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Elang 273: Statistics. Review: Scientific Method 1. Observe something 2. Speculated why it is so and form hypothesis 3. Test hypothesis by getting data.

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Presentation on theme: "Elang 273: Statistics. Review: Scientific Method 1. Observe something 2. Speculated why it is so and form hypothesis 3. Test hypothesis by getting data."— Presentation transcript:

1 Elang 273: Statistics

2 Review: Scientific Method 1. Observe something 2. Speculated why it is so and form hypothesis 3. Test hypothesis by getting data 4. Analyze data (using statistics)

3 Statistics Is the word glistening used more often in one register (as shown in COCA) than another? SECTIONSPOKENFICTIONMAGAZINENEWSPAPERACADEMIC PER MIL0.412.02.82.10.6 SIZE (MW)76.669.678.173.473.0 FREQ3283321915643 How much different do these frequencies have to be before we can say they are different? Stats tell you.

4 Review: P value Researchers have agreed that if the chance that the difference between two groups is greater than a certain percentage, then we will consider the difference to be statistically significant. A significant difference is better than one in twenty of happening by chance (p <.05). The opposite of significance is random chance.

5 Review: Types of data 1. Nominal (Categorical): sex, race, national origin, native speaker, how often you choose one thing over another, how often a word occurs in one register versus another 2. Continuous (ratio or interval): height, weight, age, scores on a language test, IQ, working memory span 3. Ordinal (Rank Order): No fixed interval (first, second, third place in a race)—what order people choose their favorite dialect

6 Review: Variables Dependent: what the test measures Independent: what you think may influence the dependent Experiment asks how independent variables effect the dependent variable

7 1. Nominal (Categorical) Correct dialect identification by American English speakers

8 2. Continuous

9 3. Ordinal (Rank Order) Coupland & Bishop, 2007

10 Two types of statistics 1. Descriptive (used to describe data) a. average (mean) b. percentile c. highest and lowest scores 2. Inferential (used to test hypothesis) a. chi-square b. t-tests/ANOVA c. correlations d. regression

11 Descriptive vs. Inferential Descriptive: Class A had 75% average on test and Class B had 81% You can't conclude that B is better than A. Inferential: Statistical analysis (t-test) shows that the grades in B are significantly higher than in A.

12 1. Descriptive Statistics These are the types of statistics you are familiar with—showing means, percentages, quartiles, usually through bars, pie charts, and graphs

13 2. Inferential Statistics a. Chi square b. ANOVA/t-test c. Correlations (rank order correlations) d. Logistic regression

14 2. Inferential Statistics For each type of statistics we need to know 1. Statistical value (R value, chi square value, F statistic, t statistic) 2. Probability value (p value) 3. Degrees of Freedom (df)

15 Other concepts Null hypothesis Control group Sampling


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