SCI110 tutorials
Preface
Answers to questions
Statistical software
Data sets
Call-outs used in this book
Call-outs to
orient
your study
Call-outs to indicate
activities
Call-outs that give
support
How this book was made
Learning outcomes
How to cite this book
1
Teaching Week 1 tutorial
1.1
Quick revision
1.2
Class discussion
1.3
Research process
1.4
Forming RQs
1.5
Terminology
1.6
Terminology
1.7
Creating research questions and studies
1.8
Identifying units of analysis and units of observation
1.9
Optional questions
1.9.1
(Optional)
Identifying different types of studies
1.9.2
(Optional)
Terminology
2
Teaching Week 2 tutorial
2.1
Quick revision
2.2
Class discussion
2.3
Study design
2.4
Language
2.5
Assessing reports
2.6
Planning a research study
2.7
Study design features
3
Teaching Week 3 tutorial
3.1
Quick revision
3.2
Class discussion
3.3
Using the calculator statistics mode for a small data set
3.4
Understanding centre and variation
3.5
Understanding histograms
3.6
Collecting data
3.7
Interpreting graphs
3.8
Optional drills
: computational exercises
3.9
Optional questions
3.9.1
(Optional)
Using the calculator Statistics Mode
4
Teaching Week 4 tutorial
4.1
Quick revision
4.2
Class discussion
4.3
Percentages and odds
4.4
Two-way tables
4.5
Working with odds
4.6
Optional drills
: Computational exercises
4.7
Optional questions
4.7.1
(Optional)
Odds ratios
5
Teaching Week 5 tutorial
5.1
Quick revision
5.2
Class discussion
5.3
Critiquing students' numerical summaries
5.4
Critiquing students' graphs
5.5
Understanding correlations
5.6
Numerical summary for ruler-drop data
5.7
Which summary measures to use
6
Teaching Week 6 tutorial
6.1
Quick revision
6.2
Class discussion
6.3
Normal distributions and IQs
6.4
Normal distributions and heights
6.5
Probability
6.6
Random coin tosses
6.7
Optional questions
6.7.1
(Optional)
Working with normal distributions
7
Teaching Week 7 tutorial
7.1
Quick revision
7.2
Class discussion
7.3
Concepts
7.4
Standard error for a proportion
7.5
CIs for one proportion
7.6
CIs for one mean
7.7
Optional drills
: Computational exercises
8
Teaching Week 8 tutorial
8.1
Quick revision
8.2
Class discussion
8.3
The decision-making process
8.4
Test for one proportion
8.5
Tests for one mean
8.6
Approximate
\(P\)
-values
9
Teaching Week 9 tutorial
9.1
Quick revision
9.2
Class discussion
9.3
Matching RQs and hypotheses
9.4
CIs and tests for mean differences (paired data)
9.5
CI and test for the ruler-drop
9.6
CIs and test for difference between two sample means
9.7
Optional questions
9.7.1
(Optional)
Paired
\(t\)
-test
9.7.2
(Optional)
Two-sample
\(t\)
-test
10
Teaching Week 10 tutorial
10.1
Quick revision
10.2
Class discussion
10.3
Consistency
10.4
CI and test for odds ratios
10.5
CIs and tests for ORs
10.6
Identify the correct analysis
10.7
Sample size calculations
11
Teaching Week 11 tutorial
11.1
Quick revision
11.2
Class discussion
11.3
Critiquing past students' reports 1
11.4
Critiquing past students' reports 2
11.5
Tests for ORs
11.6
Critiquing past students' reports 3
11.7
Critiquing past students' reports 4
12
Teaching Week 12 tutorial
12.1
Quick revision
12.2
Class discussion
12.3
Understanding correlations and regression
12.4
Interpreting regressions
12.5
Regression and correlation
Appendices
A
Answers for exercises
A.1
Answers for TW 1 tutorial
A.2
Answers for TW 2 tutorial
A.3
Answers for TW 3 tutorial
A.4
Answers for TW 4 tutorial
A.5
Answers for TW 5 tutorial
A.6
Answers for TW 6 tutorial
A.7
Answers for TW 7 tutorial
A.8
Answers for TW 8 tutorial
A.9
Answers for TW 9 tutorial
A.10
Answers for TW 10 tutorial
A.11
Answers for TW 11 tutorial
A.12
Answers for TW 12 tutorial
B
Appendix: Tables
B.1
When the
\(z\)
-score is known, and the area is sought
B.2
When the area is known, and the
\(z\)
-score is sought
B.3
Random numbers
C
Symbols, formulas, statistics and parameters
C.1
Symbols and standard errors
C.2
Confidence intervals
C.3
Hypothesis testing
C.4
Sample size estimation
C.5
Other formulas
C.6
Other symbols and abbreviations used
D
Appendix: Image credits
References
Published with bookdown
Science Research Methods: Tutorials
6.2
Class discussion
Discuss
: Outliers should always be removed from a dataset before analysis.