V ARIABLES CAN BE : Q UALITATIVE ( DESCRIBED WITH WORDS OR CATEGORIES I. E. QUALITIES ) N OMINAL ( CAN BE NAMED AND COUNTED, BUT CANNOT BE PUT INTO.

Slides:



Advertisements
Similar presentations
Graphs, Good and Bad.
Advertisements

Interpreting Data for use in Charts and Graphs.
Section 6.1: Scatterplots and Correlation (Day 1).
TYPES OF DATA. Qualitative vs. Quantitative Data A qualitative variable is one in which the “true” or naturally occurring levels or categories taken by.
Introduction to Statistics and Research
PY 427 Statistics 1Fall 2006 Kin Ching Kong, Ph.D Lecture 1 Chicago School of Professional Psychology.
Social Research Methods
SCATTER PLOTS AND LINES OF BEST FIT
1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Summarizing Bivariate Data Terms, Scatterplots and Correlation.
1 Lesson Shapes of Scatterplots. 2 Lesson Shapes of Scatterplots California Standard: Statistics, Data Analysis, and Probability 1.2 Represent.
Correlation Question 1 This question asks you to use the Pearson correlation coefficient to measure the association between [educ4] and [empstat]. However,
3 - 1 Module 2: Types of Data This module describes the types of data typically encountered in public health applications. Recognizing and understanding.
Unit 1 Section 1.2.
Scatter Plots and Trend Lines
Linear Regression and Correlation
LIS 570 Summarising and presenting data - Univariate analysis continued Bivariate analysis.
Chapter 1 Descriptive Analysis. Statistics – Making sense out of data. Gives verifiable evidence to support the answer to a question. 4 Major Parts 1.Collecting.
3. Data Presentation Graphs & Charts.
Chapter 3 Describing Bivariate Data General Objectives: Sometimes the data that are collected consist of observations for two variables on the same experimental.
Quantitative Data Essential Statistics. Quantitative Data O Review O Quantitative data is any data that produces a measurement or amount of something.
Prior Knowledge Linear and non linear relationships x and y coordinates Linear graphs are straight line graphs Non-linear graphs do not have a straight.
C OLLECTING D ATA I: Q UANTITATIVE METHODS John Perry.
Final Jeopardy.
2 Variable: any characteristic, number, or attribute that can be measured or counted Numeric variables have values that describe a measurable quantity.
Inquiry Unit.
Graphs, Tables and Variables By the end of the lesson you should be able to: 1)Draw a results table 2)Decide whether a line graph or bar chart is most.
8 th Grade Math Common Core Standards. The Number System 8.NS Know that there are numbers that are not rational, and approximate them by rational numbers.
LECTURE 5 Correlation.
Section 1.1 Statistics Statistics :
Example 1: page 161 #5 Example 2: page 160 #1 Explanatory Variable - Response Variable - independent variable dependent variable.
 Graph of a set of data points  Used to evaluate the correlation between two variables.
Interpreting Data for use in Charts and Graphs. V
Scatterplots are used to investigate and describe the relationship between two numerical variables When constructing a scatterplot it is conventional to.
Interpreting Data for use in Charts and Graphs. V
Unit 1: Representing Data & Analysing 2D Data 1.1 Visual Displays of Data.
1 Copyright © Cengage Learning. All rights reserved. 3 Descriptive Analysis and Presentation of Bivariate Data.
MID-TERM REVIEW NOTES DO NOT LOSE THESE!! WE WILL ADD TO THESE DAILY.
Handout week 1 course Renske Doorenspleet 1 Chapter 1 -A. The role of statistics in the research process -B. Statistical applications -C. Types of variables.
Unit 1 Section : Variables and Types of Data  Variables can be classified in two ways:  Qualitative Variable – variables that can be placed.
Correlation & Regression Correlation does not specify which variable is the IV & which is the DV.  Simply states that two variables are correlated. Hr:There.
Testing hypotheses Continuous variables. H H H H H L H L L L L L H H L H L H H L High Murder Low Murder Low Income 31 High Income 24 High Murder Low Murder.
© 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
Correlation iNZight gives r. POSITIVE linear correlation r=1 "Perfect" 0.9
© 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
Comparison by Division of Two Quantities A proportional comparison in which one quantity can be described as a ratio of the other.
2.6 Scatter Diagrams. Scatter Diagrams A relation is a correspondence between two sets of data X is the independent variable Y is the dependent variable.
1 ES9 Chapters 4 ~ Scatterplots & Correlation. 2 ES9 Chapter Goals To be able to present bivariate data in tabular and graphic form To gain an understanding.
Copyright © 2014 by Nelson Education Limited Chapter 11 Introduction to Bivariate Association and Measures of Association for Variables Measured.
1.  The practice or science of collecting and analyzing numerical data in large quantities, especially for the purpose of inferring* proportions in a.
6.7 Scatter Plots. 6.7 – Scatter Plots Goals / “I can…”  Write an equation for a trend line and use it to make predictions  Write the equation for a.
Scatter plots. Like a line graph Has x and y axis Plot individual points.
Bivariate Data – Scatter Plots and Correlation Coefficient……
1.How much longer does it take for object B to travel 40 yards than it takes for object A? 2.How much further has object A traveled in 10 seconds than.
Correlation Definition: Correlation - a mutual relationship or connection between two or more things. (google.com) When two set of data appear to be connected.
CCSS.Math.Content.8.SP.A.1 Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities.
Bivariate Association. Introduction This chapter is about measures of association This chapter is about measures of association These are designed to.
Graphs... Can describe real situations. Show relationships between two variables.
Happy Tuesday Scientists!
Descriptive Statistics: Tabular and Graphical Methods
Unit 1 Section 1.2.
Warm Up Scatter Plot Activity.
Bell ringer 9/12 Label each graph accordingly using the word bank below. speeding up constant speed slowing down no motion.
Introduction to Statistics and Research
Do Now Can you Reason abstractly?
Collecting & Displaying Data
Statistics Chapter 1 Sections
Motion and Force. Motion and Force Chapter Twelve: Distance, Time, and Speed 12.1 Distance, Direction, and Position 12.2 Speed 12.3 Graphs of Motion.
IV. Graphing Types of graphs Graphing
Descriptive Analysis and Presentation of Bivariate Data
GRAPHING PRACTICE.
Presentation transcript:

V ARIABLES CAN BE : Q UALITATIVE ( DESCRIBED WITH WORDS OR CATEGORIES I. E. QUALITIES ) N OMINAL ( CAN BE NAMED AND COUNTED, BUT CANNOT BE PUT INTO ORDER ) O RDINAL ( CAN BE NAMED AND ALSO PUT INTO ORDER ) Q UANTITATIVE ( NUMERIC, OR IS MEASURED USING NUMBERS I. E. QUANTITIES ) D ISCRETE ( NUMBERS THAT ARE DISTINCT, WHOLE NUMBERS ) C ONTINUOUS ( NUMBERS THAT CAN BE MEASURED AS DECIMALS OR FRACTIONS ON A CONTINUOUS SCALE, NOT JUST WHOLE NUMBERS ) Variables Qualitative Nominal Ordinal Quantitative Discrete Continuous

A S WE NOW KNOW, B IVARIATE DATA CONSISTS OF TWO VARIABLES. O NE VARIABLE IS GENERALLY THE DEPENDENT VARIABLE, AND THE OTHER THE INDEPENDENT VARIABLE. T HE DEPENDENT VARIABLE DEPENDS ON THE OTHER VARIABLE. T HE INDEPENDENT VARIABLE DOES NOT DEPEND ON THE OTHER VARIABLE. W HEN DATA ARE EXPRESSED IN THE FORM OF A TABLE, GENERALLY THE INDEPENDENT VARIABLE IS WRITTEN IN THE FIRST ROW OR THE FIRST COLUMN. W HEN WE GRAPH BIVARIATE DATA, THE INDEPENDENT VARIABLE IS PLACED ON THE X - AXIS AND THE DEPENDENT VARIABLE ON THE Y - AXIS. The antenna is the INDEPENDENT variable. They can change or vary without relying on the tv The tv picture is the DEPENDENT variable. The picture depends on the antenna In this diagram, which is the IV and which is the DV out of the tv screen and the antenna?

A GE AND HEIGHT OF A CHILD A GE IS INDEPENDENT, HEIGHT IS DEPENDENT ( WHEN JUST LOOKING AT THESE TWO VARIABLE, HEIGHT DEPENDS ON AGE ) C OST OF BUS FARE AND DISTANCE TRAVELLED IN THE BUS C OST IS DEPENDENT, DISTANCE IS INDEPENDENT ( COST DEPENDS ON DISTANCE COVERED ) N UMBER OF PEOPLE AT A FOOTBALL MATCH AND THE NUMBER OF DRINKS SOLD THERE N UMBER OF PEOPLE IS INDEPENDENT, NUMBER OF DRINKS SOLD IS DEPENDENT ( AS IT DEPENDS ON NUMBERS OF PEOPLE THERE )

T HE OPTIONS WE HAVE WHEN CONSIDERING TWO VARIABLES ARE AS FOLLOWS : T HERE IS A RELATIONSHIP BETWEEN THEM ( THERE SEEMS TO BE A PATTERN BETWEEN THEM ) T HERE IS NO RELATIONSHIP BETWEEN THEM ( THEY DO NOT IMPACT ON OR RELATE TO EACH OTHER AT ALL, THE DATA SEEMS RANDOM ) I F THERE IS A RELATIONSHIP, IT CAN BE CLASSIFIED AS BEING STRONG, MODERATE OR WEAK. S TRONG – THEY RELATE CLOSELY TO ONE ANOTHER W EAK – THERE IS A SMALL RELATIONSHIP BETWEEN THEM T HE RELATIONSHIP CAN ALSO BE REGARDED AS POSITIVE OR NEGATIVE. I F ONE VARIABLE INCREASES AS THE OTHER INCREASES, THE RELATIONSHIP IS POSITIVE. I F ONE VARIABLE INCREASES AS THE OTHER DECREASES, THE RELATIONSHIP IS NEGATIVE. T HE RELATIONSHIP BETWEEN VARIABLES IS ALSO KNOWN AS CORRELATION

A PERSON ’ S HEIGHT ( H ) AND WEIGHT ( W ) Y ES, THERE IS A RELATIONSHIP I T IS A POSITIVE RELATIONSHIP ( GENERALLY AS HEIGHT INCREASES, SO DOES WEIGHT ) I T IS A FAIRLY STRONG RELATIONSHIP, BUT NOT PERFECT T HE LENGTH OF A SONG ( L ) AND ITS POSITION ON A CD ( P ) N O RELATIONSHIP – LENGTH OF SONG HAS NOTHING TO DO WITH WHAT NUMBER SONG IT IS ON A CD T HE SPEED OF TRAVEL ( S ) AND THE TIME TAKEN TO REACH A DESTINATION ( T ). Y ES, THERE IS A RELATIONSHIP I S IT A NEGATIVE RELATIONSHIP ( AS SPEED INCREASES, TIME TAKEN DECREASES ) I T IS A STRONG RELATIONSHIP