Defining the Problem More details….

Slides:



Advertisements
Similar presentations
Bi-Variate Data PPDAC. Types of data We are looking for a set of data that is affected by the other data sets in our spreadsheet. This variable is called.
Advertisements

Slide 1 Investigating Bivariate Measurement Data using iNZight Statistics Teachers’ Day 22 November 2012 Ross Parsonage.
AS Achievement Standard.
Chapter 4 Describing the Relation Between Two Variables
1 BA 275 Quantitative Business Methods Simple Linear Regression Introduction Case Study: Housing Prices Agenda.
FACTOR THE FOLLOWING: Opener. 2-5 Scatter Plots and Lines of Regression 1. Bivariate Data – data with two variables 2. Scatter Plot – graph of bivariate.
Bivariate Data Notes for Merit Comparing two Bi-variate plots.
Correlation & Regression
Linear Regression Modeling with Data. The BIG Question Did you prepare for today? If you did, mark yes and estimate the amount of time you spent preparing.
Descriptive Methods in Regression and Correlation
An Introduction to Research Methodology
Correlation and Regression
Chapter 14 – Correlation and Simple Regression Math 22 Introductory Statistics.
2.4: Cautions about Regression and Correlation. Cautions: Regression & Correlation Correlation measures only linear association. Extrapolation often produces.
Lesson 7 Aim: How can we represent bivariate data graphically?
Safety  L3follow safety instructions for your practical[ ]  L4 Identify possible hazards and state if they are high or low risk.[ ]  L5 Explain why.
Notes Bivariate Data Chapters Bivariate Data Explores relationships between two quantitative variables.
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Notes Bivariate Data Chapters Bivariate Data Explores relationships between two quantitative variables.
Topic 10 - Linear Regression Least squares principle - pages 301 – – 309 Hypothesis tests/confidence intervals/prediction intervals for regression.
Section 4.1 Scatter Diagrams and Correlation. Definitions The Response Variable is the variable whose value can be explained by the value of the explanatory.
Chapter 4 Describing the Relation Between Two Variables 4.1 Scatter Diagrams; Correlation.
Regression Regression relationship = trend + scatter
Dr. Engr. Sami ur Rahman Data Analysis Correlational Research.
Lecture 02.
Recall What was Required in the Rationale Assignment? 1.What is the phenomenon? 2.How is it different & similar to another phenomenon? 3.When/where/how.
Creating a Residual Plot and Investigating the Correlation Coefficient.
3.3 Correlation: The Strength of a Linear Trend Estimating the Correlation Measure strength of a linear trend using: r (between -1 to 1) Positive, Negative.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Chapter 10 Correlation and Regression 10-2 Correlation 10-3 Regression.
Lesson 4.5 – Conducting a Survey to Collect Two-Variable Data.
Response Variable: measures the outcome of a study (aka Dependent Variable) Explanatory Variable: helps explain or influences the change in the response.
REGRESSION MODELS OF BEST FIT Assess the fit of a function model for bivariate (2 variables) data by plotting and analyzing residuals.
Correlation & Linear Regression Using a TI-Nspire.
Investigating Bivariate Measurement Data using iNZight.
LOOKING FOR PATTERNS BETWEEN VARIABLES WHY DO SCIENTISTS COLLECT DATA, GRAPH IT AND WRITE EQUATIONS TO EXPRESS RELATIONSHIPS BETWEEN VARIABLES ?
Chapter 12: Correlation and Linear Regression 1.
Trail Mix Investigation
Predictions 3.9 Bivariate Data.
Is there a relationship between the lengths of body parts?
Sections Review.
REGRESSION (R2).
MATH1005 STATISTICS Tutorial 3: Bivariate Data.
Statistics: Chapter 1.
Aim: How to plot or graph data
Sociological Research Methods
Lecture 02.
Bivariate Data.
Scatterplots, Association, and Correlation
AGENDA: Quiz # minutes Begin notes Section 3.1.
Your Research Question
1.11 Bivariate Data Credits 3 As91036.
Day 49 Causation and Correlation
11A Correlation, 11B Measuring Correlation
Variables Qualitative (categorical, nominal, discrete)
Investigation Report write-up
Investigation Report write-up
Do Now How do scientists communicate their findings?
This will be the score for your POGIL
Bivariate Data credits.
Descriptive Statistics Univariate Data
1.1 – Social Science Research Methods
Statistics – Bivariate
Section 11.1 Correlation.
Chapter 3 Examining Relationships
Statistics 101 CORRELATION Section 3.2.
Aim: How to plot or graph data
Bivariate Data Response Variable: measures the outcome of a study (aka Dependent Variable) Explanatory Variable: helps explain or influences the change.
Chapter 3: Describing Relationships
Evaluating Experiments
Presentation transcript:

Defining the Problem More details…

Define your population! Writing an Introduction Include:  Where is the data from? How reliable is the source?  Is it representative? Why have you chosen your variables? Define your population! Pose your Investigative Question Variables must be clearly defined with units State which variable is considered explanatory and which is dependent (if applicable)

IMPORTANT: Give evidence of having completed Your Own RESEARCH Provide background context with evidence of research Do you think there will be an association and why?  Is the question of interest and value? Could the results be reasonably extended to the population? What are the findings of other related research? Include References (refer to them specifically) 10 min spent researching on wikipedia is not enough!!

Variable types: Discrete (Qualitative), Continuous (Quantitative) Bivariate investigations use Quantitative data (measured) The variable to be predicted (response or dependent variable) is plotted on the 'y' axis. The explanatory variable (used to make the prediction from) is on the 'x' axis. It would be sensible to have one response variable and compare the effect of different explanatory variables. (scope for discussion ) Sometimes there will be no 'causation' between the variables so the two variables are associated, and can go on either axis. Ensure that if there is a possible causation between the variables then the independent (explanatory) variable is 'x' and the dependent variable is 'y' EXPLAIN of why you chose your variables and reasons for deciding if explanatory, dependent or associated.

Types of Research Question (CORRELATION style question) I wonder what the relationship between (variable 1) and (variable 2) is in (population)? [ Is this relationship different between (groups)?] OR What sort of relationship is there between … I wonder what the relationship between (variable 1) and (variable 2) is in (population)? [How might this relationship compare between (variable 1) and (variable 3)?] (REGRESSION style questions) Can (variable 1) be used as a good predictor for (variable 2)? [Or would (variable 3) be a better predictor for (variable 2)?]

The aim of your investigation should include the word 'relationship'but NOT imply that a relationship exists    NO:   “what is the relationship between…”    YES: “I wonder if there is a relationship between…” “what is the nature of the relationship between…”

Extra Note (The investigative question needs to be an appropriate one it is expected that a purpose for the investigation is evident) If you choose a pointless question you will be limited to achieved level (maximum)