Stats Exam 4 Flashcards Preview

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Flashcards in Stats Exam 4 Deck (32):
1

Chi Square Null

Ho: There is no relationship between the two categorical variables.
Alternative

2

Chi Square Alternative

Ha: There is a relationship between the two categorical variables.

3

Assumptions of The Chi-Square Test for Independence

1. The sample should be random.
2. In general, the larger the sample, the more accurate and reliable the test results are. All expected counts need to be greater than 1, with at least 80% exceeding 5 to ensure reliable use of the test. Note: this rule applies only to expected frequencies. It is acceptable for an observed frequency to be 0, provided the expected frequencies meet the criterion

4

Linear correlations

-Have two components: direction & size
-Both described by “r”(sample) or “ρ” (rho, population)
r = Pearson’s Correlation Coefficient

5

Properties of linear correlation coefficient r

- Range: -1 ≤ r ≤ 1
Scale is irrelevant (based on standardized scores)
Only measures strength of linear associations
DOES NOT IMPLY CAUSALITY

6

r^2

r2 = proportion of the variation in y that is determined by x

7

interpreting r


0.5 0.9 : correlation is very strong
r = ±1.00 : correlation is perfect

8

Is the given r value statistically significant?

A weak correlation (small r) can be significant.

A moderate/large correlation can occur by chance alone and be statistically insignificant.

If r is NOT significant…
the best predictor of x is x_
the best predictor of y is y_

9

Regression line

= A “best fit line”, y = mx + b.

10

Residuals

variation not explained by the regression model

11

Least Squares Property

Linear regression produces the smallest possible sum of squares for residuals.
S.O.S. Residuals= Unexplained Variation

12

If no significant correlation exists, the best estimate of Y is

the MEAN of Y

13

F statistic

Mean Square Regression / Means Square Residual

14

F Test for Regression

Tells us if the regression model is statistically significant.

15

Multiple Regression

Bivariate regression can be extended to multivariate data
-When 2 or more independent variables may be related to a dependent variable

Advantages
-Improved predictive value (r square)
-Estimates are more precise

16

r^2 (R^2)

Multiple Coefficient of Determination
r2 still equals the amount of variation in one variable, explained by other predictor variables.

17

Adjusted Coefficient of Determination

Adding predictor variables will increase r2, even if the contribution is trivial.
The best regression equation may not have the largest r2.
For multiple regression, use an adjusted r2



k = number of predictor variables

The adjusted r square increases only if the new variables contribution is more than what would be expected by chance alone.

18

What independent variables to include as predictors?

1) Consider thCommon sense & practical considerations.
- Bear “Age” may be predictive, but impractical to measure.
2) e standardized coefficients
- Independent variables converted to Z scores. “Standardized coefficients” indicated relative strength influence
3) Evaluate several regression models
Choose the model with the highest adjusted r square and the fewest variables possible
Avoid multicollinear variables (head width & ear tip distance)
Choose the equation with the lowest P value (based on F statistic in ANOVA table)

19

χ2

χ2 = quantifies difference from expected frequencies
Small χ2 >> due to random variation.
Large χ2 >> unlikely to occur by chance.
No negative values & always a one tailed test

20

If the observed frequencies perfectly match the expected frequencies…

you would see 0

21

If the observed frequencies are vastly different than the expected frequencies…

you would see a big value

22

chi square calculations

Sigma[(O-E)^2/E]

23

Chi-Square Distribution

Skewed right
χ2 = 0 to ∞
Different curve for every degree of freedom
Degrees of freedom = (rows–1)*(columns–1)

24

Chi Square Review

Evaluates C  C relationships
Compares expected to observed frequencies


Tests of Independence
Expected = P*n = (row total*column total)/total
Used to test any frequency related hypothesis
E.g.: Car accidents are 5 times more common on weekdays than on weekends.

25

Number Needed to Treat to prevent one case

The number of subjects we would need to treat, to prevent one case of disease

26

Risk

Probability for a condition/disease

27

Risk Ratio

a ration of two sample risks

28

Risk & Risk Ratio Hypotheses

H0: RR = 1.0 Ha: RR ≠ 1.0

29

CI for Risk Ratio

--RR captures 1.0  Fail to reject H0,Fail to support Ha
--RR does not include 1.0  Reject H0, support Ha

30

Risk Ratio Caveat

Only applies to “natural” data, that are not “case controlled”.
Includes Prospective Studies
Randomized Controlled Trials (E.g. Salk Vaccine)
The natural incidence of disease is observed
Excludes most Retrospective Studies
Case controlled studies
Experimenter decides how many cases of each condition to include
Odds Ratios can be used in these cases
Odds compare the incidence of one condition to another (not to the total)

31

Odd

A ratio of the incidence of one condition to it’s complimentary condition. Not a probability.

32

Risk vs Odds Ratio

An odds ratio is always valid
A risk ratio is valid only if the incidence of the the response variable occurs naturally.
The component risks must be valid
Often not valid in retrospective studies
Hypothesis Testing and Confidence Interval Interpretation are the same.
H0: RR = 1.0 Ha: RR ≠ 1.0
H0: OR = 1.0 Ha: OR ≠ 1.0