Maths

Question 1

Giga

Answer 1

(G)
x 1,000,000,000

Question 2

Mega

Answer 2

(M)
x 1,000,000

Question 3

Kilo

Answer 3

(k)
x 1,000

Question 4

Deci

Answer 4

(d)
/ 10

Question 5

Centi

Answer 5

(c)
/ 100

Question 6

Milli

Answer 6

(m)
/ 1,000

Question 7

Micro

Answer 7

(μ)
/ 1,000,000

Question 8

Nano

Answer 8

(n)
/ 1,000,000,000

Question 9

Volume of a prism

Answer 9

area of cross section x length

Question 10

Volume of cylinder

Answer 10

π x r^2 x h

Question 11

Volume of sphere

Answer 11

4/3 x π x r^3

Question 12

Surface area of prism

Answer 12

(2 × Base Area) + (Base perimeter × height)

Question 13

Surface area of cylinder

Answer 13

2(π. x r^2) x π.r x length

Question 14

Surface area of sphere

Answer 14

4 x π. x r^2

Question 15

Surface area of cube

Answer 15

6(length x width)

Question 16

Percentage error

Answer 16

maximum uncertainty/ experimental reading x100
(maximum uncertainty is half the distance between the two smallest graduations on a piece of equipment).

Question 17

∝

Answer 17

Proportional to
(One variable has a constant ratio to another variable. They change at the same rate, so the relationship between them doesn’t change).

Question 18

≈

Answer 18

Approximately equal to

Question 19

Density =

Answer 19

mass / volume

Question 20

Calculating mean in frequency table

Answer 20

Find the total sum of each value multiplied by its frequency and divide by total frequency.

Question 21

Standard deviation

Answer 21

x = individual value
μ = (x̄) mean
n = number of values

Question 22

Chi squared test

Answer 22

Used when looking for a difference between several categories that have no particular order and the data collected involves whole number frequencies.

Question 23

Chi squared test: Step 1

Answer 23

Construct a null hypothesis, which states that there is no significant difference between the things you are measuring.
(eg. the differences are due to random error and only occurred by chance).

Question 24

Chi squared test: Step 2

Answer 24

Calculate the expected frequency if the null hypothesis was true (there was no difference).
(Expected frequency = total freq. / number of categories)

Question 25

Chi squared test: Step 3

Answer 25

Calculate chi-squared
X^2 = chi-squared
O = frequency observed
E = frequency expected

Question 26

Chi squared test: Step 4

Answer 26

Determine degrees of freedom:
number of categories - 1

Question 27

Chi squared test: Step 5

Answer 27

Look up the critical value of chi-squared for your probability and degrees of freedom

Question 28

Chi squared test: Step 6

Answer 28

Reject or accept null hypothesis and write a conclusion.
–> If the value of chi-squared is equal to or greater than the critical value then reject null hypothesis (and difference between the results is significant)
–> If the value of chi-squared is less than the critical value than there is no difference so accept null hypothesis
(Eg. The chi-squared is greater than the critical value. We reject the null hypothesis. The difference between the results is significant and unlikely to be due to chance. More animals are found on bladder wrack).

Question 29

t-Test

Answer 29

Used when looking for a difference between means, the independent variable has just two categories (eg. treated vs untreated) and the dependent variable has continuous data.

Question 30

Unpaired t-Test

Answer 30

Looks at whether there is s difference in the means between the two separate/independent groups.

Question 31

Paired t-Test

Answer 31

Looks at whether there is a difference in the mean between between the same group before and after a change.

Question 32

Unpaired t-Test: Steps

Answer 32

S1: Construct a null hypothesis
S2: Label the set as set 1 or set 2
S3: Calculate t
S4: Determine the degrees of freedom (n1 + n2 - 2)
S5: Look up critical value for t
S6: Reject or accept null hypothesis and write conclusion (H0 ≥ CV reject H0 OR H0 ≤ CV accept H0)
(Eg. The value for t is greater than the critical region. We reject null hypothesis. The difference between the means is significant and unlikely to be due to chance. A high sugar diet does increase weight gain).

Question 33

Calculating t (unpaired)

Answer 33

x̄1 = mean of set 1
x̄2 = mean of set 2
S^2 1 = sd of set 1 squared
S^2 2 = sd of set 2 squared
n1 = number of items in set 1
n2 = number of items in set 2

Question 34

Paired t-Test: Steps

Answer 34

S1: Construct a null hypothesis
S2: Calculate t
S3: Determine the degrees of freedom (n-1)
S4: Look up critical value
S5: Reject or accept null hypothesis ad write conclusion

Question 35

Calculating t (paired)

Answer 35

đ = mean of differences between each pair of measurement
n = number of pairs
sd = standard deviation of the differences between each pair of measurements

Question 36

Correlation Coefficient/ Spearman’s Rank

Answer 36

Used when looking for correlation/relationships between two variables (eg. alcohol consumption and incidence of cancer).
(Continuous variables)

Question 37

Correlation coefficient: Steps

Answer 37

S1: Construct a null hypothesis, which states that there is no correlation between the velocity of the two
S2: Draw out a modified table of results and rank both variables from smallest to largest
S3: Find the difference in rank between the two variables for each participant (D), then square (D^2)
S4: Calculate rs
S5: Look up the critical value for rs for your probability and number of observations
S6: Reject or accept null hypothesis and write conclusion

Question 38

rs

Answer 38

d = difference between ranks
n = number of pairs of data

Question 39

Independent variable

Answer 39

What is changed.

Question 40

Dependent variable

Answer 40

What is measured.

Question 41

Continuous data

Answer 41

Data that can take any value.
eg. height, body mass

Question 42

Discontinuous data

Answer 42

Only a limited number of possible values.
eg. shoe size, tongue rolling ability

Question 43

Discrete data

Answer 43

The values are distinct and separate.

Question 44

Normal distribution

Answer 44

Many individuals have a middle value for a feature with fewer having greater or lesser values.
It forms a bell shape on charts and graphs.