FoSSA: Fundamentals of Statistical Software & Analysis

Course Information
Meet the Teaching Team 
Course Dataset 1

Course Dataset 2

MODULE A1: INTRODUCTION TO STATISTICS USING R, STATA, AND SPSSA1.1 What is Statistics?

A1.2.1a Introduction to Stata

A1.2.2b: Introduction to R

A1.2.2c: Introduction to SPSS

A1.3: Descriptive Statistics

A1.4: Estimates and Confidence Intervals

A1.5: Hypothesis Testing

A1.6: Transforming Variables

End of Module A11 Quiz

MODULE A2: POWER & SAMPLE SIZE CALCULATIONSA2.1 Key Concepts

A2.2 Power calculations for a difference in means

A2.3 Power Calculations for a difference in proportions

A2.4 Sample Size Calculation for RCTs

A2.5 Sample size calculations for crosssectional studies (or surveys)

A2.6 Sample size calculations for casecontrol studies

End of Module A21 Quiz

MODULE B1: LINEAR REGRESSIONB1.1 Correlation and Scatterplots

B1.2 Differences Between Means (ANOVA 1)

B1.3 Univariable Linear Regression

B1.4 Multivariable Linear Regression

B1.5 Model Selection and FTests

B1.6 Regression Diagnostics

End of Module B11 Quiz

MODULE B2: MULTIPLE COMPARISONS & REPEATED MEASURESB2.1 ANOVA Revisited – PostHoc Testing

B2.2 Correcting For Multiple Comparisons

B2.3 Twoway ANOVA

B2.4 Repeated Measures and the Paired TTest

B2.5 Repeated Measures ANOVA

End of Module B21 Quiz

MODULE B3: NONPARAMETRIC MEASURESB3.1 The Parametric Assumptions

B3.2 MannWhitney U Test

B3.3 KruskalWallis Test

B3.4 Wilcoxon Signed Rank Test

B3.5 Friedman Test

B3.6 Spearman’s Rank Order Correlation

End of Module B31 Quiz

MODULE C1: BINARY OUTCOME DATA & LOGISTIC REGRESSIONC1.1 Introduction to Prevalence, Risk, Odds and Rates

C1.2 The ChiSquare Test and the Test For Trend

C1.3 Univariable Logistic Regression

C1.4 Multivariable Logistic Regression

End of Module C11 Quiz

MODULE C2: SURVIVAL DATAC2.1 Introduction to Survival Data

C2.2 KaplanMeier Survival Function & the Log Rank Test

C2.3 Cox Proportional Hazards Regression

C2.4 Poisson Regression

End of Module C21 Quiz
Participants 252
The quiz below is designed to test your knowledge of the material covered in the module. Best of luck!
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Question 1 of 10
1. Question
The correlation coefficient (r):
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Question 2 of 10
2. Question
In the context of simple linear regression, consider the following scenario: The relationship between height (cm) and weight (kg) was studied in 100 women aged 3540 years. The following regression equation was obtained:
Weight (kg) = 72.05 + 0.82 x height (cm)
What does this equation imply?
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Question 3 of 10
3. Question
[In reference to the previous Simple Linear Regression question]
If a woman is 160 cm tall, what weight would the model predict?
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Question 4 of 10
4. Question
A fictitious study was conducted among 500 male weekly drinkers to investigate the relationship between systolic blood pressure (SBP) and alcohol consumption level. SBP was measured in mmHg and recorded by variable called sbp_mean. Based on their selfreported alcohol consumption, participants were categorised into 4 alcohol categories (wkcat) (wkcat1: 1140 g/week; wkcat2: 140 – 279 g/week; wkcat3: 280 – 419 g/week; wkcat4: 420 + g/week).
The association between SBP and alcohol consumption was investigated using ANOVA. Based on the following ANOVA output, what can you conclude?
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Question 5 of 10
5. Question
[In reference to the Linear Regression & ANOVA question]
A simple linear regression model was used to investigate the relationship between SBP and alcohol consumption using the same dataset. What conclusion(s) can be drawn from the output shown?
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Question 6 of 10
6. Question
Based on data from 212 study volunteers aged 1327 years, it has been estimated that peak nasal inspiratory flow can be estimated by the following regression equation:
Peak nasal inspiratory flow (l/min) = 1.4256 x height (cm) + 33.0215 x gender (where 0=female and 1=male) + 1.4117 x age (years)  136.6778
The intercept of the multiple regression model provides an estimate of:
CorrectIncorrect 
Question 7 of 10
7. Question
Referring to the same regression model:
Peak nasal inspiratory flow (l/min) = 1.4256 x height (cm) + 33.0215 x gender (where 0=female and 1=male) + 1.4117 x age (years)  136.6778
If gender were to be recategorized as 1=female and 0=male:
CorrectIncorrect 
Question 8 of 10
8. Question
Referring once more to the same regression model:
Peak nasal inspiratory flow (l/min) = 1.4256 x height (cm) + 33.0215 x gender (where 0=female and 1=male) + 1.4117 x age (years)  136.6778
Which of the following is the correct interpretation of the model regression coefficients?
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Question 9 of 10
9. Question
Which of the following is not a required model assumption for linear regression?
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Question 10 of 10
10. Question
Regarding linear regression, if the assumption of homogeneity in variance (i.e. homoscedasticity) of the residuals is satisfied, in a plot of the residuals against the fitted (predicted) values we should see:
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