Complete Analysis of the Given Data

Oct 13, 2025 by pinkandtrash.com 139 views

Complete Analysis of the Given Data

Introduction

In this article, we will be analyzing a given dataset and completing various tasks related to it. The dataset consists of two variables, x and y, with five data points each. We will be using this data to create a scatter diagram, calculate the mean and median of both variables, find the correlation coefficient, and more.

(a) Scatter Diagram

A scatter diagram, also known as a scatter plot, is a graphical representation of the relationship between two variables. It is a useful tool for visualizing the relationship between the variables and identifying any patterns or trends.

To create a scatter diagram by hand, we need to plot the data points on a coordinate plane. The x-axis represents the values of x, and the y-axis represents the values of y. We will plot each data point on the plane, using a dot to represent each point.

Here is the scatter diagram for the given data:

x	y
20	79
30	75
40	71
50	63
60	50

Scatter Diagram:

Plot the data points on a coordinate plane.
Use a dot to represent each data point.
Label the x-axis and y-axis with the corresponding variable names.

The scatter diagram shows a negative trend between the variables x and y. As the value of x increases, the value of y decreases.

(b) Mean of x and y

The mean of a set of data is the average value of the data points. To calculate the mean, we need to add up all the data points and divide by the number of data points.

Mean of x:

Add up all the values of x: 20 + 30 + 40 + 50 + 60 = 200
Divide the sum by the number of data points: 200 ÷ 5 = 40

Mean of y:

Add up all the values of y: 79 + 75 + 71 + 63 + 50 = 338
Divide the sum by the number of data points: 338 ÷ 5 = 67.6

(c) Median of x and y

The median of a set of data is the middle value of the data points when they are arranged in order. If there are an even number of data points, the median is the average of the two middle values.

Median of x:

Arrange the values of x in order: 20, 30, 40, 50, 60
Since there are an odd number of data points, the median is the middle value: 40

Median of y:

Arrange the values of y in order: 50, 63, 71, 75, 79
Since there are an odd number of data points, the median is the middle value: 71

(d) Mode of x and y

The mode of a set of data is the value that appears most frequently. If there are multiple values that appear with the same frequency, the mode is not defined.

Mode of x:

Check if any value of x appears more than once: No
Since no value appears more than once, the mode is not defined.

Mode of y:

Check if any value of y appears more than once: No
Since no value appears more than once, the mode is not defined.

(e) Range of x and y

The range of a set of data is the difference between the largest and smallest values.

Range of x:

Find the largest value of x: 60
Find the smallest value of x: 20
Calculate the range: 60 - 20 = 40

Range of y:

Find the largest value of y: 79
Find the smallest value of y: 50
Calculate the range: 79 - 50 = 29

(f) Variance of x and y

The variance of a set of data is a measure of the spread of the data points. It is calculated as the average of the squared differences between each data point and the mean.

Variance of x:

Calculate the squared differences between each data point and the mean: (20-40)^2 + (30-40)^2 + (40-40)^2 + (50-40)^2 + (60-40)^2 = 400 + 100 + 0 + 100 + 400 = 1000
Divide the sum by the number of data points: 1000 ÷ 5 = 200

Variance of y:

Calculate the squared differences between each data point and the mean: (79-67.6)^2 + (75-67.6)^2 + (71-67.6)^2 + (63-67.6)^2 + (50-67.6)^2 = 121.44 + 56.16 + 16.96 + 21.04 + 189.76 = 405.36
Divide the sum by the number of data points: 405.36 ÷ 5 = 81.072

(g) Standard Deviation of x and y

The standard deviation of a set of data is the square root of the variance.

Standard Deviation of x:

Calculate the square root of the variance: √200 ≈ 14.14

Standard Deviation of y:

Calculate the square root of the variance: √81.072 ≈ 9.01

(h) Correlation Coefficient

The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 indicates a perfect negative linear relationship, 1 indicates a perfect positive linear relationship, and 0 indicates no linear relationship.

To calculate the correlation coefficient, we need to use the following formula:

r = Σ[(xi - x̄)(yi - ȳ)] / (√Σ(xi - x̄)^2 * √Σ(yi - ȳ)^2)

where xi and yi are the individual data points, x̄ and ȳ are the means of the data points, and Σ denotes the sum.

Correlation Coefficient:

Calculate the numerator: Σ[(xi - x̄)(yi - ȳ)] = (20-40)(79-67.6) + (30-40)(75-67.6) + (40-40)(71-67.6) + (50-40)(63-67.6) + (60-40)(50-67.6) = -21.6 + -6.4 + 0 + -4.6 + -17.6 = -50.2
Calculate the denominator: (√Σ(xi - x̄)^2 * √Σ(yi - ȳ)^2) = (√200 * √81.072) ≈ (√200 * 9.01) ≈ (14.14 * 9.01) ≈ 127.51
Calculate the correlation coefficient: r = -50.2 / 127.51 ≈ -0.394

The correlation coefficient is approximately -0.394, indicating a weak negative linear relationship between the variables x and y.
Frequently Asked Questions (FAQs) about the Given Data

Q: What is the purpose of a scatter diagram?

A: A scatter diagram is a graphical representation of the relationship between two variables. It is a useful tool for visualizing the relationship between the variables and identifying any patterns or trends.

Q: How do I calculate the mean of a set of data?

A: To calculate the mean, you need to add up all the data points and divide by the number of data points.

Q: What is the difference between the mean and the median?

A: The mean is the average value of the data points, while the median is the middle value of the data points when they are arranged in order.

Q: How do I calculate the variance of a set of data?

A: To calculate the variance, you need to calculate the squared differences between each data point and the mean, and then divide the sum by the number of data points.

Q: What is the standard deviation of a set of data?

A: The standard deviation is the square root of the variance.

Q: How do I calculate the correlation coefficient?

A: To calculate the correlation coefficient, you need to use the following formula:

r = Σ[(xi - x̄)(yi - ȳ)] / (√Σ(xi - x̄)^2 * √Σ(yi - ȳ)^2)

where xi and yi are the individual data points, x̄ and ȳ are the means of the data points, and Σ denotes the sum.

Q: What does the correlation coefficient tell us about the relationship between the variables?

A: The correlation coefficient tells us the strength and direction of the linear relationship between the variables. A correlation coefficient of -1 indicates a perfect negative linear relationship, a correlation coefficient of 1 indicates a perfect positive linear relationship, and a correlation coefficient of 0 indicates no linear relationship.

Q: What is the range of a set of data?

A: The range is the difference between the largest and smallest values.

Q: How do I calculate the mode of a set of data?

A: To calculate the mode, you need to check if any value appears more than once. If a value appears more than once, it is the mode.

Q: What is the difference between the mode and the median?

A: The mode is the value that appears most frequently, while the median is the middle value of the data points when they are arranged in order.

Q: Can a set of data have more than one mode?

A: Yes, a set of data can have more than one mode if multiple values appear with the same frequency.

Q: What is the significance of the standard deviation in a set of data?

A: The standard deviation is a measure of the spread of the data points. A small standard deviation indicates that the data points are close together, while a large standard deviation indicates that the data points are far apart.

Q: How do I interpret the correlation coefficient in the context of the given data?

A: In the context of the given data, the correlation coefficient of -0.394 indicates a weak negative linear relationship between the variables x and y. This means that as the value of x increases, the value of y decreases, but the relationship is not very strong.

Q: What are some common applications of the correlation coefficient?

A: The correlation coefficient is commonly used in statistics and data analysis to measure the strength and direction of the linear relationship between two variables. It is used in a wide range of fields, including economics, finance, sociology, and medicine.

Q: Can the correlation coefficient be used to predict the value of one variable based on the value of another variable?

A: Yes, the correlation coefficient can be used to predict the value of one variable based on the value of another variable, but only if the relationship between the variables is linear and strong. In the case of the given data, the correlation coefficient is weak, so it is not possible to make accurate predictions based on it.