5, 8, 13, 17, 22, 24, 25, 27, 29, 30. 8, 10, 22, 24, 25, 25, 26, 27, 45, 72 Graph & Describe.

Slides:



Advertisements
Similar presentations
Measures of Position Percentiles Z-scores.
Advertisements

Chapter 2: Modeling Distributions of Data
AP Statistics: Section 2.2 A. One particularly important class of density curves are Normal curves which describe Normal distributions. Normal curves.
1.2: Describing Distributions
20, 22, 23, 24, 24, 25, 25, 27, 35 Are there any outliers? Draw a skeleton boxplot. Draw a modified boxplot.
Chapter 2: The Normal Distribution
+ Chapter 2: Modeling Distributions of Data Section 2.2 Normal Distributions The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE.
Chapter 2: Modeling Distributions of Data
+ Chapter 2: Modeling Distributions of Data Section 2.1 Describing Location in a Distribution The Practice of Statistics, 4 th edition - For AP* STARNES,
Get out your Density Curve WS! You will be able to describe a Normal curve. You will be able to find percentages and standard deviations based on a Normal.
Chapter 3 Section 2 Normal Distributions. Normal Distributions as Density Curves Normal Curves: Symmetric, Single-Peaked and Bell-Shaped. They describe.
Using the Empirical Rule. Normal Distributions These are special density curves. They have the same overall shape  Symmetric  Single-Peaked  Bell-Shaped.
The distribution of heights of adult American men is approximately normal with mean 69 inches and standard deviation 2.5 inches. Use the rule.
+ Chapter 2: Modeling Distributions of Data Section 2.2 Normal Distributions The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE.
+ Warm Up The graph below shows cumulative proportions plotted against GPA for a large public high school. What is the median GPA? a) 0.8b) 2.0c) 2.4d)
NOTES The Normal Distribution. In earlier courses, you have explored data in the following ways: By plotting data (histogram, stemplot, bar graph, etc.)
+ Chapter 2: Modeling Distributions of Data Section 2.1 Describing Location in a Distribution The Practice of Statistics, 4 th edition - For AP* STARNES,
Section 2.1 Describing Location in a Distribution
+ Chapter 2: Modeling Distributions of Data Section 2.1 Describing Location in a Distribution The Practice of Statistics, 4 th edition - For AP* STARNES,
+ Chapter 2: Modeling Distributions of Data Section 2.1 Describing Location in a Distribution The Practice of Statistics, 4 th edition - For AP* STARNES,
Using the Empirical Rule. Normal Distributions These are special density curves. They have the same overall shape  Symmetric  Single-Peaked  Bell-Shaped.
Chapter 2: Modeling Distributions of Data
+ Chapter 2: Modeling Distributions of Data Section 2.2 Normal Distributions The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE.
+ Chapter 2: Modeling Distributions of Data Lesson 1: Describing Location in a Distribution.
20, 22, 23, 24, 24, 25, 25, 27, 35 Are there any outliers? Draw a skeleton boxplot. Draw a modified boxplot.
Chapter 2 Modeling Distributions of Data Objectives SWBAT: 1)Find and interpret the percentile of an individual value within a distribution of data. 2)Find.
An article on peanut butter reported the following scores (quality ratings on a scale of 0 to 100) for various brands. Construct a comparative stem-and-leaf.
Modeling Distributions
+ Chapter 2: Modeling Distributions of Data Section 2.1 Describing Location in a Distribution The Practice of Statistics, 4 th edition - For AP* STARNES,
SWBAT: Use percentiles to locate individual values within distributions of data. Interpret a cumulative relative frequency graph. Find the standardized.
+ Progress Reports Homework Test Corrections Signed 1.
Chapter 3.3 – 3.4 Applications of the Standard Deviation and Measures of Relative Standing.
Density Curves & Normal Distributions Textbook Section 2.2.
Daniel S. Yates The Practice of Statistics Third Edition Chapter 2: Describing Location in a Distribution Copyright © 2008 by W. H. Freeman & Company.
 2.1 Measures of Relative Standing and Density Curves.
 By the end of this section, you should be able to: › Find and interpret the percentile of an individual value within a distribution of data. › Estimate.
Chapter 2 Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Statistics Objectives: The students will be able to … 1. Identify and define a Normal Curve by its mean and standard deviation. 2. Use the 68 – 95 – 99.7.
Good Afternoon! Agenda: Knight’s Charge-please get started Good things
Using the Empirical Rule
Ninth grade students in an English class were surveyed to find out about how many times during the last year they saw a movie in a theater. The results.
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Unit 1 - Day 1 Introduction to
Using the Empirical Rule
Chapter 2b.
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Describing Location in a Distribution
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Mean and Median.
Chapter 2: Modeling Distributions of Data
Chapter 2: Modeling Distributions of Data
Presentation transcript:

5, 8, 13, 17, 22, 24, 25, 27, 29, 30

8, 10, 22, 24, 25, 25, 26, 27, 45, 72 Graph & Describe

22, 27, 33, 39, 57, 88, 110

Modified Boxplot Mild outliers are represented by shaded circles. Extreme outliers are represented by open circles Whiskers are only extended to largest values that are not outliers.

Create a Modified Boxplot

An article on peanut butter reported the following scores (quality ratings on a scale of 0 to 100) for various brands. Construct a comparative stem-and-leaf plot and compare the graphs. Creamy: Crunchy:

20, 22, 23, 24, 24, 25, 25, 27, 35 Are there any outliers? Draw a skeleton boxplot. Draw a modified boxplot.

Chebyshev’s & The Empirical Rule

Describing Data in terms of the Standard Deviation. Test Mean = 80 St. Dev. = 5

Chebyshev’s Rule The percent of observations that are within k standard deviations of the mean is at least

Facts about Chebyshev Applicable to any data set – whether it is symmetric or skewed. Many times there are more than 75% - this is a very conservative estimation.

# St. Dev. % w/in k st. dev. of mean

Interpret using Chebyshev Test Mean = 80 St. Dev. = 5 1.What percent are between 75 and 85? 2.What percent are between 60 and 100?

Collect wrist measurements (in) Create distribution Find st. dev & mean. What percent is within 1 deviation of mean

Practice Problems 1.Using Chebyshev, solve the following problem for a distribution with a mean of 80 and a st. dev. Of 10. a. At least what percentage of values will fall between 60 and 100? b. At least what percentage of values will fall between 65 and 95?

Normal Distributions These are special density curves. They have the same overall shape  Symmetric  Single-Peaked  Bell-Shaped They are completely described by giving its mean (  ) and its standard deviation (  ). We abbreviate it N( ,  )

Normal Curves…. Changing the mean without changing the standard deviation simply moves the curve horizontally. The Standard deviation controls the spread of a Normal Curve.

Standard Deviation It’s the natural measure of spread for Normal distributions. It can be located by eye on a Normal curve.  It’s the point at which the curve changes from concave down to concave up.

Why is the Normal Curve Important? They are good descriptions for some real data such as  Test scores like SAT, IQ  Repeated careful measurements of the same quantity  Characteristics of biological populations (height) They are good approximations to the results of many kinds of chance outcomes They are used in many statistical inference procedures.

Empirical Rule Can only be used if the data can be reasonably described by a normal curve. Approximately  68% of the data is within 1 st. dev. of mean  95% of the data is within 2 st. dev. of mean  99.7% of data is within 3 st. dev. of mean

Empirical Rule What percent do you think……

Empirical Rule ( Rule) In the Normal distribution with mean (  ) and standard deviation (  ):  Within 1  of  ≈ 68% of the observations  Within 2  of  ≈ 95% of the observations  Within 3  of  ≈ 99.7% of the observations

The distribution of batting average (proportion of hits) for the 432 Major League Baseball players with at least 100 plate appearances in the 2009 season is normally distributed defined N(0.261, 0.034). Sketch a Normal density curve for this distribution of batting averages. Label the points that are 1, 2, and 3 standard deviations from the mean. What percent of the batting averages are above 0.329? What percent are between and.295?

Scores on the Wechsler adult Intelligence Scale (a standard IQ test) for the 20 to 34 age group are approximately Normally distributed. N(110, 25). What percent are between 85 and 135? What percent are below 185? What percent are below 60?

2.A sample of the hourly wages of employees who work in restaurants in a large city has a mean of $5.02 and a st. dev. of $0.09. a. Using Chebyshev’s, find the range in which at least 75% of the data will fall. b. Using the Empirical rule, find the range in which at least 68% of the data will fall.

The mean of a distribution is 50 and the standard deviation is 6. Using the empirical rule, find the percentage that will fall between 38 and 62.

A sample of the labor costs per hour to assemble a certain product has a mean of $2.60 and a standard deviation of $0.15, using Chebyshev’s, find the values in which at least 88.89% of the data will lie.

Measures of Position Percentiles Z-scores

0 min 30 min The following represents my results when playing an online sudoku game…at

Introduction A student gets a test back with a score of 78 on it. A 10 th -grader scores 46 on the PSAT Writing test Isolated numbers don’t always provide enough information…what we want to know is where we stand.

Where Do I Stand? Let’s make a dotplot of our heights from 58 to 78 inches. How many people in the class have heights less than you? What percent of the dents in the class have heights less than yours?  This is your percentile in the distribution of heights

Finishing…. Calculate the mean and standard deviation. Where does your height fall in relation to the mean: above or below? How many standard deviations above or below the mean is it?  This is the z-score for your height.

Let’s discuss What would happen to the class’s height distribution if you converted each data value from inches to centimeters. (2.54cm = 1 in) How would this change of units affect the measures of center, spread, and location (percentile & z-score) that you calculated.

National Center for Health Statistics Look at Clinical Growth Charts at

Percentiles Value such that r% of the observations in the data set fall at or below that value. If you are at the 75 th percentile, then 75% of the students had heights less than yours.

Test scores on last AP Test. Jenny made an 86. How did she perform relative to her classmates? Her score was greater than 21 of the 25 observations. Since 21 of the 25, or 84%, of the scores are below hers, Jenny is at the 84 th percentile in the class’s test score distribution

Find the percentiles for the following students…. Mary, who earned a 74. Two students who earned scores of

Cumulative Relative Frequency Table: Age of First 44 Presidents When They Were Inaugurated AgeFrequencyRelative frequency Cumulative frequency Cumulative relative frequency /44 = 4.5%22/44 = 4.5% /44 = 15.9%99/44 = 20.5% /44 = 29.5%2222/44 = 50.0% /44 = 34%3434/44 = 77.3% /44 = 15.9%4141/44 = 93.2% /44 = 6.8%4444/44 = 100%

Cumulative Relative Frequency Graph:

Interpreting… Why does it get very steep beginning at age 50? When does it slow down? Why? What percent were inaugurated before age 70? What’s the IQR? Obama was 47….

Describing Location in aDistribution Use the graph from page 88 to answer the following questions. Was Barack Obama, who wasinaugurated at age 47, unusuallyyoung? Estimate and interpret the 65 th percentile of the distribution Interpreting Cumulative Relative Frequency Graphs

What is the relationship between percentiles and quartiles?

Z-Score – (standardized score) It represents the number of deviations from the mean. If it’s positive, then it’s above the mean. If it’s negative, then it’s below the mean. It standardized measurements since it’s in terms of st. deviation.

Discovery: Mean = 90 St. dev = 10 Find z score for

Z-Score Formula

Compare…using z-score. History Test Mean = 92 St. Dev = 3 My Score = 95 Math Test Mean = 80 St. Dev = 5 My Score = 90

Compare Math: mean = 70 x = 62 s = 6 English: mean = 80 x = 72 s = 3

Be Careful! Being better is relative to the situation. What if I wanted to compare race times?

Find the following percentiles. X Rel. FreqC.F th percentile? 2.17 th percentile? 3.70 th percentile? 4.25 th percentile?

Homework Worksheet