Final Exam Part 2

Question 1

Two variable bivariate hypothesis tests exmples

Answer 1

• tabular analysis
• difference of means
• correlation coefficient and regression

Question 2

Tabular Analysis

Answer 2

• Categorical (IV and DV)
• goal: is difference bw groups statistically significant?
• How: calculate the chi2 stat and p-value

Question 3

Degrees of Freedom

Answer 3

max amount of independent values, that have freedom to vary in data sample
- Formula: degrees of freedom=size of data sample-1

Question 4

critical value

Answer 4

point on the test distribution that is compared to the test stat to determine whether to reject the null hypothesis

Question 5

level of significance

Answer 5

• in a p-value approach
• level of significance= alpha
  alpha=1-confidence interval
• probability of incorrectly rejecting the null hypothesis

Question 6

Difference of Means

Answer 6

• continuous DV and IV
• use sample means and SD to make inferences about unobserved pop.
• are means different across values of independent variable?
• How: calculate the t-statistic and p-value

Question 7

correlation coefficient

Answer 7

• are they correlated/associated?
• how do we know they are correlated?
• measure of the strength of the relationship
• more of X associated w more/less of Y
• can use scatter plot or pearsons r regression

Question 8

perfect negative correlation

Answer 8

-1

Question 9

perfect positive correlation

Answer 9

1

Question 10

no linear relationship bw two variables

Answer 10

0

Question 11

The pearsons r

Answer 11

• no unambiguous classification rule for the quantity of a linear relationship bw two variables
• tell us how confident we can be that relationship is different from what we would see if our IV and DV were not related in population

Question 12

examples of non-linear relationships

Answer 12

• quadratic/culvilinear

- cubic/polynomial

Question 13

common mistakes w pearsons r

Answer 13

• it is not applicable for non-linear relationships

- it does not equal slope

Question 14

R-square

Answer 14

• always bw 0-1

- proportion of variation in the DV(y) that is explained in the IV (x)

Question 15

coefficient in the regression

Answer 15

represents mean increase of Y for every additional unit of X

Question 16

Simple regression model

Answer 16

no controls (only X and Y)

Question 17

multiple regression model

Answer 17

controls for confounding variables (Z)

- more applicable in the real world, bc realworld is multivariate

Question 18

most causal theories are…

Answer 18

bivariate, does X cause Y

Question 19

two-variable regression

Answer 19

fitting the best line through a scatter plot of data

Question 20

statistical model

Answer 20

slope and y-intercept

Question 21

systematic components of population regression model

Answer 21

• aplha= fixed value, value of Y depends on alpha, so no random value in systematic components
• when X changes by 1 unit, then value of Y changes by beta units

Question 22

random components of population regression model

Answer 22

• cannot predict values of this part (no systematic patterns)
• condition or mood

Question 23

names for the estimated stochastic component

Answer 23

• residual: leftover part of Yi, after line was drawn

- sample error mean/pop. error term

Question 24

how to calculate the smallest sum of residual values (ordinary-least- squares)

Answer 24

• add the squared value of each the residuals for each line

- choose line that has the smallest total value–> draw line that minimizes the sum of the squared residuals

Question 25

goodness of fit measures (root-mean squared error)

Answer 25

- measures of the overall fit bw a regression model and the dependent variable - quantifies how well the OLS regression we have obtained fits the data - r-squared statistic

Question 26

p value and estimated beta

Answer 26

how likely it is that we observe this sample slope of estimated beta from the real world if the true (but unobserved) population slope beta is equal to 0

Question 27

two-tailed (non-directional) hypothesis tests

Answer 27

most common hypothesis tests about parameters from the OLS regression model

Question 28

in any observational study, how do we control for the effects of other variables?

Answer 28

multiple regression is the most common method in social science

Question 29

omitted variables bias

Answer 29

bias from the failure to include a variable that belongs in regression model (result of omitting a variable, Z, that should have been in the model)

Question 30

small bias

Answer 30

if either or both components of bias term are close to zero (red area overlapped is small)

Question 31

large bias

Answer 31

if both components are likely large (red area overlapped is quite large)

Question 32

positive bias

Answer 32

if beta1 and beta2 correlate bw X and Y are positive

Question 33

standardized coefficient

Answer 33

remove metric of each variable to make them comparable to one another - coefficients on a standardized metric

Question 34

unstandardized coefficients

Answer 34

coefficients in the table each exist in the native metric of each variable. Normally not comparable