Alles bij elkaar brengen!

Laten we nu overstappen op een nieuwe vraag om enkele vaardigheden uit dit hoofdstuk te oefenen. We gaan onderzoeken of het totale cholesterol gemiddeld verschilt tussen fysiek actieve en niet-actieve Amerikanen.

Deze oefening maakt deel uit van de cursus

Enquêtedata analyseren in R

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Find means of total cholesterol by whether or not active 
out <- ___(formula = ___,
           by = ___, 
           design = NHANES_design,
           FUN = ___, 
           na.rm = TRUE, 
           keep.names = FALSE)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Enquêtedata analyseren in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

Our exploration of survey data will begin with survey weights. In this chapter, we will learn what survey weights are and why they are so important in survey data analysis. Another unique feature of survey data are how they were collected via clustering and stratification. We'll practice specifying and exploring these sampling features for several survey datasets.

Exercise 1: What are survey weights?Exercise 2: Survey weights Exercise 3: Visualizing the weights Exercise 4: Specifying elements of the design in R Exercise 5: Designs in R Exercise 6: Stratified designs in R Exercise 7: Cluster designs in R Exercise 8: Comparing survey weights of different designs Exercise 9: Visualizing the impact of survey weights Exercise 10: NHANES weights Exercise 11: Tying it all together!

Now that we have a handle of survey weights, we will practice incorporating those weights into our analysis of categorical data in this chapter. We'll conduct descriptive inference by calculating summary statistics, building summary tables, and constructing bar graphs. For analytic inference, we will learn to run chi-squared tests.

Exercise 1: Visualizing a categorical variable Exercise 2: Summarizing a categorical variable Exercise 3: Interpreting frequency tables Exercise 4: Graphing a categorical variable Exercise 5: Exploring two categorical variables Exercise 6: Creating contingency tables Exercise 7: Building segments bar graphs Exercise 8: Summarizing with svytotal()Exercise 9: Interpreting svymean()Exercise 10: Inference for categorical variables Exercise 11: Running a chi squared test Exercise 12: Tying it all together!

Of course not all survey data are categorical and so in this chapter, we will explore analyzing quantitative survey data. We will learn to compute survey-weighted statistics, such as the mean and quantiles. For data visualization, we'll construct bar-graphs, histograms and density plots. We will close out the chapter by conducting analytic inference with survey-weighted t-tests.

Exercise 1: Kwantitatieve gegevens samenvatten Exercise 2: Enquête-statistieken Exercise 3: Kwantiëlen schatten Exercise 4: Kwantitatieve gegevens visualiseren Exercise 5: Staafdiagrammen van enquête-gewogen gemiddelden Exercise 6: Uitvoer van svyby()Exercise 7: Staafdiagrammen met foutmarges Exercise 8: Histogrammen met enquêtegewichten Exercise 9: Steekproefgewogen dichtheidsplots Exercise 10: Inferentie voor kwantitatieve data Exercise 11: Enquête-gewogen t-toets Exercise 12: Alles bij elkaar brengen!

Huidige oefening

To model survey data also requires careful consideration of how the data were collected. We will start our modeling chapter by learning how to incorporate survey weights into scatter plots through aesthetics such as size, color, and transparency. We'll model the survey data with linear regression and will explore how to incorporate categorical predictors and polynomial terms into our models.

Exercise 1: Visualization with scatter plots Exercise 2: Bubble plots Exercise 3: Survey-weighted scatter plots Exercise 4: Use of color in scatter plots Exercise 5: Visualizing trends Exercise 6: Line of best fit Exercise 7: Trend lines Exercise 8: Modeling survey data Exercise 9: Regression model Exercise 10: Regression inference Exercise 11: More complex modeling Exercise 12: Multiple linear regression Exercise 13: Tying it all together Exercise 14: Wrap-up