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IB Mathematics AA Standardization of normal variables Study Notes

IB Mathematics AA Standardization of normal variables Study Notes

IB Mathematics AA Standardization of normal variables Study Notes

IB Mathematics AA Standardization of normal variables Notes Offer a clear explanation of Standardization of normal variables, its mean and variance, including various formula, rules, exam style questions as example to explain the topics. Worked Out examples and common problem types provided here will be sufficient to cover for topic Standardization of normal variables, its mean and variance.

Standardization of Normal Variables

Introduction

The process of standardization allows us to compare data points from different normal distributions by converting them into a common scale called the z-score. The z-value represents the number of standard deviations a data point is from the mean.

Key Concepts

1. Standardization Formula

  • The z-score is calculated using the formula:

\( z = \frac{x – \mu}{\sigma} \)

  • \( x \) = observed value
  • \( \mu \) = mean of the distribution
  • \( \sigma \) = standard deviation of the distribution
  • The z-value tells us how many standard deviations away \( x \) is from \( \mu \).

2. Properties of z-Scores

  • If \( z > 0 \), the value is above the mean.
  • If \( z < 0 \), the value is below the mean.
  • If \( z = 0 \), the value is exactly at the mean.

Connections and Applications

1. Links to Other Subjects

  • Biology: The normal distribution is used to analyze biological measurements.
  • Psychology: Descriptive statistics in psychology often use standardization.

Solved Example

Problem: A standardized test has a mean score of 500 and a standard deviation of 100. A student scores 650 on the test. Find the z-score and interpret it.

Step 1: Identify Given Values

  • \( x = 650 \) (observed value)
  • \( \mu = 500 \) (mean)
  • \( \sigma = 100 \) (standard deviation)

Step 2: Apply the Standardization Formula

\( z = \frac{x – \mu}{\sigma} \)

\( = \frac{650 – 500}{100} \)

\( = \frac{150}{100} \)

\( = 1.5 \)

Step 3: Interpretation

  • The student’s score is 1.5 standard deviations above the mean.
  • This means the score is higher than the average test score.

IB Mathematics AA SL Standardization of normal variables Exam Style Worked Out Questions

Question

A survey is conducted in a large office building. It is found that \(30\% \) of the office workers weigh less than \(62\) kg and that \(25\% \) of the office workers weigh more than \(98\) kg.

The weights of the office workers may be modelled by a normal distribution with mean \(\mu \) and standard deviation \(\sigma \).

a.(i)     Determine two simultaneous linear equations satisfied by \(\mu \) and \(\sigma \).

(ii)     Find the values of \(\mu \) and \(\sigma \). [6]

b.

Find the probability that an office worker weighs more than \(100\) kg. [1]

c.

There are elevators in the office building that take the office workers to their offices.

Given that there are \(10\) workers in a particular elevator,

find the probability that at least four of the workers weigh more than \(100\) kg. [2]

d.

Given that there are \(10\) workers in an elevator and at least one weighs more than \(100\) kg,

find the probability that there are fewer than four workers exceeding \(100\) kg. [3]

e.

The arrival of the elevators at the ground floor between \(08:00\) and \(09:00\) can be modelled by a Poisson distribution. Elevators arrive on average every \(36\) seconds.

Find the probability that in any half hour period between \(08:00\) and \(09:00\) more than \(60\) elevators arrive at the ground floor. [3]

f.

An elevator can take a maximum of \(10\) workers. Given that \(400\) workers arrive in a half hour period independently of each other,

find the probability that there are sufficient elevators to take them to their offices. [3]

 
▶️Answer/Explanation

Markscheme

Note:     In Section B, accept answers that correctly round to 2 sf.

a.(i)     let \(W\) be the weight of a worker and \(W \sim {\text{N}}(\mu ,{\text{ }}{\sigma ^2})\)

\({\text{P}}\left( {Z < \frac{{62 – \mu }}{\alpha }} \right) = 0.3\) and \({\text{P}}\left( {Z < \frac{{98 – \mu }}{\sigma }} \right) = 0.75\)     (M1)

Note:     Award M1 for a correctly shaded and labelled diagram.

\(\frac{{62 – \mu }}{\sigma } = {\Phi ^{ – 1}}(0.3)\;\;\;( =  – 0.524 \ldots )\;\;\;\)and

\(\frac{{98 – \mu }}{\sigma } = {\Phi ^{ – 1}}(0.75)\;\;\;( = 0.674 \ldots )\)

or linear equivalents     A1A1

Note:     Condone equations containing the GDC inverse normal command.

(ii)     attempting to solve simultaneously     (M1)

\(\mu  = 77.7,{\text{ }}\sigma  = 30.0\)     A1A1

[6 marks]

b.

Note:     In Section B, accept answers that correctly round to 2 sf.

\({\text{P}}(W > 100) = 0.229\)     A1

[1 mark]

c.

Note:     In Section B, accept answers that correctly round to 2 sf.

let \(X\) represent the number of workers over \(100\) kg in a lift of ten passengers

\(X \sim {\text{B}}(10,{\text{ }}0.229 \ldots )\)     (M1)

\({\text{P}}(X \ge 4) = 0.178\)     A1

[2 marks]

d

Note:     In Section B, accept answers that correctly round to 2 sf.

\({\text{P}}(X < 4|X \ge 1) = \frac{{{\text{P}}(1 \le X \le 3)}}{{{\text{P}}(X \ge 1)}}\)     M1(A1)

Note:     Award the M1 for a clear indication of a conditional probability.

\( = 0.808\)     A1

[3 marks]

.e.

Note:     In Section B, accept answers that correctly round to 2 sf.

\(L \sim {\text{Po}}(50)\)     (M1)

\({\text{P}}(L > 60) = 1 – {\text{P}}(L \le 60)\)     (M1)

\( = 0.0722\)     A1

[3 marks]

f.

Note:     In Section B, accept answers that correctly round to 2 sf.

\(400\) workers require at least \(40\) elevators     (A1)

\({\text{P}}(L \ge 40) = 1 – {\text{P}}(L \le 39)\)     (M1)

\( = 0.935\)     A1

[3 marks]

Total [18 marks]

 

Question

a.Farmer Suzie grows turnips and the weights of her turnips are normally distributed with a mean of \(122g\) and standard deviation of \(14.7g\).

(i)     Calculate the percentage of Suzie’s turnips that weigh between \(110g\) and \(130g\).

(ii)     Suzie has \(100\) turnips to take to market. Find the expected number weighing more than \(130g\).

(iii)     Find the probability that at least \(30\) of the \(100g\) turnips weigh more than \(130g\). [6]

b.

Farmer Ray also grows turnips and the weights of his turnips are normally distributed with a mean of \(144g\). Ray only takes to market turnips that weigh more than \(130g\). Over a period of time, Ray finds he has to reject \(1\) in \(15\) turnips due to their being underweight.

(i)     Find the standard deviation of the weights of Ray’s turnips.

(ii)     Ray has \(200\) turnips to take to market. Find the expected number weighing more than \(150g\). [6]

 
▶️Answer/Explanation

Markscheme

a.(i)     \(P(110 < X < 130) = 0.49969 \ldots  = 0.500 = 50.0\% \)     (M1)A1

Note:     Accept \(50\)

Note:     Award M1A0 for \(0.50\) (\(0.500\))

(ii)     \(P(X > 130) = (1 – 0.707 \ldots ) = 0.293 \ldots \)     M1

expected number of turnips \( = 29.3\)     A1

Note:     Accept \(29\).

(iii)     no of turnips weighing more than \(130\) is \(Y \sim B(100,{\text{ }}0.293)\)     M1

\(P(Y \ge 30) = 0.478\)     A1 [6 marks]

b.

(i)     \(X \sim N(144,{\text{ }}{\sigma ^2})\)

\(P(X \le 130) = \frac{1}{{15}} = 0.0667\)     (M1)

\(P\left( {Z \le \frac{{130 – 144}}{\sigma }} \right) = 0.0667\)

\(\frac{{14}}{\sigma } = 1.501\)     (A1)

\(\sigma  = 9.33{\text{ g}}\)     A1

(ii)     \(P(X > 150|X > 130) = \frac{{P(X > 150)}}{{P(X > 130)}}\)     M1

\( = \frac{{0.26008 \ldots }}{{1 – 0.06667}} = 0.279\)     A1

expected number of turnips \( = 55.7\)     A1 [6 marks] Total [12 marks]

 
 

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