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3.9 Bivariate Measurement Data
1. Home
2. Standard
3. Contents
01. Overview
Interesting Correlations
02. Concepts
a. Univariate vs Bivariate
b. The Scatter Plot
c. Estimating strength
d. Obtaining a model
e. Interpreting the gradient
f. Fitting and Justifying a Model
g. Making a prediction
h. PPDAC cycle
03. The Process
a. Introduction
Problem
Plan - Purpose Statement
b. Data
i. Identifying the variables
ii. Naming the Source
iii. Eyeballing - Trend
iv. Eyeballing - Association
v. Eyeballing - Strength
vi. Groups
vii. Unusual Features
c. Analysis
i. Regression Line
ii. Correlation coefficient
iii. Predictions
iv. Causality
d. Conclusion
e. Summary
A possible approach
4. Report Writing
a. A3 Report Template
b. A4 Report Template
c. Report Template (Web)
5. Assessment
Marking Schedule
Exemplars
Sports Science Exemplars
Achieve Exemplar
Merit Exemplar
Excellence Exemplar
NZQA Exemplars
6. Vocabulary
7. Data Sets
Crash Testing
Food and Diets
8. Workbooks
Workbook Answers
9. Teacher Notes
Workbook
1. Introduction
Web links
Prezi links
06. Report Writing
200. Archived Pages
v. Eyeballing - Strength
02. Report Introductions
03. Data Analysis
04. Scatterplots
05. Using iNZight
06. Correlation Coefficient
07. Groups and Outliers
08. Predictions
09. Causality
10. Non-linear Models
11. The Conclusion
12. Report Writing
21. Introduction
22. Regression and Correlation
23. Making Predictions
24. Swapping axes
25. Predictor and Response Variable
26. Clusters and Outliers
27. Case Study 1
28. Case Study 2
29. Analysing Residuals
30. Causation
a. Overview
c. Univariate vs Bivariate
Exemplars
1. Sports Science Exemplars
1. Low Achieved
2. High Achieved
3. Low Merit
4. High Merit
5. Low Excellence
2. Dolphins Exemplars
1. High Not Achieved
2. Low Achieved
3. High Achieved
4. Low Merit
5. High Merit
6. Low Excellence
7. Report Comparisons
3. Achieved Exemplar
Introduction 2
Introduction 3
Copy of Plan - Purpose Statement
Line of Best Fit
4. Assignments
World Population
Copy of Success Criteria
Writing a good question
Copy of c. Measuring strength
3.9 Bivariate Measurement Data
1. Home
2. Standard
3. Contents
01. Overview
Interesting Correlations
02. Concepts
a. Univariate vs Bivariate
b. The Scatter Plot
c. Estimating strength
d. Obtaining a model
e. Interpreting the gradient
f. Fitting and Justifying a Model
g. Making a prediction
h. PPDAC cycle
03. The Process
a. Introduction
Problem
Plan - Purpose Statement
b. Data
i. Identifying the variables
ii. Naming the Source
iii. Eyeballing - Trend
iv. Eyeballing - Association
v. Eyeballing - Strength
vi. Groups
vii. Unusual Features
c. Analysis
i. Regression Line
ii. Correlation coefficient
iii. Predictions
iv. Causality
d. Conclusion
e. Summary
A possible approach
4. Report Writing
a. A3 Report Template
b. A4 Report Template
c. Report Template (Web)
5. Assessment
Marking Schedule
Exemplars
Sports Science Exemplars
Achieve Exemplar
Merit Exemplar
Excellence Exemplar
NZQA Exemplars
6. Vocabulary
7. Data Sets
Crash Testing
Food and Diets
8. Workbooks
Workbook Answers
9. Teacher Notes
Workbook
1. Introduction
Web links
Prezi links
06. Report Writing
200. Archived Pages
v. Eyeballing - Strength
02. Report Introductions
03. Data Analysis
04. Scatterplots
05. Using iNZight
06. Correlation Coefficient
07. Groups and Outliers
08. Predictions
09. Causality
10. Non-linear Models
11. The Conclusion
12. Report Writing
21. Introduction
22. Regression and Correlation
23. Making Predictions
24. Swapping axes
25. Predictor and Response Variable
26. Clusters and Outliers
27. Case Study 1
28. Case Study 2
29. Analysing Residuals
30. Causation
a. Overview
c. Univariate vs Bivariate
Exemplars
1. Sports Science Exemplars
1. Low Achieved
2. High Achieved
3. Low Merit
4. High Merit
5. Low Excellence
2. Dolphins Exemplars
1. High Not Achieved
2. Low Achieved
3. High Achieved
4. Low Merit
5. High Merit
6. Low Excellence
7. Report Comparisons
3. Achieved Exemplar
Introduction 2
Introduction 3
Copy of Plan - Purpose Statement
Line of Best Fit
4. Assignments
World Population
Copy of Success Criteria
Writing a good question
Copy of c. Measuring strength
More
1. Home
2. Standard
3. Contents
01. Overview
Interesting Correlations
02. Concepts
a. Univariate vs Bivariate
b. The Scatter Plot
c. Estimating strength
d. Obtaining a model
e. Interpreting the gradient
f. Fitting and Justifying a Model
g. Making a prediction
h. PPDAC cycle
03. The Process
a. Introduction
Problem
Plan - Purpose Statement
b. Data
i. Identifying the variables
ii. Naming the Source
iii. Eyeballing - Trend
iv. Eyeballing - Association
v. Eyeballing - Strength
vi. Groups
vii. Unusual Features
c. Analysis
i. Regression Line
ii. Correlation coefficient
iii. Predictions
iv. Causality
d. Conclusion
e. Summary
A possible approach
4. Report Writing
a. A3 Report Template
b. A4 Report Template
c. Report Template (Web)
5. Assessment
Marking Schedule
Exemplars
Sports Science Exemplars
Achieve Exemplar
Merit Exemplar
Excellence Exemplar
NZQA Exemplars
6. Vocabulary
7. Data Sets
Crash Testing
Food and Diets
8. Workbooks
Workbook Answers
9. Teacher Notes
Workbook
1. Introduction
Web links
Prezi links
06. Report Writing
200. Archived Pages
v. Eyeballing - Strength
02. Report Introductions
03. Data Analysis
04. Scatterplots
05. Using iNZight
06. Correlation Coefficient
07. Groups and Outliers
08. Predictions
09. Causality
10. Non-linear Models
11. The Conclusion
12. Report Writing
21. Introduction
22. Regression and Correlation
23. Making Predictions
24. Swapping axes
25. Predictor and Response Variable
26. Clusters and Outliers
27. Case Study 1
28. Case Study 2
29. Analysing Residuals
30. Causation
a. Overview
c. Univariate vs Bivariate
Exemplars
1. Sports Science Exemplars
1. Low Achieved
2. High Achieved
3. Low Merit
4. High Merit
5. Low Excellence
2. Dolphins Exemplars
1. High Not Achieved
2. Low Achieved
3. High Achieved
4. Low Merit
5. High Merit
6. Low Excellence
7. Report Comparisons
3. Achieved Exemplar
Introduction 2
Introduction 3
Copy of Plan - Purpose Statement
Line of Best Fit
4. Assignments
World Population
Copy of Success Criteria
Writing a good question
Copy of c. Measuring strength
03. Data Analysis
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