Thursday, November 8, 2018
Sunday, October 21, 2018
Wednesday, September 19, 2018
Sunday, August 26, 2018
HL Math - Pythagoras and Trigonometry
HL Pythagoras and Trigonometry.
This work has also been placed on the google classroom.
Links to the worksheets:
Thursday, August 23, 2018
Monday, August 6, 2018
Sunday, August 5, 2018
Monday, June 11, 2018
Tuesday, May 29, 2018
Sunday, May 20, 2018
Mathematics Standard information
This slideshow shows the information about the Mathematics Standard we are assessing in this unit. It also has a list of the skills you need to learn, practice and apply through the business plan and a list of how this can be assess in your business plan.
Monday, April 9, 2018
Mathematics Assessment: Please read carefully!!!!
Read all steps before opening and using the data sheet. Do not waste any of the double period we have as this will impact how much of the assessment we get through.
Statistics Multivariate data.
Using the information you gathered from the Maketu Estuary you will compare they samples of the species evident during your observation.
I have attached the spreadsheet of data.
On the first sheet it has the data collected at each transect and the distance along the transect which it was collected. This is useful information for discussing why certain species were more evident at different parts of the estuary.
For the data to be displayed in a box and whisker we need to sort into species. I have set up a table for you to put the data into. You will need to organise yourselves into 4 groups. This way you only need to sort around 30 samples each rather than the entire data set. Each group will sort 3 transect lines worth of data for each of the four species. E.g Group 1 will put the information for transect 1, 2 & 3 and place the numbers collected under the headings for Amphibola crenata, Zeacumantus lutulentus, Gracilaria (% cover) & Ulva lactuca (% cover).
Once you have completed this you can make a copy of one of the tables and use this for your assessment.
You must sort the data from smallest to biggest for each species (Amphibola crenata, Zeacumantus lutulentus, Gracilaria (% cover) & Ulva lactuca (% cover)). Then work out the summary statistics, Minimum, lower quartile, median, upper quartile, maximum, range & interquartile range.
You are required to complete a full PPDAC cycle so need to make a question, prediction and plan can be written using the science experiements (you may want to wait until I am back before completing this part and focus on sorting the data and drawing the graphs first.
You can then place this information into a box and whisker for each species (so four boxes in total), and place the dots above the boxes to show spread.
Then make the necessary comments for the analysis section and conclusion.
Here is the link to access the spreadsheet.
Spreadsheet of data
Statistics Multivariate data.
Using the information you gathered from the Maketu Estuary you will compare they samples of the species evident during your observation.
I have attached the spreadsheet of data.
On the first sheet it has the data collected at each transect and the distance along the transect which it was collected. This is useful information for discussing why certain species were more evident at different parts of the estuary.
For the data to be displayed in a box and whisker we need to sort into species. I have set up a table for you to put the data into. You will need to organise yourselves into 4 groups. This way you only need to sort around 30 samples each rather than the entire data set. Each group will sort 3 transect lines worth of data for each of the four species. E.g Group 1 will put the information for transect 1, 2 & 3 and place the numbers collected under the headings for Amphibola crenata, Zeacumantus lutulentus, Gracilaria (% cover) & Ulva lactuca (% cover).
You must sort the data from smallest to biggest for each species (Amphibola crenata, Zeacumantus lutulentus, Gracilaria (% cover) & Ulva lactuca (% cover)). Then work out the summary statistics, Minimum, lower quartile, median, upper quartile, maximum, range & interquartile range.
You are required to complete a full PPDAC cycle so need to make a question, prediction and plan can be written using the science experiements (you may want to wait until I am back before completing this part and focus on sorting the data and drawing the graphs first.
You can then place this information into a box and whisker for each species (so four boxes in total), and place the dots above the boxes to show spread.
Then make the necessary comments for the analysis section and conclusion.
Here is the link to access the spreadsheet.
Spreadsheet of data
Sunday, April 8, 2018
Statistics Practice paper
I will attach this practice task to the google classroom for you to complete this Monday.This is here in case you have any issues using the attachment, copy and paste this information into another document.
Student instructions sheet
Introduction
This activity requires you to undertake a statistical investigation using a randomly selected sample from a list of countries of the world. First, you will pose two investigative questions. Then you will analyse the sample and form a conclusion for one of your questions.
This activity is to be completed independently.
You will be assessed on the quality of your discussion and reasoning and how well you link this to the context.
Task
Carry out a statistical investigation on the The World at a Glance sample using the statistical enquiry cycle (Problem, Plan, Data, Analysis, Conclusion).
Problem
Pose two investigative questions about countries that can be explored using The World at a Glance dataset. See Student Resource A for a description of the variables.
Your investigative questions must be comparison questions. A suitable comparison investigative question is one that reflects the population, has a clear variable to investigate, compares the values of a continuous variable across different categories, and can be answered with the data.
For each question, state the variable you are investigating and the groups you are comparing.
Now choose one of your two questions for investigation using the data found in Student Resource B.
Plan and data
Because the 60 countries in Student Resource B were selected using a random sampling technique, the sample can be considered representative of all the countries in the database.
Analysis
Draw at least two appropriate graphs that show different features of the data in relation to your investigative question.
Give appropriate summary statistics.
Describe features of the distributions comparatively (for example, shape, middle 50%, shift, overlap, spread, unusual or interesting features).
Conclusion
Write a conclusion summarising your findings. The conclusion needs to include an informal inference in response to your investigative question and to be supported with relevant evidence.
Student Resource A: The World at a Glance variables
The table below shows the values for the six variables included in the dataset in Student Resource B.
The variables in the dataset and explanations of how the variables were collected are:
Variable
|
Explanation
|
Country
|
Randomly selected countries
|
Climate
|
Tropical countries are hot and around the equator, temperate countries are colder and lie between the tropics and the poles (tropical, temperate)
|
Level of development
|
High means developed countries and low means developing countries
|
TV Sets
|
The number of households that have television (per 1000 people)
|
Literacy rate (%)
|
The percent of the population in that country that can read
|
Internet use (%)
|
The percent of the population in that country that use the internet
|
This data has been collated using the most recently available information, and can be assumed to describe countries during the year 2013.
Student Resource B: The World at a Glance dataset
See Student Resource A for a list of variables.
* = data unavailable
| ||||||
Country
|
Climate
|
Level of Development
|
TV sets
|
Literacy rate (%)
|
Internet Use (%)
| |
1
|
Argentina
|
Tropical
|
High
|
223
|
98
|
56
|
2
|
Austria
|
Temperate
|
High
|
533
|
98
|
81
|
3
|
Bangladesh
|
Tropical
|
Low
|
6
|
58
|
6
|
4
|
Bolivia
|
Tropical
|
Low
|
115
|
91
|
43
|
5
|
Brazil
|
Tropical
|
High
|
219
|
90
|
50
|
6
|
Canada
|
Temperate
|
High
|
717
|
99
|
87
|
7
|
Chile
|
Temperate
|
High
|
212
|
99
|
61
|
8
|
China
|
Temperate
|
Low
|
325
|
95
|
42
|
9
|
Colombia
|
Tropical
|
High
|
116
|
94
|
49
|
10
|
Denmark
|
Temperate
|
High
|
590
|
99
|
93
|
11
|
Ecuador
|
Tropical
|
High
|
132
|
93
|
35
|
12
|
Egypt
|
Tropical
|
Low
|
121
|
74
|
44
|
13
|
Ethiopia
|
Tropical
|
Low
|
503
|
39
|
2
|
14
|
France
|
Temperate
|
High
|
598
|
99
|
83
|
15
|
Georgia
|
Temperate
|
High
|
526
|
100
|
46
|
16
|
Germany
|
Temperate
|
High
|
567
|
99
|
84
|
17
|
Ghana
|
Tropical
|
Low
|
93
|
72
|
17
|
18
|
Hong Kong
|
Tropical
|
High
|
284
|
94
|
73
|
19
|
India
|
Tropical
|
Low
|
65
|
74
|
13
|
20
|
Indonesia
|
Tropical
|
Low
|
69
|
90
|
15
|
21
|
Iran
|
Tropical
|
High
|
76
|
85
|
26
|
22
|
Iraq
|
Tropical
|
Low
|
76
|
78
|
7
|
23
|
Italy
|
Temperate
|
High
|
533
|
99
|
58
|
24
|
Jamaica
|
Tropical
|
High
|
182
|
88
|
47
|
25
|
Japan
|
Temperate
|
High
|
686
|
99
|
79
|
26
|
Kenya
|
Tropical
|
Low
|
25
|
87
|
32
|
27
|
Korea, North
|
Tropical
|
Low
|
56
|
99
|
*
|
28
|
Korea, South
|
Tropical
|
High
|
346
|
98
|
84
|
29
|
Kyrgyzstan
|
Temperate
|
Low
|
44
|
99
|
22
|
30
|
Lithuania
|
Temperate
|
High
|
475
|
100
|
68
|
31
|
Mali
|
Tropical
|
Low
|
4
|
28
|
2
|
32
|
Mexico
|
Tropical
|
High
|
273
|
93
|
38
|
33
|
Mauritania
|
Tropical
|
Low
|
26
|
58
|
5
|
34
|
Morocco
|
Tropical
|
Low
|
114
|
67
|
55
|
35
|
Myanmar (Burma)
|
Tropical
|
Low
|
592
|
93
|
1
|
36
|
Netherlands
|
Temperate
|
High
|
519
|
99
|
93
|
37
|
New Zealand
|
Temperate
|
High
|
509
|
99
|
90
|
38
|
Pakistan
|
Temperate
|
Low
|
24
|
57
|
10
|
39
|
Panama
|
Tropical
|
High
|
183
|
92
|
45
|
40
|
Peru
|
Tropical
|
High
|
124
|
93
|
38
|
41
|
Philippines
|
Tropical
|
Low
|
52
|
93
|
36
|
42
|
Poland
|
Temperate
|
High
|
338
|
100
|
65
|
43
|
Portugal
|
Temperate
|
High
|
328
|
95
|
64
|
44
|
Romania
|
Temperate
|
High
|
6
|
98
|
50
|
45
|
Russia
|
Temperate
|
High
|
411
|
100
|
53
|
46
|
Samoa
|
Tropical
|
Low
|
64
|
100
|
13
|
47
|
Singapore
|
Tropical
|
High
|
351
|
93
|
74
|
48
|
South Africa
|
Temperate
|
Low
|
127
|
93
|
41
|
49
|
Spain
|
Temperate
|
High
|
409
|
98
|
72
|
50
|
Sudan
|
Tropical
|
Low
|
77
|
71
|
21
|
51
|
Taiwan
|
Tropical
|
High
|
3
|
98
|
76
|
52
|
Tanzania
|
Tropical
|
Low
|
3
|
69
|
13
|
53
|
Thailand
|
Tropical
|
Low
|
255
|
93
|
27
|
54
|
Turkey
|
Temperate
|
High
|
326
|
95
|
45
|
55
|
Ukraine
|
Temperate
|
High
|
3
|
100
|
34
|
56
|
United Kingdom
|
Temperate
|
High
|
522
|
99
|
87
|
57
|
United States
|
Temperate
|
High
|
803
|
99
|
81
|
58
|
Venezuela
|
Tropical
|
High
|
179
|
93
|
44
|
59
|
Vietnam
|
Tropical
|
Low
|
47
|
94
|
40
|
60
|
Zambia
|
Tropical
|
Low
|
24
|
81
|
14
|
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