Interpreting Tables

Question 1

What does statistics mean?

Answer 1

The science of learning from data.

Question 2

Which are the 2 types of data sources?

Answer 2

1. Primary data.

2. Secondary data.

Question 3

What are the primary data?

Answer 3

Collected specifically of investigation.
Experiments.
Observation.
Interviews.
Surveys.

Question 4

What are the secondary data?

Answer 4

Already compiled.
Available for analysis.
Records.
Databases.
Health data.

Question 5

What does the frequency in a table mean?

Answer 5

The cunt in each brand category.

Question 6

How can we analyse qualitative data?

Answer 6

Ask participants a common questions.
Make a table base on the question, the participants, and their answers.

Calculate the frequency of their answers based on the question.
Calculate the %frequency.
Cumulative frequency.
% Cumulative frequency.

Question 7

What is the cumulative frequency when calculated in a table of dataset took from participants for a qualitative data analysis?

Answer 7

Running total of the frequency column.

Question 8

What is the % cumulative frequency?

Answer 8

Running total of the % frequency column.

Question 9

What do we find when they ask: ‘What % of respondents did an action?’?

Answer 9

% frequency value.

Question 10

What do we answer when they ask :’How many respondents bought this no more than twice?’?

Answer 10

Count frequency values of nil (o) + once + twice.

Question 11

What do we answer when they ask: ‘How many respondents bought this at least 3 times?’?

Answer 11

Count frequency values of 4 + 4 + 5 + 6 + 7 + more.

Question 12

What is the cumulative frequency?

Answer 12

The number of individuals/items in all categories up to and including the considered category.

Question 13

What is the cumulative percent?

Answer 13

The cumulative frequency of a categories divided by the sample size multiply by 100%.

Question 14

How can we design a questionnaire when the question is:
‘Who buys fast food most often?’
and we need to find:
the association between age group and no. of purchases?

Answer 14

Ask respondents their age and how often they buy fast food.

Pu data on a table.

Count total of data.

Question 15

Where do we use contingency tables?

Answer 15

For nominal.

Ordinal variables.

Question 16

Why do we use contingency tables?

Answer 16

To record.

Analyse relationship between 2/more variables.

Question 17

What does each cell on the dataset represent?

Answer 17

The number of items into corresponding categories.

Question 18

Where do we have totals on a dataset table?

Answer 18

Horizontally and landscape.

Question 19

Which is a higher outcome?:
7 out of 8 people
or
7 out of 12 people?

Answer 19

7 out of 8 people.

Question 20

What do we use when the counts are not very helpful to make them more informative?

Answer 20

Percentage.

Question 21

Where do use percentage in contingency tables?

Answer 21

For nominal and ordinary variables.

Question 22

How are the column percentages obtained?

Answer 22

(Each cell / corresponding column total ) * 100%.

Question 23

How can we calculate row percentages?

Answer 23

(Each cell / corresponding row total ) * 100%.

Question 24

How can we calculate total percentages?

Answer 24

(Each cell / sample) * 100%.

Question 25

Whatis percentage equal to?

Answer 25

Proportion. | Probability.

Question 26

If we have a SPSS Contingency table of household income v Internet use of participants. How much is the % of internet non-users? How much is the proportion of them? How much is the probability of them in the sample?

Answer 26

Total amount of internet non-users / Total amount of all participants * 100= 40%. 40%. 40% or P(internet non user) = 0.4

Question 27

How do we show the probability value of a fact in the sample?

Answer 27

P(X) = value.

Question 28

How much is the % of internet non-users with an upper household income?

Answer 28

Value of no-user upper / total non-users value * 100% = value. Same with proportion and probability.

Question 29

How do we show the probability of a fact with another fact in the sample, known as condition probability?

Answer 29

P(X|Y) = probability of X given the condition Y.

Question 30

What will the probability of a randomly selected participant being a male who smokes be?

Answer 30

Male value / total number of participants.

Question 31

What are the marginal tools?

Answer 31

Marginal probabilities. | Total numbers of questions asked to participants.

Question 32

What is the probability of a randomly selected individual being a male?

Answer 32

Total male / total. Not mentioned smoking/not. Includes all cases.

Question 33

What is the probability of a randomly selected individual smoking?

Answer 33

total who smoke/total. No gender mentioned.

Question 34

What is the probability of a randomly selected male smoking?

Answer 34

Males smoke / total males. Only interested in male. Only smokers.

Question 35

What is the probability that a randomly selected smoker is male?

Answer 35

Male smokers / total smokers. Focus on smokers. Male only.

Question 36

What are the comparing proportions?

Answer 36

Probabilities which provide evidence for/against associations.

Question 37

What is the probability that a woman has breast cancer?

Answer 37

P(D) = Total with disease/Total in study.

Question 38

What is the probability that a woman has breast cancer given she has taken HRT?

Answer 38

P(D | E) = no. taking HRT with disease / no. taking HRT.

Question 39

What is the probability that a woman has breast cancer given she has not taken HRT?

Answer 39

P(D | not E) = no. of taking HRT with disease / no. not taking HRT in study.

Question 40

What is the association between the values that a woman has breast cancer with and without HRT?

Answer 40

HRT taken --> bigger probability to have breast cancer = 0.01 > 0.007.

Question 41

What % of patients had an outcome of 'Full Recovery'?

Answer 41

Total number of full recovery / Total number of all.

Question 42

Of patients who received a high dose, what % recovered fully?

Answer 42

High dose fully recover value / Total of high dose value.

Question 43

Of patients who made a full recovery, what % received a medium dose?

Answer 43

Medium dose dully recover / Total of fully recover.

Question 44

Of patients who died, what % received at least a medium dose?

Answer 44

Sum of medium dose value + high dose / Total of death value.

Question 45

For patients who received a low dose of drug, what % of patients had at most a minor disability?

Answer 45

Sum values of low dose of death + vegetative state + major disability + minor disability / total of low dose value.

Question 46

For patients who received at least a low dose of drug, what % of patients had at most a minor disability?

Answer 46

Sum of values 1, 2, .., until minor disability of low dose and more people /sum of low dose and more of patients.