Exam 1

Question 1

Population

Answer 1

All subjects being studied in a given area (ex. all people with schizophrenia in the United States)

Question 2

Sample

Answer 2

Data from a subset of the population (sample size = n)

Question 3

Biased Sample

Answer 3

The biased sample systematically overestimates or systematically underestimates a characteristic of the population

Question 4

Random Sample

Answer 4

Every member of the population has the same chance of being included in the sample and the members of the sample are chosen independently from one another (meaning that the chance of a given number of the population being chosen does not depend on which other members are chosen

Question 5

Sampling Error

Answer 5

Chance effects causing discrepancy between sample and population.

Question 6

Sampling Bias

Answer 6

Systematic tendency for some individuals of the population to be selected more readily than others

Question 7

Nonresponse bias

Answer 7

Bias caused by persons not responding to some of the questions in a survey or not returning a written survey

Question 8

Systematic Sampling

Answer 8

Samples next to one another will never be picked (every nth term will be included in sample)

Question 9

Stratified Random

Answer 9

Each subpopulation (stratum) is made up of a more homogenous collection of subjects (i.e. sample of state of New York- out of a larger whole)

Question 10

Subsample size

Answer 10

Number of population size of each stratum

Question 11

Statistical Inference

Answer 11

Making conclusions about a population based on a sample

Question 12

Dotplot

Answer 12

A simple graph that can be used to show the distribution of a numeric variable when the sample size is small

Question 13

Histogram

Answer 13

A bar graph version of a dot plot

Question 14

Skewed to the right

Answer 14

Values are more concentrated to the left side in the distribution, so the right side has a longer tail

Question 15

Population Mean

Population Standard Deviation

Answer 15

mu = population mean
sigma = population standard deviation

Question 16

Response vs. Explanatory Variable

Answer 16

Response Variable: focus of a question in a study or experiment
Explanatory Variable: Explains changes in that variable

Question 17

Qualitative vs. Quantitative Variables

Answer 17

Quantitative: measures that can be written by numbers, discrete and continuous
Qualitative: exploratory research on underlying opinions, motivations. Nominal and Ordinal

Question 18

What is the difference between a statistic and a parameter?

Answer 18

Statistic: a numerical measure that describes a sample
Parameter: a numerical measure that describes a population

Question 19

What is noise in statistical terms?

Answer 19

A term for recognized amounts of unexplained variation in a sample.

Question 20

Efficiency

Answer 20

Takes full advantage of all the information in the data, so fewer observations or data points are needed to get the same performance

Question 21

What are 4 ways to measure the spread of your data?

Answer 21

Range, Average Deviation, Variance, and Standard Deviation

Question 22

What is a significant result?

Answer 22

The likelihood that a relationship between two or more variables is caused by something other than random chance. Statistical hypothesis testing is used to determine whether the result of a data set is statistically significant.

Question 23

(A) U (B) - Union or intersection?

Answer 23

Union = one or the other or both events occur
Intersection = event that both occurred

Question 24

Type I Error

Answer 24

We reject the null hypothesis when HA is true. Claim that data provide evidence that significantly supports HA when H0 is true.

Question 25

Type I Error

Answer 25

We reject the null hypothesis when HA is true (finding significant evidence for HA when H0 is true). If HA is true but we do not observe sufficient evidence to support HA.

Question 26

Type II Error

Answer 26

The null is true when its actually not true (not finding significant evidence for HA when HA is true)

Question 27

P-Value of a hypothesis test

Answer 27

Probability, under the condition that H0 is true, of the test statistic being at least as extreme as the of the observed value. (measure of compatability between the data and H0)

Question 28

Random variable

Answer 28

Takes on numerical values that depend on the outcome of a chance operation

Question 29

QQ Plot

Answer 29

We are comparing our sample values to theoretical values (aka comparing our distribution to the normal distribution to find the relationship) to see how the values fit to see how normal our data is

Question 30

Sampling Variability

Answer 30

Variability among random samples from the same population

Question 31

Sampling Distribution

Answer 31

A probability distribution that characterizes some aspect of sampling variability

Question 32

Central Limit Theorem

Answer 32

No matter what distribution Y may have in the population, if the sample size is large enough, then the sampling distribution of Y will be approximately a normal distribution

Question 33

Beta

Answer 33

The probability of committing a Type II Error

Question 34

Power

Answer 34

1 - Beta Power = 1 - Pr(accepting H0 when it is false) A test is more powerful when it correctly rejects the null when there is an alternate hypothesis available

Question 35

ts (test statistic)

Answer 35

It is a measure of how far the difference between the sample means (y bar) is from the difference we would expect to see if H0 were true expressed in relation to the SE of the difference - the amount of variation we expect to see in differences of means from random samples.

Question 36

P-value

Answer 36

is the area under students t curve in the double tails beyond -ts and +ts

Question 37

Why do we divide the variance by n-1 instead of n?

Answer 37

We want average of the sample variances for all possible sets of samples to equal the population variance Using n – 1 as denominator produces an unbiased estimate of σ2