correlation

Question 1

this is the variable where you do not count but measure

Answer 1

continuous variable

Question 2

this set the limits to statistical analysis and determines which test are appropriate

Answer 2

type of variables
research design

Question 2

it involves the same study participants or involves comparison of matched pairs of study participants, a paired test of statistical significance

Answer 2

before-and-after comparison

example:
in drug study
- 1 receives treatment
- other does not

in diet
- new diet
-continues their regular diet

Question 3

why does some common statistical test might not work properly

Answer 3

u aren’t using random sampling where every1 has an equal chance of being selected

if non-random sampling = misleading results

Question 4

what type of variables used for

Answer 4

Pearson correlation coefficient/ linear regression = C + C
Spearman correlation coefficient = C + O
student t test = C + DU (example: gender)
paired test = C + DP (example before vs after treatment)

Question 5

a kind of hypothesis which gives generalization for generating records recording the mean of the primary/ original position

Answer 5

parametric test
- good for random sampling bcs u know the popu and SD

*usually a normal distribution & often focuses on the mean

Question 6

non parametric test is aka

Answer 6

distribution-free test

Question 7

what does it mean that non parametric does not require any population

Answer 7

distinct parameters

Question 8

It is also a kind of hypothesis testing, which is not based on the underlying hypothesis

Answer 8

non parametric test

Question 9

the test is based on the differences in the median.

Answer 9

non parametric test

Question 10

what types of variables used in non parametric test and parametric test

Answer 10

non parametric test
- nominal
- ordinal
parametric test
- interval
- ratio

Question 11

parametric test vs non parametric test

Answer 11

parametric test: (pearson C)
assumes
requires popu knowledge
central tendency value: mean
normal distribution

non parametric test: (spearman C)
does not assume
does not require popu knowledge
central tendency value: median
not normal distribution

Question 12

Defined as the quantification of the degree to which 2 continuous random variables are related, provided that the relationship is linear.

Answer 12

correlation

linear - r/s or pattern

Question 13

how do u visualize correlation

Answer 13

joint distribution graph / two-way scatter plot

Question 14

scatter plot

Answer 14

x axis - independent
y axis - dependent

present r/s btwn 2 variables
represent on a 2 dimensional plane or cartesian system

Question 15

if points trend upward:
if points trend downward:
random, no pattern:

Answer 15

if points trend upward: +ve C
if points trend downward: -ve C
random, no pattern: no C

Question 16

when does scatter plot being used

Answer 16

• paired numerical data
• multiple values of dependent values for a unique value of independent variable
• determine r/s btwn 2 variables
  (ex: identify potential root causes of problems)

Question 17

____ variable depend on ____ variable

Answer 17

dependent
independent

Question 18

r

Answer 18

referred to as r value
- varies from -1 (perfect nega C) to +1 (perfect positive C)

Question 19

this measures the strength and direction of the relationship between 2 variables

Answer 19

Pearson Correlation Coefficient
* when 1 variable changes, the other variable changes in the same direction

Question 20

value of r - degree of correlation

Answer 20

0 : no
0.01 - 0.35 : weak/ low
0.36 - 0.70 : average
0.71 - 0.99 : strong/ high
1 : perfect

*consider 0.99 as perfect

Question 21

seeks to quantify the linear Relationship that may exist between an independent variable x and a dependent variable y

Answer 21

linear regression

Question 22

it can be changed/ control:
it can be measured/ observed:

Answer 22

it can be changed/ control: independent (x)
it can be measured/ observed: dependent (y)

Question 23

specifies how much y would be expected to change (and in what direction) for a unit change in x .

Answer 23

regression

Question 24

true or false:
linear regression is related to correlation

Answer 24

true - produces 2 parameters that can be directly related to the data

Question 25

what are the 2 parameters that can be directly related to the data

Answer 25

slope
- shows how steep
intercept
-whr the line crosses the y axis

Question 26

difference btwn correlation and regression

Answer 26

correlation:
measures how strong r/s btwn x and y
regression: (predict)
clearer pic of how changes in x affect y

Question 27

It’s appropriate when you have a large enough sample size and your data meets certain conditions, like being normally distributed.

Answer 27

parametric test

Question 28

it’s useful when you have smaller samples, or when your data is ranked or categorized (like survey responses) rather than measured on a continuous scale.

Answer 28

nonparametric test