DISPERSION
Describing distributions with numbers
Descriptive Statistics Used to describe the basic features of the data in any quantitative study. Both graphical displays and descriptive summary statistics.
Describing Distributions with Numbers ESS chapter 2 © 2013 W.H. Freeman and Company.
Section 1 Topic 31 Summarising metric data: Median, IQR, and boxplots.
Chapter 5 Describing Distributions Numerically Comparing Groups: Step – by – step Mean vs Median Standard Deviation.
Describing distributions with numbers
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 3 Descriptive Statistics: Numerical Methods.
Chapter 3 Looking at Data: Distributions Chapter Three
Revision Analysing data. Measures of central tendency such as the mean and the median can be used to determine the location of the distribution of data.
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 5 Describing Distributions Numerically.
Organizing Data AP Stats Chapter 1. Organizing Data Categorical Categorical Dotplot (also used for quantitative) Dotplot (also used for quantitative)
BPS - 5th Ed. Chapter 21 Describing Distributions with Numbers.
+ Chapter 1: Exploring Data Section 1.3 Describing Quantitative Data with Numbers The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE.
Example Suppose that we take a random sample of eight fuel efficient cars and record the gas mileage. Calculate the following for the results: 33, 41,
More Univariate Data Quantitative Graphs & Describing Distributions with Numbers.
Chapter 1: Exploring Data
Describing Distributions with Numbers
CHAPTER 1 Exploring Data
Chapter 5 : Describing Distributions Numerically I
Describing Distributions Numerically
Numerical Descriptive Measures
CHAPTER 2: Describing Distributions with Numbers
Averages and Variation
NUMERICAL DESCRIPTIVE MEASURES
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
Chapter 5: Describing Distributions Numerically
Warmup What is the shape of the distribution? Will the mean be smaller or larger than the median (don’t calculate) What is the median? Calculate the.
Quartile Measures DCOVA
CHAPTER 1 Exploring Data
Organizing Data AP Stats Chapter 1.
1.3 Describing Quantitative Data with Numbers
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
Measures of Center.
Describing Distributions Numerically
Chapter 1: Exploring Data
CHAPTER 1 Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Numerical Descriptive Measures
MBA 510 Lecture 2 Spring 2013 Dr. Tonya Balan 4/20/2019.
Chapter 1: Exploring Data
Chapter 1: Exploring Data
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
Chapter 1: Exploring Data
The Five-Number Summary
CHAPTER 1 Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
5 Number Summaries.
Describing Distributions with Numbers
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
CHAPTER 1 Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Presentation transcript:

Describing Distributions Numerically Measures of Variation And Boxplots

Range: highest number - lowest number Five number summary: Minimum Q1 Median Q3 Maximum

Boxplot Continued Interquartile Range: IQR = Q3 - Q1 *Tells us how much territory the middle half of the data covers. Percentile: for whole number P (where 1≤P≤99), the Pth percentile of a distribution is a value such that P% of the data fall at or below it and (100-P)% of the data fall at or above it.

Histogram Median-splits the histogram into two halves with equal area Mean-point at which the histogram would balance

Measures of Variation Deviation: how far each data value is from the mean Variance (s 2 ): average (almost) of squared deviations Standard Deviation (s):

Thinking about Variation… The U.S. Census Bureau reports the median family income in its summary of census data. Why do you suppose they use the median instead of the mean? What might be the disadvantages of reporting the mean?

Thinking about Variation… You’ve just bought a new car that claims to get a highway fuel efficiency of 31 mpg. Of course, your mileage will vary. If you had to guess, would you expect the IQR of gas mileage attained by all cars like yours be 30 mpg, 3 mpg, or 0.3 mpg? Why?

Thinking about Variation… A company selling a new MP3 player advertises that the player has a mean lifetime of 5 years. If you were in charge of quality control at the factory, would you prefer that the standard deviation of lifespans of the players you produce be 2 years or 2 months? Why?

Rules about shape, center, and spread 1.If the shape is skewed, report the median and IQR. 2.If the shape is symmetrical, report the mean and standard deviation. IQR is usually larger than the standard deviation. 3.If outliers, report mean and standard deviation with outliers present and with outliers removed.

Summarizing a Distribution A man owned a 1989 Nissan Maxima for 8 years. Being a statistician, he recorded the car’s fuel efficiency (in mpg) each time he filled the tank. He wanted to know what fuel efficiency to expect as “ordinary” for his car. Knowing this, he was able to predict when he’d need to fill the tank again, and notice if the fuel efficiency suddenly got worse, which could be a sign of trouble. What does the data say?

When comparing boxplots Compare the medians, which group has the higher center? Compare the IQRs; which group is more spread out? Judged by the size of the IQRs, are the medians very different? Check for possible outliers. Identify them if you can.

Comparing Boxplots A student designed an experiment to test the efficiency of various coffee containers by placing hot liquid in each of 4 different containers types 8 different times. After 30 minutes she measured the temperature again and recorded the difference in temperature. What can we say about the effectiveness of these four mugs? *Because these are temperature differences, smaller differences mean that the liquid stayed hot.

Measure of Variation Continued Coefficient of Variation: Chebyshev’s Theorem: