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Assignment JIM2

The document provides air pollution concentration data collected in Penang over a time period. It instructs the reader to [1] analyze the data using scatter plots, correlation analysis, simple and multiple linear regression, and the coefficient of determination. It also instructs the reader to [2] collect nutritional information from at least 30 food items in the same category, determine the relationships between variables, the variance in calories explained by fat, and write a regression equation relating calories to fat. The reader is asked to analyze the provided data set and complete two assignments analyzing nutritional data and relationships between variables.

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83 views3 pages

Assignment JIM2

The document provides air pollution concentration data collected in Penang over a time period. It instructs the reader to [1] analyze the data using scatter plots, correlation analysis, simple and multiple linear regression, and the coefficient of determination. It also instructs the reader to [2] collect nutritional information from at least 30 food items in the same category, determine the relationships between variables, the variance in calories explained by fat, and write a regression equation relating calories to fat. The reader is asked to analyze the provided data set and complete two assignments analyzing nutritional data and relationships between variables.

Uploaded by

danieloon
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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JIM 212 : STATISTICAL METHODS

ASSIGNMENT
2022/2023 ACADEMIC SESSION

1. The following data is a reading of the concentration of air pollutants in Penang


over a specific time period. Apply all your knowledge to analyse and interpret
your findings from the provided data sets.

Dependent
Variable
Particulate Carbon
Matter (PM10) Ozone (O3) Relative
Monoxide (CO)
in ppm Humidity in %
in g / m3 in ppm

22 0.35 0.030 74.37


25 0.39 0.034 73.29
22 0.43 0.023 82.08
22 0.43 0.021 82.27
20 0.51 0.020 82.81
27 0.62 0.018 85.56
19 0.51 0.020 84.01
18 0.48 0.014 83.59
16 0.47 0.015 85.23
16 0.50 0.011 80.05
15 0.61 0.016 88.33
16 0.61 0.022 90.01
26 0.66 0.027 88.30
24 0.58 0.028 86.81
29 0.57 0.028 77.91
24 0.53 0.025 74.62
22 0.50 0.026 76.58
31 0.56 0.031 76.02
25 0.55 0.023 80.22
19 0.53 0.019 83.78
15 0.49 0.015 84.01
20 0.53 0.013 83.08
19 0.48 0.013 83.76
14 0.40 0.012 83.55
20 0.39 0.012 80.32
19 0.35 0.013 76.88
19 0.38 0.013 78.67
19 0.39 0.014 81.41
20 0.52 0.014 87.33
22 0.48 0.019 82.05
20 0.39 0.017 77.35
17 0.42 0.022 74.30
20 0.49 0.025 75.77
22 0.48 0.027 71.44
28 0.51 0.040 68.53
28 0.53 0.044 67.34
31 0.49 0.044 66.56
29 0.47 0.045 66.35
29 0.47 0.046 68.22
41 0.57 0.055 68.30

Notes
The complete analysis must include:
 scatter plots
 correlation analysis
 simple linear regression analysis
 coefficient of determination
 multiple regression analysis

2. Go to any supermarket and look for the dry food or canned food section. Take
images of the nutritional information for at least 30 different brands of food in the
same category (for example, canned food) and compile your data (Total calories,
fat, protein, carbohydrate, etc).
a) Determine whether there is a linear relationship between any two
variables.
b) Determine how much variance in total calories is explained by fat grams.
c) Write a regression equation to determine how many total calories you
would expect in an item, knowing its fat grams.
(Note : Please provide a clear picture of the nutritional data that you collect.)

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