301 final

a sample such that each member of the underlying population has an equal probability of being selected.

data for every possible realveant case

a probabilistic statement about the likely value of a population charterisitc based on the observation in the sample

the relationship between two varribales is not due to radom chance and therefore exists in the boarder population

a bell-shaped statistical distburtion that can be entirely charterzied by its mean and standard deviation

the standard deviation of the sampling distribution of the sample mean.
Inference- the process of using what we know about a sample to make probabilistic statements about the border population

a method of bivarriate hypothesis testing that is appropriate for a categorical independent varribale and a continuous dependent varribale

measure of linear association between 2 continuous varribales

predicts no relationship between the varribales

specific type of bias that results from the failure to include a varribale that belongs in the regression model

regression coefficients such that the rise-over-run interpretation is expressed in standard deviation units of each varribale

when there is an exact linear relationship between any two or more of a regressions models independent varriable

a varribale that takes on one of two values usually 1 or zero

probablilty of an event is the number of times that event can occur relative to the total number of times any event can occur.

The probability of an event is between 1 and 0
If the events can not occur simultaneously then p is the sum of the event probabilities
Probability that an event will not occur is 1-p(event)
Sum of probabilities from all possible events is 1

two events are independent if the chance of one event occurring is not affected by the outcome of the other event.

1. Addition rule for mutually exclusive events -p. (outcome 1 or outcome 2) = p(outcome1) or p(outcome2)
2. Complement rule: -p(not outcome 1)= 1-p(outcome1)
3. Multiplication rule for independent outcomes- -p(outcome1 and outcome2)= p(outocme1 x P(outcome 2)
4. Multiplication rule for dependent events

Perform a hypothesis test
Start with null hypothesis

There is no difference between treatments
What the probablility distrubution is

reject it

is the process of making probabilistic statements about a population characteristic based on our knowledge of the sample charterisitic.

result of radom observation

two categorical varribales

binaryiv/ internalatio dv

ways to measure an association between a and y where both values are at the intervalatio level of measurement

visual representation of the data on an x and y axis

1. State the null hypothesis
2. Look at the data
3. Computing the “calculated” t
4. Calculate p-value
5. Draw a conclusion about the null hypothesis

Reject the null hypothsis

accept the null hypotheis