Course Content
Matrices and Transformations
This course introduces students to the fundamental concepts of matrices and their role in transformations. It provides a comprehensive understanding of matrix operations, determinants, and inverses, as well as how matrices are applied to various geometric transformations on the Cartesian plane. Specific Objectives By the end of the topic the learner should be able to: (a) Relate image and object under a given transformation on the Cartesian Plane; (b) Determine the matrix of a transformation; (c) Perform successive transformations; (d) Determine and identify a single matrix for successive transformation; (e) Relate identity matrix and transformation; (f) Determine the inverse of a transformation; (g) Establish and use the relationship between area scale factor and determinant of a matrix; (h) Determine shear and stretch transformations; (i) Define and distinguish isometric and non-isometric transformation; (j) Apply transformation to real life situations. Content (a) Transformation on the Cartesian plane (b) Identification of transformation matrix (c) Successive transformations (d) Single matrix of transformation for successive transformations (e) Identity matrix and transformation (f) Inverse of a transformations (g) Area scale factor and determinant of a matrix (h) Shear and stretch (include their matrices) (i) Isometric and non-isometric transformations (j) Application of transformation to real life situations.
0/2
Statistics II
This course introduces students to the fundamental concepts of statistics, focusing on measures of central tendency , cumulative frequency tables and ogives, and measures of dispersion. By the end of this module, students will have a strong understanding of how to organize, analyze, and interpret data using various statistical methods. Specific Objectives By the end of the topic the learner should be able to: (a) State the measures of central t e n d e n c y; (b) Calculate the mean using the assumed mean method; (c) Make cumulative frequency table, (d) Estimate the median and the quartiles b y - Calculation and - Using ogive; (e) Define and calculate the measures of dispersion: range, quartiles,interquartile range, quartile deviation, variance and standard deviation (f) Interpret measures of dispersion Content (a) Mean from assumed mean: (b) Cumulative frequency table (c) Ogive (d) Meadian (e) Quartiles (f) Range (g) Interquartile range (h) Quartile deviation (i) Variance (j) Standard deviation These statistical measures are called measures of central tendency and they are mean, mode and median
0/2
THREE-DIMENSIONAL GEOMETRY
Specific Objectives By the end of the topic the learner should be able to: (a) State the geometric properties of common solids; (b) Identify projection of a line onto a plane; (c) Identify skew lines; (d) Calculate the length between two points in three dimensional geometry; (e) Identify and calculate the angle between (i) Two lines; (ii) A line and a plane; (ii) Two planes. Content (a) Geometrical properties of common solids (b) Skew lines and projection of a line onto a plane (c) Length of a line in 3-dimensional geometry (d) The angle between i) A line and a line ii) A line a plane iii) A plane and a plane iv) Angles between skewlines.
0/1
Probability II
This course delves into advanced probability concepts, focusing on both theoretical and experimental probability. It introduces probability spaces, combined events, and probability laws while incorporating visual tools like tree diagrams for better understanding. Key Topics Covered: 1. Probability: - The measure of how likely an event is to occur. - Expressed as a fraction, decimal, or percentage. - Example: Rolling a die and getting a 3 (P = 1/6). 2. Experimental Probability: - Probability based on actual experiments or observations. - Example: Flipping a coin 100 times and recording how many heads appear. 3. Range of Probability Measure: - Probability values are always between 0 (impossible event) and 1 (certain event). 4. Probability Space: - A set of all possible outcomes of an experiment. - Includes: - Sample space (S): All possible outcomes. - Events (E): A subset of the sample space. - Example: Rolling a six-sided die β†’ Sample space: {1, 2, 3, 4, 5, 6}. 5. Theoretical Probability: - Probability determined using logic rather than experiments. - Example: The probability of drawing a red card from a standard deck is 26/52 = 1/2. 6. Discrete and Continuous Probability: - Discrete Probability: Deals with countable outcomes (e.g., rolling a die). - Continuous Probability: Deals with uncountable outcomes over an interval (e.g., height of students). 7. Combined Events: - Mutually Exclusive Events: Events that cannot happen at the same time. - Example: Getting heads and tails in a single coin toss. - Independent Events: Events where one does not affect the probability of the other. - Example: Rolling two dice. 8. Laws of Probability: - Addition Law: Used for mutually exclusive events. - Multiplication Law: Used for independent events. 9. Tree Diagrams: - A visual representation of probabilities in multi-step experiments. - Used to calculate the probability of sequences of events. - Example: Finding the probability of getting two heads in a row when flipping a coin. Learning Outcomes: By the end of this course, students will be able to: - Differentiate between experimental and theoretical probability. - Understand probability spaces and how to define sample spaces. - Apply probability laws to mutually exclusive and independent events. - Construct and analyze tree diagrams for multi-stage events. - Use probability in real-world applications like statistics, risk assessment, and decision-making. Why This Course Matters: Probability is essential in daily decision-making, finance, health sciences, and artificial intelligence. Mastering these concepts enhances logical reasoning and prepares students for advanced mathematical studies and KCSE exams.
0/1
TRIGONOMETRY III
This topic explores trigonometric ratios, identities, graphing, and solving trigonometric equations. Students will learn how to analyze trigonometric functions and their key properties, such as amplitude, period, and phase angle. Key Learning Objectives By the end of this topic, students will be able to: 1. Recall and define trigonometric ratios (sine, cosine, and tangent). 2. Derive and apply the fundamental identity: sin2x+cos2x = 1 3. Plot and interpret graphs of trigonometric functions. 4. Solve simple trigonometric equations both analytically and graphically. 5. Determine amplitude, period, wavelength, and phase angle from trigonometric graphs. Content Overview (a) Trigonometric ratios (b) Deriving the relation sin2x+cos2x =1 (c) Graphs of trigonometric functions of the form y = sin x y = cos x, y = tan x y = a sin x, y = a cos x, y = a tan x y = a sin bx, y = a cos bx y = a tan bx y = a sin(bx Β± 9) y = a cos(bx Β± 9) y = a tan(bx Β± 9) (d) Simple trigonometric equation (e) Amplitude, period, wavelength and phase angle of trigonometric functions. Why This Topic Matters Trigonometry is fundamental in physics, engineering, and real-world applications such as sound waves, light waves, and mechanical vibrations. Understanding trigonometric functions and their properties is essential for solving complex problems in science and technology.
0/2
Vectors II
Alright, let’s break down Vectors II like it’s your ultimate cheat sheet for conquering 2D and 3D space. We’re diving into the nitty-gritty of representing vectors, doing the math with them, and applying these skills to geometry like a boss.
0/1
Form 4 Mathematics Online Tuition
About Lesson

Mean using working (Assumed) Mean

Assumed mean is a method of calculating the arithmetic men and standard deviation of a data set. It simplifies calculation.

Example

The masses to the nearest kilogram of 40 students in the form 3 class were measured and recorded in the table below. Calculate the mean mass

Β 

Mass (kg) 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Number of Employees 2 0 1 2 3 2 5 6 7 5 3 2 1 1

Β 

Solution
We are using assumed mean of 53

Here is the data you provided in a table format:

Mass (x) kg t = x – 53 f (Frequency) ft (f * t)
47 -6 0 0
48 -5 1 -5
49 -4 2 -8
50 -3 3 -9
51 -2 2 -4

52

-1 5 -5
53 0 6 0
54 1 7 7

55

Β 

2 14 28
56 3 5 15
57 4 3 12
58 5 2 10
59 6 1 6
60 7 1 7
Β  Β  Ξ£f = 40 Ξ£ft = 40

Β 

Mean of t = βˆ‘ 𝑓 𝑑
βˆ‘π‘“ = 40
40 = 1

Mean of x = 53 + mean of t
= 53 + 1
= 54

Β 

Mean of grouped data
The masses to the nearest gram of 100 eggs were as follows

Here is the data you provided in a table format:

Marks Range Frequency
100 – 103 1
104 – 107 15
108 – 111 42
112 – 115 31
116 – 119 8
120 – 123 3

Find the mean mass

Β 

Solution
Let use a working mean of 109.5.

Class Range Mid-point (x) t = x – 109.5 Frequency (f) ft = f * t
100 – 103 101.5 -8 1 -8
104 – 107 105.5 -4 15 -60
108 – 111 109.5 0 42 0
112 – 115 113.5 4 31 124
116 – 119 117.5 8 8 64
120 – 123 121.5 12 3 36
Β  Β  Β  Ξ£f = 100 Ξ£ft = 156

Β 

Mean of t = 156
100 = 1.56

Therefore,mean of x = 109.5 + mean of t
= 109.5 + 1.56
= 111.06 g

Β 

To get the mean of a grouped data easily, we divide each figure by the class width after substracting the assumed mean.Inorder to obtain the mean of the original data from the mean of the new set of data, we will have to reverse the steps in the following order;

  • Β Multiply the mean by the class width and then add the working mean.

Example
The example above to be used to demonstrate the steps

Class Range Mid-point (x) t = x – 109.5 Frequency (f) ft = f * t
100 – 103 101.5 -2 1 -2
104 – 107 105.5 -1 15 -15
108 – 111 109.5 0 42 0
112 – 115 113.5 1 31 31
116 – 119 117.5 2 8 16
120 – 123 121.5 3 3 9
Β  Β  Β  Ξ£f = 100 Ξ£ft = 39

Β 

𝑑̅ = βˆ‘π‘“π‘‘
βˆ‘π‘“ = 39
100
= 0.39

Therefore π‘₯Μ… = 0.39 x 4 + 109.5
= 1.56 + 109.5
= 111.06 g

Β 

Quartiles, Deciles and Percentiles

A median divides a set of data into two equal part with equal number of items.
Quartiles divides a set of data into four equal parts.The lower quartile is the median of the bottom half.The upper quartile is the median of the top half and the middle coincides with the median of the whole set of data.
Deciles divides a set of data into ten equal parts.Percentiles divides a set of data into hundred equal parts.

Note;
For percentiles deciles and quartiles the data is arranged in order of size.
Example

Height (cm) Frequency
145 – 149 2
150 – 154 5
155 – 159 16
160 – 164 9
165 – 169 5
170 – 174 2
175 – 179 1

Β 

Calculate the ;
a.) Median height
b.) I.)Lower quartile
Β Β Β Β  ii) Upper quartile
c.) 80th percentile

Β 

Solution

I. There are 40 students. Therefore, the median height is the average of the heights of the 20th and 21st students.

Β 

Class Range Frequency Cumulative Frequency
145 – 149 2 2
150 – 154 5 7
155 – 159 16 23
160 – 164 9 32
165 – 169 5 37
170 – 174 2 39
175 – 179 1 40

Both the 20th and 21st students falls in the 155 -159 class. This class is called the median class. Using the formula m = L + (𝑛
2βˆ’πΆ)𝑖
𝑓

(n divided by 2 minus C multiplied by i , divided by f)

Where L is the lower class limit of the median class
N is the total frequency
C is the cumulative frequency above the median class
I is the class interval
F is the frequency of the median class

Therefore;

Height of the 20th student = 154.5 + 13
16 π‘₯ 5
= 154.5 + 4.0625
=158.5625

Height of the 21st = 154.5 + 14
16 π‘₯ 5
= 154.5 + 4.375
=158.875

Therefore median height = 158.5625+158.875
2
= 158.7 cm

b.) (I ) lower quartile 𝑄1= L + (𝑛
4βˆ’πΆ)𝑖
𝑓

The 10th student fall in the in 155 – 159 class

Β 

𝑄1= 154.5 + (40
4 βˆ’7)5
16
= 154.5 + 0.9375
= 155.4375

(ii) Upper quartile 𝑄3= L + (3
4 𝑛 βˆ’πΆ)𝑖
𝑓

The 10th student fall in the in 155 – 159 class
𝑄3= 159.5 + (3
4 π‘₯ 40 βˆ’23)5
9
= 159.5 + 3.888
= 163.3889

Β 

Note;
The median corresponds to the middle quartile 𝑄2 or the 50th percentile

c.) 80
100 π‘₯ 40 = 32 the 32nd student falls in the 160 -164 class
π‘‡β„Žπ‘’ 80π‘‘β„Ž π‘π‘’π‘Ÿπ‘π‘’π‘›π‘‘π‘–π‘™π‘’= L + ( 80
100 𝑛 βˆ’πΆ)𝑖
𝑓
= 159.5 + (32 βˆ’23)5
9
= 159.5 + 5
= 164.5

Β 

Example
Determine the upper quartile and the lower quartile for the following set of numbers
5, 10 ,6 ,5 ,8, 7 ,3 ,2 ,7 , 8 ,9

Solution

Arranging in ascending order
2, 3, 5,5,6, 7,7,8,8,9,10

The median is 7

The lower quartile is the median of the first half, which is 5.

The upper quartile is the median of the second half, which is 8.

Median from cumulative frequency curve

Graph for cumulative frequency is called an ogive. We plot a graph of cumulative frequency against the upper class limit.

Β 

Measure of Dispersion

Range
The difference between the highest value and the lowest value

Disadvantage
It depends only on the two extreme values

Interquartile range
The difference between the lower and upper quartiles. It includes the middle 50% of the values

Semi quartile range
The difference between the lower quartile and upper quartile divided by 2.It is also called the quartile
deviation.

Β 

Mean Absolute Deviation

If we find the difference of each number from the mean and find their mean , we get the mean Absolute deviation.

Variance
The mean of the square of the square of the deviations from the mean is called is called variance or mean deviation.

Β 

Example

Β 

Deviation from Mean (d) Frequency (fi) fi * d
+1 1 +1
-1 1 -1
+6 36 +216
-4 16 -64
-2 4 -8
-11 121 -1331
+1 1 +1
+10 100 +1000

Sum 𝑑2 = 1 + 1 + 36 + 16 + 4 + 121 + 1 + 100 = 280
Variance = βˆ‘π‘‘2
𝑁 = 280
8 = 35

The square root of the variance is called the standard deviation.It is also called root mean square deviation. For the above example its standard deviation =√35 = 5.9

Example
The following table shows the number of children per family in a housing estate

Β 

Number of Children Number of Families
0 1
1 5
2 11
3 27
4 10
5 4
6 Β 

Calculate
a.) The mean number of children per family
b.) The standard deviation

Β 

Solution

Number of Children (x) Number of Families (f) fβ‹…x Deviation = x – m Β  f
0 1 0 -3 9 9
1 5 5 -2 4 20
2 11 22 -1 1 11
3 27 81 0 0 0
4 10 40 1 1 10
5 4 20 2 4 16
6 2 12 3 9 18
Β  Ξ£f = 60 Β  Β  Β  Ξ£f= -40

Β 

a.) Mean = 180
Β Β Β Β  60 = 3 π‘β„Žπ‘–π‘™π‘‘π‘’π‘›
b.) Variance = βˆ‘π‘“π‘‘2
βˆ‘π‘“
= 84
60
= 1.4
π‘ π‘‘π‘Žπ‘›π‘‘π‘Žπ‘Ÿπ‘‘ π‘‘π‘’π‘£π‘–π‘Žπ‘‘π‘–π‘œπ‘› = √1.4 = 1.183

Β 

Example
The table below shows the distribution of marks of 40 candidates in a test

Marks Range Frequency (f)
1 – 10 2
11 – 20 2
21 – 30 3
31 – 40 9
41 – 50 12
51 – 60 5
61 – 70 2
71 – 80 3
81 – 90 1
91 – 100 1

Calculate the mean and standard deviation.

Β 

Β 

Marks Midpoint (x) Frequency (f) fx fx d=x-m f
1 – 10 5.5 2 11.0 -39.5 1560.25 3120.5
11 – 20 15.5 2 31.0 -29.5 870.25 1740.5
21 – 30 25.5 3 76.5 -19.5 380.25 1140.75
31 – 40 35.5 9 319.5 -9.5 90.25 812.25
41 – 50 45.5 12 546.0 0.5 0.25 3.00
51 – 60 55.5 5 277.5 10.5 110.25 551.25
61 – 70 65.5 2 131.0 20.5 420.25 840.5
71 – 80 75.5 3 226.5 30.5 930.25 2790.75
81 – 90 85.5 1 85.5 40.5 1640.25 1640.25
91 – 100 95.5 1 95.5 50.5 2550.25 2550.25

Β 

Totals:

  • Ξ£f = 40
  • Ξ£f * x = 1800
  • f = 15190

Β 

Mean π‘₯Μ… = βˆ‘π‘“π‘₯
βˆ‘π‘“ = 1800
40 = 45 π‘šπ‘Žπ‘Ÿπ‘˜π‘ 

Variance =βˆ‘π‘“π‘‘2
βˆ‘π‘“ = 15190
40 = 379.75
= 379.8

Standard deviation = √379.8
= 19.49

Β 

Note;
Adding or subtracting a constant to or from each number in a set of data does not alter the value of the variance or standard deviation.

More formulas
The formula for getting the variance 𝑠2 π‘œπ‘“ π‘Ž π‘£π‘Žπ‘Ÿπ‘–π‘Žπ‘›π‘π‘’ π‘₯ 𝑖𝑠
𝑑2 = βˆ‘(π‘₯ βˆ’ π‘₯Μ… )2𝑓
βˆ‘π‘“
= βˆ‘π‘“π‘₯2
βˆ‘π‘“ βˆ’ (βˆ‘π‘“π‘₯
βˆ‘π‘“ )2

Example
The table below shows the length in centimeter of 80 plants of a particular species of tomato

Length Range Frequency (f)
152 – 156 12
157 – 161 14
162 – 166 24
167 – 171 15
172 – 176 8
177 – 181 7

Calculate the mean and the standard deviation

Β 

Solution
Let A = 169

Length Range Mid-point (x) x – 169 t = x – 169 f ft ftΒ²
152 – 156 154 -15 -3 12 -36 108
157 – 161 159 -10 -2 14 -28 56
162 – 166 164 -5 -1 24 -24 24
167 – 171 169 0 0 15 0 0
172 – 176 174 5 1 8 8 8
177 – 181 179 10 2 7 14 28

Β 

Summaries:

  • Ξ£f = 80
  • Ξ£ft = -66
  • Ξ£ftΒ² = 224

Β 

𝑑̅ =βˆ’66
80 = βˆ’0.825

Therefore π‘₯Μ… = βˆ’0.825 π‘₯ 5 + 169
= -4.125 + 169
= 164.875 ( to 4 s.f)
Variance of t =βˆ‘π‘“π‘‘2
βˆ‘π‘“ βˆ’ 𝑑2
= 224
80 βˆ’ ( βˆ’0.825)2
= 2.8 – 0.6806
= 2.119

Therefore , variance of x = 2.119 x 52
= 52.975
= 52.98 ( 4 s.f)
Standard deviation of x = √52.98
= 7.279
= 7.28 (to 2 d.p)

End of topic

Did you understand everything?
If not ask a teacher, friends or anybody and make sure you understand!