Week 2

Question 1

Normal curves

Answer 1

when curves are symmetrical, unimodal, and bell-shaped

Question 2

The 68-95-99.7 rule

Answer 2

Approximately 68% of the observations fall within σ and the mean μ
Approximately 95% of the observations fall within 2 σ of μ
Approximately 99.7% of the observations fall within 3 σ of μ

Question 3

Cumulative proportion

Answer 3

is the proportion of observations that lie below or at a given value.

Question 4

Find the proportion from X

Answer 4

o Standardize, Subtract the mean, then divide by the standard deviation, to transform the problem about X into a problem about a standard Normal Z

Question 5

statistical inference

Answer 5

When you we infer conclusions about the wider population based on a sample

Question 6

parameter (p)

Answer 6

is a number that describes the population. It is a fixed number but in practise we don’t know its value (e.g u and o)

Question 7

statistic (p ̂)

Answer 7

is a number that describes a sample. The value is known because we took the sample. We often use a statistic to estimate an unknown parameter (X bar and sx)

Question 8

sampling variability

Answer 8

the value of a statistic varies in repeated random sampling

Question 9

sampling distributions

Answer 9

of a statistic is the distribution of values taken by the statistic in all possible samples of the same size from the same population

Question 10

Bias

Answer 10

concerns the centre of the sampling distribution

Question 11

Unbiased estimator

Answer 11

When the centre lies at the true value of the parameter

Question 12

Margin error

Answer 12

is a numerical measure of the spread of a sampling distribution. It can be used to set bounds on the size of likely error in using the statistic as an estimator of a population parameter

Question 13

To reduce bias

Answer 13

use random sampling

Question 14

To reduce variability

Answer 14

use a large sample. You can make the variability as small as you want by taking a large enough sample