relational Statistics - workshops 1, II, III.pptx
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Three hands-on workshops on how to apply relational statistics in news reporting. In teaching partnership with the amazing Jodi Upton.
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relational Statistics - workshops 1, II, III.pptx
- 1. Stats I, II and II Frequencies, crosstabs, correlation, ANOVA, regression Jodi Upton and Crina Boros CIJ Summer 2017
- 2. The Data Ladder -- categorical I. One type of response (yes or no) Frequencies: Crosstabs: Yes 432 45.3% No 521 54.7% Live in Texas Like Bush Yes No Yes 382 200 No 125 307
- 3. The Data Ladder-- categorical II. Two or more types of responses (race) Frequencies: Race Frequency Asian 4,766 Black 12,807 White 9,766 Hispanic 7.236 Crosstabs: Race Warning Ticket None Black 1 6 0 White 4 3 1 Hispanic 0 1 2 Unknown 3 2 2
- 4. The Data Ladder-- categorical III. Ordinal Data (use crosstabs and frequencies) When the value doesn’t mean much, but the order does: Grade levels Age categories Income categories
- 5. The Data Ladder-- continuous data Examples: Income Housing prices Response time (police and fire) Distance travelled (commute) What you can do: Mean Median Range Rank Correlation ANOVA Regression
- 6. Go to Kahoot.it (on your phone or computer)
- 8. In traditional statistics, the normal curve means 95% of observations will fall within most of this curve
- 11. Independent vs. Dependent variable Independent Comes first in time Can be more than one variable Dependent What you are measuring
- 12. Polling A March 9, 2016 Quinnipiac poll found the following results, with a +- 3.7 margin of error, at the 95% confidence level. Who is really ahead? What’s the MOE for women? White males?
- 14. CORRELATION AKA: Pearson’s r or coefficient of correlation ● Between 1 and -1 ● If both variables move in the same direction → positive relationship ● If variables move in opposite direction → negative relationship -1 0 +1 Strong relationship weak weak strong
- 15. Got it, so far?
- 16. ANOVA What it assumes: Normal distribution Independence of errors Outliers removed* Equal variance (*but journalists love those!) What it measures: Whether the difference within the group is greater than the difference between the groups
- 17. ANOVA needs an hypothesis Null hypothesis: the treatment has no impact F = the treatment variance + the random variance the random variance
- 18. What you’re looking for: The F statistic is between 0 and 1 (if it’s negative, you’ve made a mistake) If F > F crit, you must reject the null hypothesis (treatment had an impact) If F < F crit, you can’t rule out the null hypothesis The p value If the p value is less than alpha (.05) then the result is significant (it matters) If the p value is greater than alpha, the results are not significant
- 19. In Massachusetts, are there more suicides in local jails or in the prison system?
- 20. What you still don’t know What accounts for the difference? For that you need a t-test, regression or other tool.
- 21. ‘HOW TO CHOOSE’ MADE EASY THE 2 MOST ESSENTIAL QUESTIONS: 1. DO YOU HAVE CATEGORICAL OR CONTINUOUS DATA IN THE VARIABLES? 2. WHAT IS YOUR INDEPENDENT VARIABLE AND DEPENDENT VARIABLE? INDEPENDENT DEPENDENT STATISTICS Categorical Categorical CROSS-TAB Continuous Continuous LINEAR-REGRESSION / MULTIPLE REGRESSION Categorical Continuous ANALYSIS OF VARIANCE / ANOVA Continuous Categorical LOGISTIC REGRESSION
- 22. iT’S A FINE DAY FOR LINEAR REGRESSION! Image by Paul Wesley
- 23. Linear Regression I. Does the data fit the 1st assumption: is there a linear relationship? 1. Scatter plot 2. Trendline 3. Create a new variable II. The last assumption: the data should approximate a Bell curve (normal distribution). 1. Data analysis toolpak - Descriptive statistics 1. Mean and average should be close to each other 1. Tick Summary Statistics 2. Tick Confidence level >> 95%
- 26. Source: Indian Journal of Dermatology https://tinyurl.com/ydad546c
- 27. Linear Regression Conditions met? Run the Regression from the Data Analysis tool pack: Y Range - Dependant X Range - Independent Turn on LABELS CONFIDENCE LEVEL 95% NEW WORKSHEET - REGRESSION RESIDUALS ADJUSTED R SQUARE 0 TO 1.0. The closer it gets to 1, the closest is to perfection. SIGNIFICANT F THE RESIDUAL STORY - Sort! THE LINEAR REGRESSION IS JUST THE BEGINNING OF THE REPORTING Conrad Carlberg - Statistical Analysis
- 29. Thank you! Jodi Upton: jodi.upton@gmail.com and @jodiupton Crina Boros: crinaboros@gmail.com Special thanks to: Jennifer LaFleur, Center for Investigative Reporting/Reveal Steve Doig, Arizona State University
Notes de l'éditeur
- Starting at the bottom...
- Heard continuous referred to as ‘infinite’ but it’s really not. Income and prices, for example, are limited to two decimal places. Another way that may help: Continuous (measured) vs discrete (counted) If it would take ‘forever’ to count, it’s probably continuous
- Amy Poehler
- Skew = body (positive or negative); kurtosis = tail
- All you really need to know: is my data evenly distributed
- The margin of error does not depend on the size of the population; it depends on the size of the sample. (In astronomy, the margin of error is 4.12 light years -- the distance to Proxima Centauri)
- Important: 1 in 20 observations will NOT! Bayesian: start with a different hypothesis, 100% within curve, but may be ‘off’
- ANOVA was created by an evolutionary biologist and statistician, who wanted to be able to tell if two groups were the same or different, ie were they the same species or not?
- In other words, is there enough randomness within the sample, that it outweighs any variance between the samples -- and any measured difference is the result of chance?
- “F” stands for Sir Reginald Fisher, who invented this “P” stands for the probability that -- if the null hypothesis is true -- the results are ‘extreme’ (one in 500 chance of being wrong)
- A paper ‘suicide suit’ worn by a model
- ANOVA was created by an evolutionary biologist and statistician, who wanted to be able to tell if two groups were the same or different, ie were they the same species or not?