Range, Width, min-max Values and Graphs

Slides:



Advertisements
Similar presentations
Unit 1.1 Investigating Data 1. Frequency and Histograms CCSS: S.ID.1 Represent data with plots on the real number line (dot plots, histograms, and box.
Advertisements

Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 2 Exploring Data with Graphs and Numerical Summaries Section 2.2 Graphical Summaries.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Lecture Slides Elementary Statistics Eleventh Edition and the Triola.
8.1 Types of Data Displays Remember to Silence Your Cell Phone and Put It In Your Bag!
Chapter 2 Graphical Distributions Distribution – a graphical display of data. After a survey or experiment is complete, different graphing methods are.
Histograms & Comparing Graphs
Section 2.2 Graphical Displays of Distributions.  Dot Plots  Histograms: uses bars to show quantity of cases within a range of values  Stem-and-leaf.
Chapter 2 Describing Data Sets
1. Statistics 2. Frequency Table 3. Graphical Representations  Bar Chart, Pie Chart, and Histogram 4. Median and Quartiles 5. Box Plots 6. Interquartile.
Histogram A frequency plot that shows the number of times a response or range of responses occurred in a data set.
Histogram A frequency plot that shows the number of times a response or range of responses occurred in a data set.
Frequency Distributions and Graphs
CHAPTER 2 Frequency Distributions and Graphs. 2-1Introduction 2-2Organizing Data 2-3Histograms, Frequency Polygons, and Ogives 2-4Other Types of Graphs.
Chapter 02 McGraw-Hill/Irwin
8.1 Graphing Data In this chapter, we will study techniques for graphing data. We will see the importance of visually displaying large sets of data so.
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 2 Describing Data: Frequency Tables, Frequency Distributions,
Chapter 2 Presenting Data in Tables and Charts. 2.1 Tables and Charts for Categorical Data Mutual Funds –Variables? Measurement scales? Four Techniques.
Variable  An item of data  Examples: –gender –test scores –weight  Value varies from one observation to another.
 Frequency Distribution is a statistical technique to explore the underlying patterns of raw data.  Preparing frequency distribution tables, we can.
Dr. Asawer A. Alwasiti.  Chapter one: Introduction  Chapter two: Frequency Distribution  Chapter Three: Measures of Central Tendency  Chapter Four:
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation Chapter 2 McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies,
Chapter 2 Graphs, Charts, and Tables - Describing Your Data ©
Graphing Data: Introduction to Basic Graphs Grade 8 M.Cacciotti.
Presentation Of Data. Data Presentation All business decisions are based on evaluation of some data All business decisions are based on evaluation of.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Section 2-2 Frequency Distributions.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 2 Descriptive Statistics: Tabular and Graphical Methods.
Chapter 11 Data Descriptions and Probability Distributions Section 1 Graphing Data.
Chapter 2 Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies,
© Copyright McGraw-Hill CHAPTER 2 Frequency Distributions and Graphs.
STATISTICS AND OPTIMIZATION Dr. Asawer A. Alwasiti.
Histograms, Frequency Polygons, and Ogives. What is a histogram?  A graphic representation of the frequency distribution of a continuous variable. Rectangles.
Histograms, Frequency Polygons, and Ogives
Chapter 2 Frequency Distributions and Graphs 1 Copyright © 2012 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
MATH 2311 Section 1.5. Graphs and Describing Distributions Lets start with an example: Height measurements for a group of people were taken. The results.
Stat 101Dr SaMeH1 Statistics (Stat 101) Associate Professor of Environmental Eng. Civil Engineering Department Engineering College Almajma’ah University.
Statistics Year 9. Note 1: Statistical Displays.
Statistical Fundamentals: Using Microsoft Excel for Univariate and Bivariate Analysis Alfred P. Rovai Charts Overview PowerPoint Prepared by Alfred P.
The Presentation of Scientific Data: Results, Tables and Graphs Mr. England – Science FHS.
Data Coaching Services Chart Interpretation 1. o Bar o Stacked Bar o Pie o Line o Scatter plot 2.
Central Tendency  Key Learnings: Statistics is a branch of mathematics that involves collecting, organizing, interpreting, and making predictions from.
Descriptive Statistics: Tabular and Graphical Methods
Exploratory Data Analysis
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation Chapter 2.
Chapter 12 Statistics 2012 Pearson Education, Inc.
Chapter 12 Statistics.
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation Chapter 2.
Graphical & Tabular Descriptive Techniques
Chapter 2: Methods for Describing Data Sets
Frequency Distributions and Graphs
Graphical Presentation of data
Chapter 2 McGraw-Hill/Irwin
ORGANIZING AND GRAPHING DATA
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation Chapter 2.
Classification and Tabulation of data
Chapter 2 McGraw-Hill/Irwin
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation Chapter 2.
Graphing.
Chapter 2 Presenting Data in Tables and Charts
Frequency Distributions and Graphs
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation Chapter 2.
Technical Writing (AEEE299)
Histograms & Comparing Graphs
Chapter-2: Measurements Dr. Chirie Sumanasekera
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation Chapter 2.
Constructing and Interpreting Visual Displays of Data
Chapter Nine: Using Statistics to Answer Questions
x Straight Line Graphs y = mx + c Pie Charts x Positive correlation
Range, Width, min-max Values and Graphs
ALGEBRA STATISTICS.
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation Chapter 2.
Presentation transcript:

Range, Width, min-max Values and Graphs Chapter 3 Range, Width, min-max Values and Graphs

Range: The Range is the difference between the lowest and highest values. Example: In {4, 6, 9, 3, 7} the lowest value is 3, and the highest is 9.

Class Width Class Width = (x - y) / n Where, x = Maximum Value y = Minimum Value n = Number of Classes Calculate class width for 3 data items having a maximum value of 9, minimum value of 5. Solution: Class Width = (x - y) / n = (9 - 5) / 3 = 1.333

Frequency In statistics the frequency (or absolute frequency) of an event is the number of times the event occurred in an experiment or study. These frequencies are often graphically represented in histograms.

Histogram A histogram is a chart that shows frequencies for equal width intervals of values of a metric variable. A histogram is a graphical representation that organizes a group of data points into user-specified ranges. It is similar in appearance to a bar graph. The histogram condenses a data series into an easily interpreted visual by taking many data points and grouping them into logical ranges.

Example of a Histogram:

Class Work: Let us assume that we have 20 workers and they are installing different number of (let us say cars) weekly. Each worker assemble different numbers. Let us find the min-max numbers, range, width of the distribution. The let us draw the the histogram of the distribution.

The minimum value is 20 cars and the maximum value is 40 cars Range=highest value – Lowest Value Range is 40-20=20 into interval scale. İn order to find the width we need to classify the observations

Number of Cars Frequency 20-25 4 26-30 5 31-35 2 36-40 9 Total 20

Width= Range/The Number of Classes

The Percentage and Cumulative Number of Cars Frequency Percentage Cumulative 20-25 4 20 26-30 5 25 45 31-35 2 10 55 36-40 9 100 Total -- Let us calculate The Weekly Average We need the middle value for each classes (20+25)/2=22,5 (26+30)/2=28 (31+35)/2=33 (36+40)/2=38

(22,5x0.2)+(28x0.25)+(33x0.10)+(38x0.45) = 31.9 = 32 Cars Number of Cars Mid Classes Frequency Percentage Cumulative 20-25 22,5 4 20 26-30 28 5 25 45 31-35 33 2 10 55 36-40 38 9 100 Total -- (22,5x0.2)+(28x0.25)+(33x0.10)+(38x0.45) = 31.9 = 32 Cars

In order the lengths are: 1,1,1,4,4,5,5,5,6,7,8,8,8,9,9,9,9,9,9,10,10,11,12,12,13,14,14,15,15,16,16,16,16,17,17,17,18,18 The smallest value (the "minimum") is 1 cm The largest value (the "maximum") is 18 cm The range is 18−1 = 17 cm Let us say we want about 5 groups. Divide the range by 5: 17/5 = 3.4 Then round that up to 4 Pick a starting value that is less than or equal to the smallest value. Try to make it a multiple of the group size if you can. In our case a start value of 0 makes the most sense

Starting at 0 and with a group size of 4 we get: 0, 4, 8, 12, 16 Write down the groups, include the end value of each group (must be less than the next group): The last group goes to 19 which is greater than the largest value. That is OK: the main thing is that it must include the largest value.

Line Graph With One Variable

Line Graphs With two Variables

Bar Graph

Bar chart (or graph): A bar chart is a graph which uses parallel rectangular shapes to represent changes in the size, value, or rate of something or to compare the amount of something relating to a number of different countries or groups.

Bar Graph With One Variable

Bar Graph With Two Variables

Pie Chart: A pie chart (or a circle chart) is a circular statistical graphic which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice (and consequently its central angle and area), is proportional to the quantity it represents.

Static Relation Between Two Variables (Scatter Graph)

Scatter plots (also called scatter graphs) are similar to line graphs. A line graph uses a line on an X-Y axis to plot a continuous function, while a scatter plot uses dots to represent individual pieces of data. In statistics, these plots are useful to see if two variables are related to each other. For example, a scatter chart can suggest a linear relationship (i.e. a straight line).

Suppose we have 15 consumers; their monthly income and consumptions are given in the table

Strong estimation Week estimation

EViews