Module 1

Question 1

What is a Noninal Scale used for?

Answer 1

It’s used to label variables in different classifications and does not imply a quantitate value or order (for example, gender, sex, ethnicity but there are no order in it)

Question 2

What is an Interval Scale?

Answer 2

It is defined as a numerical scale where the order of the variables and the difference between them are known. Moninal and Ordinal are included in the Interval scale. You can categorise, rank and infer the time differences between neighbouring data points. But there is no true 0 point. By that, it means there is no starting or nothing point to build on. For example, temperature uses this. The scale will increase or decrease, but it cannot X or + itself (i.e., 12 degrees x 2 degrees + 24 degrees)

Question 3

What is an Ordinal Scale?

Answer 3

It’s used to represent non-mathematical ideas such as frequency, satisfaction, happiness, a degree of pain, etc. (you can rank something in order, e.g., someone comes first, second, last. But you can’t infer that their time difference is the same. For example, runners that some first, second, and third you can record, but you can know the time between the athletes.).

Question 4

What is a Ratio Scale

Answer 4

It’s a variable measurement scale that produces the order of variables and makes the difference between the known variables, along with information about the value of the true 0—for example, age. You might have a participant who is 40 years old and another who is 20 years old. The 40-year-old is 2X older than the 20-year-old.

Question 5

Mean

Answer 5

In math and statistics, the mean summarises an entire dataset with a single number representing the data’s centre point or typical value. It is also known as the arithmetic mean, the most common measure of central tendency. It is frequently called the “average.” It adds up all the numbers and then divides by the data set’s size.

Question 6

Medium

Answer 6

The median, in statistics, is the middle value of the given list of data when arranged in an order. The data or observations can be arranged either in ascending order or descending order. Example: The median of 2,3,4 is 3.

Question 7

Mode

Answer 7

Definition. The mode is the most common number that appears in your set of data. To find the mode, count how often each number appears, and the number that appears the most times is the mode.

Question 8

Kurtosis

Answer 8

It is the measurement between the curve’s peak and the curve’s tails. The kurtosis measure is responsible for capturing this phenomenon

Question 9

Mesokurtic

Answer 9

This is when the distribution of kurtosis is 3. It is considered a normal kurtosis.

Question 10

Leptokurtic

Answer 10

This is when the distribution of kurtosis is greater than 3: the greater the value, the more the peakedness.

Question 11

Platykurtic

Answer 11

This is when the distribution of kurtosis is less than 3.

Question 12

Descriptive statistics

Answer 12

Sample size could be a descriptive statistic. Whatever described the sample you have chosen. A sample average could also be a descriptive statistic.

Question 13

Scales of measurement

Answer 13

Nominal, ordinal, interval, ratio

Question 14

Quartiles

Answer 14

In statistics, quartiles are a way to divide data into four equal parts, or quarters, based on the value of the data points.

Question 15

Box and whisker plot range

Answer 15

The difference between the lowest and highest values in a data set. To calculate the range, subtract the lowest value from the highest value

Question 15

Interquartile range

Answer 15

The interquartile range defines the difference between the third and the first quartile.

Question 16

Inferential statistics

Answer 16

I am using a population sample size to infer something about the more significant portion of that population. For example, testing 100 babies to infer something about all babies in Australia

Question 17

Statistic

Answer 17

Describe sample

Question 18

Perimeter

Answer 18

Describe a population

Question 19

Random variability

Answer 19

Something (a variable) that varies unpredictably.

Question 20

Deseret random variability

Answer 20

Only a small limited number of variables can happen—for example, rolling a die, number of patients coming to the ER in the next hour, number of children in a family, number of fatalities in Sydney, etc.

Question 21

Probability distributions

Answer 21

This is the way we can describe all the possible values and likelihoods that a random variable can take. This is also known as the expected distribution. The significance of the mean of a probability distribution is it gives the expected value of a discrete random variable.

Question 22

Uniform distributions

Answer 22

All the outcomes have a uniform probability. For example, tossing a coin has a 50% chance.

Question 23

Idealised frequency distribution

Answer 23

A normal distribution is a common idealised representation of a frequency distribution. It is a bell-shaped curve symmetric around a peak in the middle. The mean and standard deviation are the two parameters that define the distribution

Question 24

Continuous random variables

Answer 24

These are variables that cannot be counted as they might be infinite - for example, age (you can break this down into years, days, hours, seconds, and it can go on infinitely) or another example could be trying to measure rain (you could break down into millilitres) the time it takes you. They are not countable.

Question 25

Exponential distribution

Answer 25

The time between each arrival/occurrence measures the expected time for an event, such as when one patient arrives and the next or until the next earthquake.

Question 26

Gamma distribution

Answer 26

This is used to measure continuous variables that possess positive and skewed distributions. This is used for how insurance companies work.