Een chi-kwadraattoets uitvoeren

Tijd om te testen of er een verband is tussen het niveau van depressie en de ervaren gezondheid met een chi-kwadraattoets. Onthoud dat de p-waarde van de chi-kwadraattoets aangeeft hoe consistent onze steekproefresultaten zijn met de aanname dat depressie en ervaren gezondheid niet samenhangen.

Deze oefening maakt deel uit van de cursus

Enquêtedata analyseren in R

Cursus bekijken

Oefeninstructies

Gebruik svychisq() om te testen op een relatie tussen Depressed en HealthGen.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Run a chi square test between Depressed and HealthGen
___(___, 
    ___ = ___, 
    statistic = "Chisq")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Enquêtedata analyseren in R

SkillTag.level.intermediateSkillTag.label

4.8+

181 reviews

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: Een categorische variabele visualiseren Exercise 2: Een categorische variabele samenvatten Exercise 3: Frequentietabellen interpreteren Exercise 4: Een categorische variabele visualiseren Exercise 5: Twee categorische variabelen verkennen Exercise 6: Contingentietabellen maken Exercise 7: Gesegmenteerde staafdiagrammen maken Exercise 8: Samenvatten met svytotal()Exercise 9: Uitleggen van svymean()Exercise 10: Inferentie voor categorische variabelen Exercise 11: Een chi-kwadraattoets uitvoeren

Huidige oefening

Exercise 12: Alles samenbrengen!

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: Summarizing quantitative data Exercise 2: Survey statistics Exercise 3: Estimating quantiles Exercise 4: Visualizing quantitative data Exercise 5: Bar plots of survey-weighted means Exercise 6: Output of svyby()Exercise 7: Bar plots with error Exercise 8: Survey-weighted histograms Exercise 9: Survey-weighted density plots Exercise 10: Inference for quantitative data Exercise 11: Survey-weighted t-test Exercise 12: Tying it all together!

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