Flashcards in Quantitative Methods V Deck (31):

1

## Null hypothesis

### One you want to reject in order to assume alternate is correct

2

##
Test statistic

(same for Z and T)

###
(sample mean - hypothesized mean) / standard error

remember, standard error = stdev / sqrt(n)

3

## Two tailed vs. one-tailed

###
Two tailed is Ho = something

One-tailed is Ho > something

4

## Type I error

### Rejection of null when it's actually true

5

## Type II error

### Failure to reject when it is actually false

6

## Power of test

### 1 - probability of type II error

7

## Confidence interval (Z-test)

### Sample mean - (standard error * critical z-value) < mean < sample mean + (standard error * critical z-value)

8

## P-value

### Prob of test stat that would lead to a type I error.

9

## T-Test

###
Use if population variance is unknown and either:

1. Sample is large

2. Sample is small, but distribution is normal

10

## Z-Test

### Use if population is normal, with known variance or when sample is large and population variance is unknown

11

## Sample distribution

### Sample statistics computed from samples of the same size drawn from the same population

12

## Desired properties of estimators (3)

### Efficiency, consistency and unbiasedness

13

## Data mining

### Searching for trading patterns until one "works"

14

## Sample selection bias

### Some data is systematically excluded (e.g. from lack of availability)

15

## Look ahead bias

### Study tests relationship using sample data that wasn't available on test date

16

## Time period bias

### Either too long or too short

17

## Chi-squared (X^2)

###
Used for tests concerning variance of normally distributed population vs. sample

Symmetrical, approaches normal as d.f. increases

Chi is bounded by 0 so test stats can't be negative

18

## Chi-squared test statistic

###
[(n-1) * sample variance] / hypothesized pop. variance

FYI, critical value is for one tail, so you have to adjust for two

19

## F-test

###
Tests equality of variances of two populations via samples of said populations

Populations are normal and samples are INDEPENDENT.

Bounded by 0 (like chi square)

20

## F-test: two tailed vs. one-tailed

###
Two tailed: variance of pop. 1 = variance pop. 2

One-tailed: variance of pop. 1 >= variance pop. 2

21

## F-statistic

###
Variance sample 1 / variance sample 2

Note: always put larger variance in numerator, use d.f. of larger sample and look at right tail.

22

## Difference in means test

###
T-statistic

Two INDEPENDENT samples, normally distributed populations

Formula won't be on test.

23

## Paired comparisons test

###
T-test statistic

Used when samples are dependent.

Sample data must be normally distributed.

24

## Paired comparisons test (formula)

###
Tstat = (sample mean difference - mean) / standard error of mean difference

Sample mean difference = 1/n * sum(mean1 - mean2...)

Standard error = sample stdev / sqrt(n)

25

## Elliot wave theory - impulse wave

### Direction of the prevailing trend, has five smaller waves

26

## Elliot wave theory - corrective wave

### Against the prevailing trend, has three smaller waves.

27

## Type II Error probability

### Calculate probability that you fail to reject the null when is actually false. Here you use the actual M and get the stat of (X-bar - M) / standard error.

28

## Treynor Ratio

### (portfolio return - risk free return) / Beta

29

## Consistent estimator

### Gets closer to population as n increases

30

## Unbiased estimator

### Expected value = true population value

31