stats

Question 1

what is an absolute value?

Answer 1

the distance from 0 on a number line, can be positive or negative. if a number is negative its absolute value is equal to the positive value of the number.

Question 2

what does an exhaustive variable mean?

Answer 2

it covers everyone

Question 3

what does a mutually exclusive variable mean?

Answer 3

everyone fits into one and only one place

Question 4

what is a nominal variable?

Answer 4

a variable with unordered categories

Question 5

what is a ordinal variable?

Answer 5

a variable with ordered categories and undefined distances between values.

Question 6

what is an interval variable?

Answer 6

a variable with defined distances between values and arbitrary zero (0 DOESN”T mean nothing) ex. temperature.

Question 7

what is a ratio variable?

Answer 7

a variable with defined distances between values and non-arbitrary zero. 0=0. Ex.Age.

Question 8

describe discrete vs continuous (interval and ratio intervals)

Answer 8

discrete is measured in whole numbers when continuous is measured in units that are infinitely divisible.

Question 9

variables with only two possible values are called ____

Answer 9

dummy, dichotomous or binary variables

Question 10

deterministic causality means…

Answer 10

if A causes B deterministically if A’s occurrence is always followed by B’s occurrence.

Question 11

probabilistic causality means…

Answer 11

A causes B probabilistic if A’s occurrence increases the probability of B’s occurrence

Question 12

how to determine causality…

Answer 12

we have reason to believe that x causes y when…
1. there is an association between x and y
2. x precedes in time
3. we have eliminated spurious casual linkages
4. we have a plausible explanatory rational for the casual relationship.

Question 13

The multivariate casual scenarios

Answer 13

1. Indirect or chain casual relationships
2. Spurious associations
3. Multiple Causality
4. Statistical interactions
   5.Suppression

Question 14

describe indirect or chain causal relationships

Answer 14

x1 casually influences x2 which then casually influences y. (x1 -> x2 -> y)

Question 15

describe spurious associations

Answer 15

a third variable x2 casually influences both x1 and y such that an empirical association exists between x1 and y but the association is not casual.

Question 16

describe multiple causality

Answer 16

x1 and x2 have distinct effects on y

Question 17

describe statistical interactions

Answer 17

x1 influences y differently for different values of x2

Question 18

describe suppression

Answer 18

x1 is related to y through distinct processes that cancel each other out.

Question 19

what does holding or controlling or above and beyond x2 mean?

Answer 19

what’s the effect of x1 with x2 set asides. removed x2 from the story. what is x1’s unique explanation.

Question 20

when thinking of OLS regression line

Answer 20

• y= bx + A
• b represents the slope of the regression line
• A positive value of b means that the slope is positive
• b represents the change in Y for a unit change in X, i.e., if X increases by a value of one, Y changes by an amount of b
  -A represents the value of Y at which the line crosses the Y axis. It is known as the intercept.

Question 21

what does R2 mean

Answer 21

R2 = the proportion of total variability in Y that is explained by X

Question 22

what are beta 1 and beta 2?

Answer 22

beta 1 represents to change in Y in standard
deviations for a one standard deviation change in x1, holding x2.
beta 2 represents the change in y in standard deviations for a one standard change in x2 holding x1 constant.

Question 23

what are the four ways we talked about for causality stuff

Answer 23

1. quick and dirty
2. eliminate cofounding
3. explore mediation
4. explore statistical interaction

Question 24

describe 1. quick and dirty multiple causality
2.eliminate cofounding
3. explore mediation
4. explore statistical interactions

Answer 24

1. quick and dirty multiple causality assemble one multiple regression model with lots of variables what’s the effect of controlling variables.
2. eliminate cofounding is about causality. Eliminate confounding and spuriousness.
3. explore mediation involves establishing causation, and explaining an association.
4. exploring statistical interactions involves having a story about _____, brings opinion.

Question 25

what does R2 = 0.0373 mean?

Answer 25

3% of the variability in Y is explained by these demographic factors. This is small.

Question 26

is P=0.913 or P=0.627 statistically significant?

Answer 26

no

Question 27

what are the 6 aspects of tests of significance

Answer 27

test of significance, test statistic, p value, alpha level, conclusion, assumptions

Question 28

test of significance - describe null and alternative hypothesis

Answer 28

null is not associated in the population. alternative is there is an association in the population.

Question 29

describe p value

Answer 29

the lower the value the more likely to contradict the Null.

Question 30

describe alpha level

Answer 30

number that p value must be below to contradict the null. 0.05 usually.

Question 31

what is the conclusion after finding p and alpha

Answer 31

is p value less than alpha or not, if yes then reject the null in favour of the alternative, if no them you don't reject.

Question 32

practice question: suppose you produced a 95% confidence interval for a variable from a sample size of n=100. describe two strategies you might follow to produce a narrower confidence interval.

Answer 32

1. increaser sample size (larger n = smaller margin of error 2. reduce to 90% confidence interval

Question 33

practice question: describe different operationalize of a UBC students GPA at the quantitative, ordinal, nominal, and dichotomous level of measurement.

Answer 33

quantitative = GPA nearest 10th or percentage ordinal = letter grades nominal = none dichotomous = pass or fall

Question 34

practice question: you are interested on the casual effect of self-esteem on self-rated health. Describe an experimental approach to studying this issue, a cross sectional approach to the issue and a longitude approach to the issue.

Answer 34

experimental = group of 100 randomly assign 50 to experimental and 50 to a control, spend am hour watching positively, then measure self rated health. cross section = random people in the street and ask them to rate self esteem and self rated health longitude = take some people and keep asking them overtime so you can track changes.

Question 35

we can explore the association. Between a categorical variable x and a numerical variable y by comparing?

Answer 35

o 1. Central tendencies (means or medians) o 2. Variability (standard deviations or interquartile ranges) and o 3. Shapes (histograms or boxplots) o For the distributions of Y by each value of X.

Question 36

What is Cramers v used for?

Answer 36

measure the strength of the categorical by categorical association

Question 37

what are the "association scores" for Cramers V

Answer 37

0.00 = no association 0.00-0.10 = very weak 0.10-0.20 = weak 0.20-0.30 = modest 0.30-0.40 = strong 0.40 or higher = very strong

Question 38

what is Kendalls tau-b?

Answer 38

tells us about the direction of the association (categorical by categorical)

Question 39

what are the "association scores" for Kendals Tau B

Answer 39

direction: range from a low of -1 (a perfect negative association) to a high of 1 (a perfect positive association) magnitude: 0.00 = no association 0.00-0.10 = very weak 0.10-0.20 = weak 0.20-0.30 = modest 0.30-0.40 = strong 0.40 or higher = very strong

Question 40

what is Pearsons R

Answer 40

Pearson r measures the direction and strength of a linear associated between two numerical variables. Pearson 4 is also referred to a r, Pearson correlation, correlation, or correlation coefficient. How closely do the point hug the linear line? The closer they are the stronger the association.

Question 41

the shapes of Pearsons r

Answer 41

You can get a good sense of the strength of the association by the shape of the cloud of points. Is the cloud of points hsaped more like a soccer ball (representing no association), a football (a moderate association) or a cucumber (strong association

Question 42

lets use the following protocol when interpreting the magnitude (strength) of r:

Answer 42

0.00 = no association 0.00-0.10 = very weak 0.10-0.20 = weak 0.20-0.30 = modest 0.30-0.40 = strong 0.40 or higher = very strong

Question 43

how to tell the direction of Pearsons R

Answer 43

R is positive when the association is positive and negative when the association is negative; r = 0 represents no linear association at all.

Question 44

what is spearman's rho

Answer 44

Spearman's Rho measures the direction and strength of an association between two numerical variables. It replaces the actual data values with ranks and then executes a Pearson's correlation on the ranked data. § Where is this country ranked lowest to highest? § Scatter plot will show if its linear or not

Question 45

OLS regression line

Answer 45

A straight line in a two-dimensional space can be represented by the equation Y = bX + a. In a scatterplot the equation for the straight line that best represents the pattern of the points can be a useful summary of the nature of the linear association between the two variables VERTICAL DISTANCE OF THE POINT FROM THE LINE

Question 46

R2 provides an indication of the strength of the association. Let's use the following protocol when interpreting the magnitude of R2.

Answer 46

0.00 = no association 0.00-0.01 = very weak 0.01-0.04 = weak 0.04-0.09 = modest 0.09-0.16 = strong 0.16 or higher = very strong

Question 47

how to conduct numerical by numerical (in order)

Answer 47

- Always start with scatter plot (lowess) - If linear we can use persons r (test of significance, 95% confidence interval) - If linear we can additionally execute a OLS regression line (b,a, beta, r squared, 95% confidence) - If curvy linear, we can transform x or y to make it linear OR spearmans ro - If it looks crazy/other, make an ordinal version or x or y. - Maybe do a contingency table.

Question 48

what is strength

Answer 48

strength is how closely points hug the line

Question 49

what is a chi-square used for

Answer 49

a test of significance for categorical by categorical

Question 50

what to mention when describing situation

Answer 50

single peak? skewed? postive or negative? curvilinear? floor or ceiling effect? linear?

Question 51

explain experimental design

Answer 51

Randomly assign study participants to an experimental group or a control group, introduce the treatment X to the experimental group but not the control group; measure Y in each group and compare the scores of Y between the two groups

Question 52

explain cross sectional design

Answer 52

Measure X and Y in a group of study participants at a single point in time

Question 53

explain longitudinal design

Answer 53

Measure X and Y in a group of study participants at multiple points in time

Question 54

what are the five random sampling strategies

Answer 54

1. simple random sample 2. systematic random 3. stratified random sample 4. cluster random sample 5. multistage random sample