1 Here are some additional methods for describing data.

Slides:



Advertisements
Similar presentations
X y Exploratory data analysis Cross tabulations and scatter diagrams.
Advertisements

1 Types of Data. 2 Data As we get started in this chapter say as a research project we want to learn more about faculty at WSC. Say we gather information.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Lecture Slides Elementary Statistics Eleventh Edition and the Triola.
1 1 Slide © 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
1 1 Slide © 2014 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
1 Normal Probability Distributions. 2 Review relative frequency histogram 1/10 2/10 4/10 2/10 1/10 Values of a variable, say test scores In.
Unit 0, Pre-Course Math Review Session 0.3, Graphing J. Jackson Barnette Professor of Biostatistics.
1 The Basics of Regression. 2 Remember back in your prior school daze some algebra? You might recall the equation for a line as being y = mx + b. Or maybe.
1 Describing Categorical Data Here we study ways of describing a variable that is categorical.
1 1 Slide © University of Minnesota-Duluth, Summer-2009 Econ-2030(Dr. Tadesse) Chapter-2: Descriptive Statistics: Tabular and Graphical Presentations Part.
1 Here are some additional methods for describing data.
1 The Normal Probability Distribution. 2 Review relative frequency histogram 1/10 2/10 4/10 2/10 1/10 Values of a variable, say test scores
The Basics of Regression continued
The Normal Distribution
X y Exploratory data analysis Cross tabulations and scatter diagrams.
1 Describing Categorical Data Here we study ways of describing a variable that is categorical or qualitative.
1 Describing Quantitative Data Here we study ways of describing a variable that is quantitative.
1 1 Slide IS 310 – Business Statistics IS 310 Business Statistics CSU Long Beach.
Covariance and Correlation
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 2 Descriptive Statistics: Tabular and Graphical Methods.
Examining Univariate Distributions Chapter 2 SHARON LAWNER WEINBERG SARAH KNAPP ABRAMOWITZ StatisticsSPSS An Integrative Approach SECOND EDITION Using.
Chapter 2 Presenting Data in Tables and Charts. Note: Sections 2.1 & examining data from 1 numerical variable. Section examining data from.
Descriptive Statistics  Summarizing, Simplifying  Useful for comprehending data, and thus making meaningful interpretations, particularly in medium to.
Section 1.1, Slide 1 Copyright © 2014, 2010, 2007 Pearson Education, Inc. Section 14.1, Slide 1 14 Descriptive Statistics What a Data Set Tells Us.
STATISTICAL GRAPHS.
What You Will Learn Frequency Distributions Histograms
1 1 Slide © 2006 Thomson/South-Western Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations Part B n Exploratory Data Analysis n Crosstabulations.
Variable  An item of data  Examples: –gender –test scores –weight  Value varies from one observation to another.
Warm Up Researchers deigned an observational study to investigate the accepted “normal” body temperature of 98.6F. In the study, 148 healthy men and women.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 2 Descriptive Statistics: Tabular and Graphical Methods.
 Frequency Distribution is a statistical technique to explore the underlying patterns of raw data.  Preparing frequency distribution tables, we can.
2.2 Organizing Quantitative Data. Data O Consider the following data O We would like to compute the frequencies and the relative frequencies.
BIA 2610 – Statistical Methods Chapter 2 – Descriptive Statistics: Tabular and Graphical Displays.
Descriptive Statistics Descriptive Statistics describe a set of data.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Section 2-2 Frequency Distributions.
Graphical and Tabular Descriptive Techniques Statistics for Management and Economics Chapter 2 Updated: 11/28/2015.
4.4 OUTLIERS AND DOT PLOTS. WHAT IS AN OUTLIER? Sometimes, distributions are characterized by extreme values that differ greatly from the other observations.
Stem and Leaf Plots.
1 Review Sections 2.1, 2.2, 1.3, 1.4, 1.5, 1.6 in text.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 4-2 Displaying Distributions with Graphs.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Chapter 3 Displaying Data. 2 Major Points Plotting data: Why bother? Plotting data: Why bother? Histograms Histograms Frequency polygon Frequency polygon.
Can help you compare data. A convenient method to display every piece of data by showing the digits of each number.
Chapter 2: Frequency Distributions. Frequency Distributions After collecting data, the first task for a researcher is to organize and simplify the data.
1 Frequency Distributions. 2 After collecting data, the first task for a researcher is to organize and simplify the data so that it is possible to get.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 2 Descriptive Statistics: Tabular and Graphical Methods.
Section 1.1, Slide 1 Copyright © 2014, 2010, 2007 Pearson Education, Inc. Section 14.1, Slide 1 14 Descriptive Statistics What a Data Set Tells Us.
Organizing and Visualizing Data © 2010 Pearson Education, Inc. All rights reserved.Section 15.1, Slide
MATH 110 Sec 14-1 Lecture: Statistics-Organizing and Visualizing Data STATISTICS The study of the collection, analysis, interpretation, presentation and.
ISTANBUL STOCK EXCHANGE (BIST) FELL 6 POINTS IN AVERAGE TODAY THE UNITED STATES DOLLAR (USD) APPRECIATED BY 4 PERCENT LAST WEEK AGAINST TURKISH LIRA (TRL).
2.2 Stem & Leaf Plots, Dot Plots and Shape. 2 Stem-and-Leaf Displays Stem-and-leaf displays contain more information than frequency distributions and.
Stem-and-Leaf Plots, Histograms, and Circle Graphs Objective: To graph and analyze data in many different ways.
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 2 Section 2 – Slide 1 of 37 Chapter 2 Section 2 Organizing Quantitative Data.
Slide Copyright © 2009 Pearson Education, Inc. Ch. 3.1 Definition A basic frequency table has two columns: One column lists all the categories of.
The Normal Approximation for Data. History The normal curve was discovered by Abraham de Moivre around Around 1870, the Belgian mathematician Adolph.
Statistics Review  Mode: the number that occurs most frequently in the data set (could have more than 1)  Median : the value when the data set is listed.
Describing Data Week 1 The W’s (Where do the Numbers come from?) Who: Who was measured? By Whom: Who did the measuring What: What was measured? Where:
M. MASTAK AL AMIN The summary Table A summary table indicates the frequency, amount or percentage of items in a set of categories so that you can see differences.
Descriptive Statistics: Tabular and Graphical Methods
Organizing Quantitative Data: The Popular Displays
Descriptive Statistics: Tabular and Graphical Methods
2.2 More Graphs and Displays
Statistics Vision 2.
Statistical Reasoning
Standard Deviation.
Drill {A, B, B, C, C, E, C, C, C, B, A, A, E, E, D, D, A, B, B, C}
Essentials of Statistics for Business and Economics (8e)
Fu Jen Catholic University
Presentation transcript:

1 Here are some additional methods for describing data

Here I have the number line. We start on the left at zero and as we move to the right we increase in value. (We can move to the left of zero into the negative numbers.) Now, if we want a number line vertically we tip it from the 100 side up, as my arrow shows. This is the normal convention in all of math. EXCEPT for the stem-and-leaf plot or display. In this one area we tip the line up from the zero side. As an example of some data say 50 people take a test that has up to 150 points. Possible values on the test are 70, 120, 132, 79 and so on.

3 With the 50 people who took the test, let’s imagine we sort the data from low score to high score. In other words, we might arrange the data so that the lowest score is first, and then the next highest and so on. The last score is the highest. When we have a 3-digit number, like with test score, we have in general xxy. I put xxy because we will call the xx part the stem and y the leaf. The number 69, for example would be stem 06, or 6, and leaf would be steam 12 and leaf 3. Say we had two people on the test with score 69 and 68. In sorted order we would have 68, 69. In stem-and-leaf form we would have stem 6 and leafs 8 and 9. On the next slide I will have the beginning of a stem-and-leaf display that is in the book on page 53.

Here I have stem and leaf for the scores in the 60’s and the 90’s. Not the frequency of scores in the 60’s was two (two leafs) and the frequency of scores in the 90’s was 12. The stem-and-leaf is like a histogram, but the leafs of each number are used in a stack to the side of the stem. Longer stacks represent more frequent groups.

5 Crosstabulations, or crosstabs for short. Sometimes in statistics we will want to work with two variables at a time. The reason is that we think the two variables are somehow related. As an example, what do you think is the relationship between student grades and the number of classes they skip in a semester. My hunch would be the more classes skipped the lower the grade. But we may want to research this idea. Crosstabs is a tabular summary of two variables. On the next screen I create a basic crosstabs based on an example of how an “expert” rates a restaurant quality and the price of meals in the restaurant. 300 restaurants were visited. Notice the expert had to rate each restaurant as being good, very good, or excellent. Plus the expert had to note the meal price.

6 Here I have a frequency crosstab. Note that 42 restaurants were rated good and had prices in the $10-19 range. The “total” row is really the frequency distribution for the variable Meal Price, and the “total” column is the frequency distribution for the variable quality. In this table, we could put percentages in several formats.

7 Percentages in the cells We call each part of the table a cell and depending on what we want to think about we can calculate different percentages. Column percentages When we look at a given column we have a meal price and each row is a quality rating. If we use a column total as the basis of a percentage then we see percents of quality rating at each price range. Row percentages When we look at a given row we have a quality rating and each column is a price range. If we use the row total as the basis for a percentage then we see the percents of price range for a given quality.

8 Overall percentages Since we had 300 in the data, each cell divided by 300 tells us the percent of times that cell came up. Scatter Diagram Say we talk to people and we ask them their years of schooling and income last year. For each person we would have two values. On the next screen I show a scatter plot, where each person is a dot in the graph.

9 Scatterplot y - income x - years of schooling In a graph we put the variable of interest on the y axis. Here it is thought that knowing the years of schooling for a person will better help us understand income and schooling is put on the x axis. In other words, certain values of income are ‘matched’ with schooling amounts. note here in the scatterplot that in general the higher the schooling, the higher the income. Thus, knowing schooling will permit better prediction of income.

10 In a scatterplot, when the dots seem to be going up hill we say there is a positive, or direct, relationship between the variables. This means that the higher the value on one variable, the higher the value of the other variable. Dots going down hill suggest a negative, or indirect, relationship. This means as the value of one variable is getter higher the value on the other is getting lower. x y x y x y Positive relationship Negative relationship No relationship?