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 311
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
Based on the below twobytwo table, choose all that are correct.
Exposed Nonexposed Diseased 59 42 Nondiseased 121 358 CorrectIncorrect 
Question 2 of 10
2. Question
A cohort study on smoking and dementia reported a relative risk of dementia = 1.03 (95% CI 0.751.41) for smokers compared with nonsmokers. How would you interpret this?
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Question 3 of 10
3. Question
In a dataset of 3000 male current drinkers, a chisquare test for association was performed between current smoking (1=yes vs. 0=no) and alcohol intake (recorded by a categorical variable wkcat; 1: 1139 g/week, 2: 140279 g/week, 3: 280419 g/week, 4: 420+ g/week). The following output was obtained. What can you conclude based on the chisquare test result?
CorrectIncorrect 
Question 4 of 10
4. Question
When would you want to perform a chisquare test for linear trend instead of (or in addition to) a chisquare test for association?
CorrectIncorrect 
Question 5 of 10
5. Question
Questions 59 are based on ananalyses conducted in a fictitious study of 20000 Chinese men to investigate the risk factors for prevalent chronic obstructive pulmonary disease (COPD) (recorded by the binary variable has_copd ).
A logistic regression model was run to investigate the relationship between prevalent COPD and age (continuous variable, in years). How would you interpret the output?
CorrectIncorrect 
Question 6 of 10
6. Question
[Continuing from the previous question]
Another logistic regression model was run to investigate the relationship between COPD and education groups (recorded by a categorical variable called education, coded from low to high level: 1= no formal school; 2= primary school; 3= middle or high school; 4= technical school/college or above). How would you interpret the results?
CorrectIncorrect 
Question 7 of 10
7. Question
[Continuing from the previous question]
Instead of a categorical variable, this time the education variable was input as an ordinal variable in the logistic regression. You obtained a single OR of prevalent COPD associated with the ordinal variable education, which is 0.54 (95% CI: 0.510.58) with p<0.001. Choose all that are correct.
CorrectIncorrect 
Question 8 of 10
8. Question
After running separate univariable logistic regression models on prevalent COPD with age, education, everalcohol drinking (recorded by a binary variable evralc), and eversmoking (recorded by a binary variable evrsmk), all of there variables were significantly associated with COPD. However, you wondered if confounding could explain some of these unvariable associations. You therefore ran a multivariable logistic regression model including all these variables. How would you interpret the results?
CorrectIncorrect 
Question 9 of 10
9. Question
You wanted to investigate if there is potential interaction between smoking and age on prevalent COPD. You fitted the below multivariable model with an interaction term between eversmoking (binary variable evrsmk: 0= neversmokers, 1= eversmokers) and age groups (binary variable agebin : 0= below 60 years, 1 = 60 years or above). Based on these output, choose all that are correct.
CorrectIncorrect 
Question 10 of 10
10. Question
(modified from Discovering Statistics Using IBM SPSS Statistics, by Andy Field)
When analysing continuous predictor variables in logistic regression, we assume a:
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