Statistical Analysis of Some Greenhouse Gases and Air Quality Index Data - A Computer Project-Based Assignment for Teaching STA2023 Course*

Transcription

1 1 Statistical Analysis of Some Greenhouse Gases and Air Quality Index Data - A Computer Project-Based Assignment for Teaching STA2023 Course* Dr. M. Shakil, Ph.D., Professor Department of Mathematics, Liberal Arts & Sciences Miami Dade College, Hialeah Campus FL 33012, USA, mshakil@mdc.edu Abstract This paper deals with developing a lesson plan to show how the teaching of basic statistical methods, such as exploratory data analysis, statistical hypothesis, among others, using statistical software (Minitab and Excel), can help the students to gain the knowledge and insights from some air pollution data, such as the greenhouse gases emissions and air quality index data, affecting the health, environment and well-being of our communities. Such studies are very important in view of the facts that there is a great emphasis on health literacy and building healthy communities. It is hoped that by implementing the techniques discussed in this paper in preparing lesson plans will help us to develop in our students the quantitative analytic skills to evaluate and process numerical data, which is one of the Gen Ed Outcomes of Miami Dade College Mathematics Subject Classifications: 97C40, 97C70, 97D40, 97D50. Keywords: Air Pollution, Excel, Greenhouse Gases, Health, Lesson Plan, Minitab, Statistical Methods, Teaching. *A paper vis-à-vis a lesson plan prepared and presented to the Miami Dade College Earth Ethics Institute (EEI) during the EEI Workshop Health: Connecting People, Places, and Planet - Part II (EEI1015-2), Spring 2018, held from 01/29/2018 to o3/12/2018, at Miami Dade College, Hialeah Campus.

2 2 1. Introduction: In recent years, there has been a great emphasis on the health and environmental effects of air pollution, and well-being of our communities. These are the issues of emerging national and international importance and significance. It appears from literature that many people suffer from the lack of the knowledge and insights of various aspects of air pollution data affecting the health, environment and well-being of our communities. Such studies are very important in view of the facts that there is a great emphasis on health literacy and building healthy communities. Thus, motivated by the importance of building healthy communities, this project aims at studying and conducting statistical analysis of some aspects of air pollution data such as the greenhouse gases data emissions, affecting the health, environment and well-being of our communities. It should be pointed out that Statistics is one of the important sciences at present. In order to summarize any type of data, we cannot underestimate its role, uses and importance in the modern world. There is a great emphasis on statistical literacy and critical thinking in education these days. An introductory course in statistics such as Statistical Methods (STA 2023) at Miami Dade College can easily provide such avenues to our students. In any Statistical Methods (STA 2023) Class, the students are first taught the frequency distributions, statistical graphs, and basic descriptive statistics, that is, center, variation, distribution, and outliers, which are important tools and techniques for describing, exploring, summarizing, comparing data sets, and other aspects of exploratory data analysis (EDA). Due to the tremendous development of computers and other technological resources, the use of Statistical Software for data analysis, in a Statistical Methods (STA 2023) class, cannot be underemphasized and ignored. This computer-based project develops a lesson plan how to use air pollution data to teach Statistical Methods (STA2023) via Minitab and Excel software. For details on Minitab and Excel, the interested readers referred, for example, to McKenzie and Goldman (2005), Bluman (2013), and Triola (2010, 2014), among others. The organization of this paper is as follows. In Section 2, an overview of some basic concepts of statistics needed for our project is presented. In Section 3, an overview of some basic facts of air pollution, and its effects on health and environment are presented. In Section 4, uses of some statistical software, such as Minitab and Excel, are presented. Section 5 contains the applications of Minitab and Excel in the statistical analysis of some greenhouse gases data. The concluding remarks are given in Section An Overview of Some Basic Concepts of Statistics: This is a computer project on the statistical analysis of some air pollution data using Minitab and Statdisk. It is expected that the students have already learned about the following topics in the class, which, for the sake of completeness, are stated here. For details on these, the interested readers referred, for example, to Bluman (2013), and Triola (2010, 2014), among others. Exploratory Data Analysis (EDA): The five-number summary, namely, the minimum value, the 1st quartile, the median (i.e., the 2nd quartile or the 50th percentile), the 3rd quartile (i.e., the 75th percentile), and the maximum value are used to construct the boxplot.

3 3 Measures of central tendency (namely, mean, median, and mode) are used to indicate the typical value in a distribution. A comparison between the median and mean are used to determine the shape of distribution, while the mode measures the most frequently occurred data. Measures of dispersion or variation (namely, range, standard deviation, and variance) are used to determine the spread out of the data. Some statistical graphs, for example, histograms, can also be used to describe the shape of distribution 3. Uses of Minitab and Excel: In what follows, we will discuss some special features of Minitab and Statdisk Minitab and Some of its Special Features: In what follows, we will discuss some special features of Minitab and its use for constructing frequency distributions, histograms, descriptive statistics and exploratory data analysis (EDA). Minitab is one of the most important statistical analysis software. When Minitab is opened, there are two windows displayed first, as described below: Session Window: It is an area which displays the statistical results of the data analysis and can also be used to enter commands. Worksheet Window: It is a grid of rows and columns (similar to a spreadsheet) and is used to enter and manipulate the data. There are also other windows in Minitab as given below: Graph Window: When graphs are generated, each graph is opened in its own window. Report Window: There is also a report manager which is helpful in organizing the results in a report. Other Windows: Minitab has also other windows known as History and Project Manager. For details on these, the interested readers are referred to Minitab help Minitab Tools (or Menus): In general, various tools (called menus) of Minitab are described in the following Table 3.2.1, which, when Minitab is opened, are displayed in the menu bar on the top. For statistical data analysis, these menus can be used appropriately by following the instructions provided in Minitab help; also see McKenzie and Goldman (2005), Bluman (2013), and Triola (2010, 2014), among others.

4 4 Table File Menu Edit Menu Data Menu Calc Menu Stat Menu Open and save files; Print files; etc. Undo and redo actions; Cut, copy, and paste; etc. Undo and redo actions; Cut, copy, and paste; etc. Calculate statistics; Generate data from a distribution; etc. Regression and ANOVA; Control charts and quality tools; etc. Graph Menu Editor Menu Tools Menu Window Menu Scatterplots; Bar charts; etc. Graph, Data, and Session window editing; Modify active window; etc. Change Minitab defaults; Create and modify toolbars and menus; etc. Arrange windows; Select active window; etc. Help Menu Searchable Help; StatGuide; etc Some Special Features of Excel: Uses of Excel: This is an Excel project. It is expected that the students have already learned about the following topics in the class: Measures of central tendency (namely, mean, median, and mode) are used to indicate the typical value in a distribution. A comparison between the median and mean are used to determine the shape of distribution, while the mode measures the most frequently occurred data. Measures of dispersion or variation (namely, range, standard deviation, and variance) are used to determine the spread out of the data. Some statistical graphs, for example, histograms, can also be used to describe the shape of distribution The five-number summary, namely, the minimum value, the 1st quartile, the median (i.e., the 2nd quartile or the 50th percentile), the 3rd quartile (i.e., the 75th percentile), and the maximum value are used to construct the box-plot. In what follows, we will discuss some special features of Excel and its use for constructing frequency distributions, histograms, and descriptive statistics.

5 Some Special Features of Excel: These are described below. Statistical Analysis Tools: Microsoft Excel provides a set of data analysis tools-called the Analysis ToolPak-that you can use to save steps when you develop complex statistical or engineering analyses. You provide the data and parameters for each analysis; the tool uses the appropriate statistical or engineering macro functions and then displays the results in an output table. Some tools generate charts in addition to output tables. Related Worksheet Functions: Excel provides many other statistical, financial, and engineering worksheet functions. Some of the statistical functions are built-in and others become available when you install the Analysis ToolPak. Accessing the Data Analysis Tools: The Analysis ToolPak includes the tools described below. To access these tools, click Data Analysis on the Tools menu. If the Data Analysis command is not available, you need to load the Analysis ToolPak add-in program. Descriptive Statistics Analysis Tools: The Descriptive Statistics Analysis tool generates a report of univariate statistics for data in the input range, providing information about the central tendency and variability of your data. Histogram Analysis Tools: The Histogram analysis tool calculates individual and cumulative frequencies for a cell range of data and data bins. This tool generates data for the number of occurrences of a value in a data set How to Perform a Statistical Analysis: These are described below. On the Tools menu, click Data Analysis. If Data Analysis is not available, load the Analysis ToolPak as follows: On the Tools menu, click Add-Ins. In the Add-Ins available list, select the Analysis ToolPak box, and then click OK. If necessary, follow the instructions in the setup program. In the Data Analysis dialog box, click the name of the analysis tool you want to use, then click OK. In the dialog box for the tool you selected, set the analysis options you want.

6 6 4. An Overview of Some Basic Facts of Air Pollution, and Its Effects on Health and Environment: These are presented below in subsections 4.1 and Types of Air Pollution: According to Agency for Toxic Substances and Disease Registry (ASTDR), the following are major types of air pollution; please see for details: (I) Gaseous pollutants: The most common gaseous pollutants are carbon dioxide, carbon monoxide, hydrocarbons, nitrogen oxides, sulfur oxides and ozone, produced by the burning of fossil fuel, cigarette smoking, the use of certain construction materials, cleaning products, home furnishings, volcanoes, fires, and industry. The most commonly recognized type of air pollution is smog. Smog generally refers to a condition caused by the action of sunlight on exhaust gases from motor vehicles and factories. (II) Greenhouse Effect: A greenhouse gas is a gas that both absorbs and emits thermal radiation or heat, and when present in the atmosphere, causes a warming process called the greenhouse effect. It includes carbon dioxide, carbon monoxide, methane, nitrous oxide, ozone, water vapor (H2O), and chlorofluorocarbon (CFC). Many scientists believe that this is causing global warming. (III) Acid Rain: It is formed when moisture in the air interacts with nitrogen oxide and sulfur dioxide released by factories, power plants, and motor vehicles that burn coal or oil. This interaction of gases with water vapor forms sulfuric acid and nitric acids. Eventually these chemicals fall to earth as precipitation, or acid rain. (IV) Ozone: It is a form of oxygen found in the earth's upper atmosphere. It s believed that the damage to the ozone layer is primarily caused by the use of chloroflurocarbons (CFCs). The depletion of ozone is causing higher levels of sun's ultraviolet (UV) radiation on earth, endangering both plants and animals. (V) Particulate Matter or PM (PM10 and PM2.5): It is the general term used for a mixture of solid particles and liquid droplets found in the air. When PM is breathed in, it can irritate and damage the lungs causing breathing problems. (VI) Climatic Effects: These are caused by the wind patterns, clouds, rain, and temperature, because of which pollutants move away quickly from an area to another area and thus creating air pollution The Air Quality Index: The Air Quality Index (AQI) is a tool used by U.S. Environmental Protection Agency (EPA) and other agencies to provide the public with timely and easy-tounderstand information on local air quality and whether air pollution levels pose a health

7 7 concern. The AQI is focused on health effects that can happen within a few hours or days after breathing polluted air, as described in the following Tables and Table (Air Quality Index) Air Quality Index (AQI) Values When the AQI is in this range: Levels of Health Concern...air quality conditions are: Colors...as symbolized by this color. 0 to 50: Good Green 51 to 100: Moderate Yellow 101 to 150: Unhealthy for Sensitive Groups Orange 151 to 200: Unhealthy Red 201 to 300: Very unhealthy Purple 301 to 500: Hazardous Maroon Source: ATSDR s Web site at

8 8 Table (AQI Values) AQI Level Numerical Ozone PM2.5 Carbon Value Monoxide Good ppb µg/m ppm Moderate ppb µg/m ppm Unhealthy for ppb µg/m ppm Sensitive Groups Unhealthy ppb µg/m ppm Very Unhealthy ppb µg/m ppm Hazardous >300 >375 ppb >250.5 µg/m 3 >30.5 ppm Sources: Statistical Analysis of Greenhouse Effect Data: In this section, we present the statistical analysis of some greenhouse gases data using the software Minitab and Excel. The concentration of greenhouse gases in the earth s atmosphere, resulting in a gradual increase in temperatures at the earth s surface, is an important area of research. Emissions of greenhouse gases worldwide resulting from human activities are expected to contribute to future climate changes. As greenhouse and climate change are fundamental issues of environmental sustainability and building healthy communities, this project aims at studying and conducting some statistical analysis of the greenhouse gas emissions data (namely, carbon dioxide, methane, nitrous oxide, and fluorinated gases) from 2001 to 2015, as reported by the annual inventory of United States Environmental Protection Agency (EPA). For details on these, please visit EPA s website at the link: These greenhouse gas emissions data are provided in the following Table 5.1.

9 9 Table 5.1 (U.S. Greenhouse Gas Emissions by Gas, ) Year / Gas Carbon dioxide Methane Nitrous oxide Fluorinated gases Minitab and Excel are efficient and effective tools for analyzing data. As such, we shall describe the frequency histograms, basic descriptive statistics and exploratory data analysis (EDA), for statistical analysis of the above-sated greenhouse gas emissions data, using Minitab and Excel Descriptive Statistics and Histograms: In this subsection, we present the descriptive statistics and the above-sated greenhouse gas emissions data, namely, carbon dioxide, methane, nitrous oxide, and fluorinated gases. Table 5.2 Minitab Output of Descriptive Statistics of Greenhouse Gas Emissions Data, Total Variable Count N N* Mean StDev Minimum Q1 Median Q3 Carbon dioxide Methane Nitrous oxide Fluorinated gase Variable Maximum Carbon dioxide Methane Nitrous oxide

10 Frequency 10 Figure 5.1: Excel Output of Histograms of Greenhouse Gas Emissions Data, Histogram of Carbon dioxide, Methane, Nitrous oxide, Fluorinated gases Normal Carbon dioxide Methane Carbon dioxide Mean 5787 StDev N 15 Methane Mean StDev N Nitrous oxide Fluorinated gases Nitrous oxide Mean StDev N 15 Fluorinated gases Mean StDev N Figure 5.2: Minitab Output of Histograms of Greenhouse Gas Emissions Data,

11 Data Exploratory Data Analysis (EDA): The concept of exploratory data analysis (EDA) was developed by John Tukey; see, for example, Tukey (1977). The objective of exploratory data analysis is to analyze data in order to find out information about the data such as the center and the spread. In this subsection, we discuss exploratory data analysis (EDA) using Minitab. For example, we organize data using a stem and leaf plot. We compute the median which is the measure of central tendency, and also we compute the interquartile range which is the measure of variation. Further, in EDA, we represent the data graphically using a boxplot (sometimes called a box-and-whisker plot). These plots involve five specific values, called a five-number summary of the data set, as defined below: i. The lowest value of the data set, that is, minimum ii. Q 1, called the first quartile iii. Q 2, called the second quartile, or the median iv. Q 3, called the third quartile v. The highest value of the data set, that is, maximum The exploratory data analysis (EDA) of the greenhouse gas emissions data (namely, carbon dioxide, methane, nitrous oxide, and fluorinated gases), from 2001 to 2015, is reported below, using Minitab, in Figure 5.3 (Boxplots) and Table in 5.3, below. Minitab Output of Boxplots of Greenhouse Gas Emissions Data, Carbon dioxide Methane Nitrous oxide Fluorinated gases Figure 5.3: Minitab Output of Boxplots of Greenhouse Gas Emissions Data,

12 12 Stem-and-leaf of Carbon dioxide N = 15 Leaf Unit = (4) Table 5.3 Minitab Output of Stem-and-Leaf Display: Greenhouse Gas Emissions Data, Stem-and-leaf of Methane N = 15 Leaf Unit = (4) Stem-and-leaf of Nitrous oxide N = 15 Leaf Unit = (6) Stem-and-leaf of Fluorinated gases N = 15 Leaf Unit = (2) Total Variable Count N N* Mean StDev Minimum Q1 Median Q3 Carbon dioxide Methane Nitrous oxide Fluorinated gase Variable Maximum Carbon dioxide Methane Nitrous oxide From the above Figure 5.3 (Boxplots) and Table in 5.3, we can easily infer the five-number summary, that is, minimum value, Q 1, Q 2, Q 3, and maximum value of the greenhouse gas emissions data, that is, carbon dioxide, methane, nitrous oxide, and fluorinated gases respectively, as given above. We observe that the median for the distribution for carbon dioxide emissions data is higher than the median for the distributions for methane, nitrous oxide, and fluorinated gases emissions data. Also, we observe that the interquartile range (IQR) for the distribution for carbon dioxide emissions data is greater than the interquartile range (IQR) for the distributions for methane, nitrous oxide, and fluorinated gases emissions data. Thus there is more variation or spread for the distribution of carbon dioxide emissions data than the variation for the distribution of methane, nitrous oxide, and fluorinated gases emissions data.

13 Time Series Analysis: Using Excel, the time-series analysis of the greenhouse gas emissions data (namely, carbon dioxide, methane, nitrous oxide, and fluorinated gases), in the United States in the United States for the recent ten-year period, from 2001 to 2015, is reported in Figure 5.4, below. Figure 5.4: Excel Output of Time Series of Greenhouse Gas Emissions Data, It is apparent from the above time-series graph (Figure 5.4) that the amounts of the four greenhouse gas emissions, (namely, carbon dioxide, methane, nitrous oxide, and fluorinated gases), in the United States for the recent ten-year period, from 2001 to 2015, are decreasing Scatterplot Analysis: In order to explore the relationship of carbon dioxide emissions data with methane, nitrous oxide, and fluorinated gases emissions data respectively, we have provided the scatterplots, using Minitab, in the following Figures

14 Carbon dioxide Carbon dioxide Scatterplot of Carbon dioxide vs Nitrous oxide Nitrous oxide Figure 5.5: Minitab Output of Scatterplot of Carbon Dioxide vs Nitrous Oxide Emissions 6200 Scatterplot of Carbon dioxide vs Methane Methane Figure 5.6: Minitab Output of Scatterplot of Carbon Dioxide vs Methane Emissions

15 Scatterplot of Nitrous oxide vs Fluorinated gases 380 Nitrous oxide Fluorinated gases Figure 5.7: Minitab Output of Scatterplot of Carbon Dioxide vs Fluorinated Gas Emissions It is obvious from the above scatterplots (Figures ) that there is no relationship of carbon dioxide emissions data with methane, nitrous oxide, and fluorinated gases emissions data respectively. 6. Statistical Analysis of Air Quality Data: As air quality index values and concentration of pollutants are also fundamental issues of environmental sustainability and building healthy communities, this project aims at studying and conducting some statistical analysis of daily mean PM2.5 concentration and corresponding daily AQI value for the months of January, 2017 and December, 2017, collected at outdoor monitors across the Metropolitan Core Based Statistical Areas (CBSA), namely, Miami-Fort Lauderdale-West Palm Beach, FL, with CBSA CODE: 33100, and reported by the United States Environmental Protection Agency (EPA). For details on these, please visit EPA s website at the link: These data are provided in the following Table 6.1.

16 16 JAN2017 Daily Mean PM2.5 Concentration 1/1/2017-1/31/2017 Table 6.1 Outdoor Air Quality Data (January and December 2017) (Criteria Pollutant: PM2.5; Daily Air Quality Index Value) Metropolitan Core Based Statistical Areas (CBSA) NAME: Miami-Fort Lauderdale-West Palm Beach, FL; CBSA CODE: JAN2017 DAILY AQI VALUE 1/1/2017-1/31/2017 DEC2017 Daily Mean PM2.5 Concentration 12/1/ /31/2017 DEC2017 DAILY AQI VALUE 12/1/ /31/

17 Frequency 17 In what follows, using the data in Table 6.1, we have provided various statistical analysis in Tables , and Figures , such as the descriptive statistics, histograms, hypothesis testing, confidence interval estimates, box plots, time series plot, among others, from which we can easily draw some inferences about the daily mean PM2.5 concentration and corresponding daily AQI value for the months of January, 2017 and December, 2017, collected at outdoor monitors across the Metropolitan Core Based Statistical Areas (CBSA), namely, Miami-Fort Lauderdale-West Palm Beach, FL, with CBSA CODE: Table 6.2 Minitab Output of Descriptive Statistics: JAN-2017-AQIValue, DEC-2017-AQI-Value Variable N N* Mean StDev Variance CoefVar Minimum Q1 JAN-2017-AQIValu DEC-2017-AQI-Val Variable Median Q3 Maximum JAN-2017-AQIValu DEC-2017-AQI-Val Histogram (with Normal Curve) of JAN-2017-AQIValue 9 8 Mean StDev N JAN-2017-AQIValue Figure 6.1: Histogram (with Normal Curve) of JAN-2017-AQIValue

18 Frequency Histogram (with Normal Curve) of DEC-2017-AQI-Value Mean StDev N DEC-2017-AQI-Value Figure 6.2: Histogram (with Normal Curve) of DEC-2017-AQI-Value Table 6.3 Minitab Output of Two-Sample T-Test and CI: JAN-2017-AQIValue, DEC-2017-AQI-Value Two-sample T for JAN-2017-AQIValue vs DEC-2017-AQI-Value N Mean StDev SE Mean JAN-2017-AQIValu DEC-2017-AQI-Val Difference = mu (JAN-2017-AQIValue) - mu (DEC-2017-AQI-Value) Estimate for difference: % CI for difference: ( , ) T-Test of difference = 0 (vs not =): T-Value = P-Value = DF = 60 Both use Pooled StDev =

19 19 80 Boxplot of JAN-2017-AQIValue, DEC-2017-AQI-Value Data JAN-2017-AQIValue DEC-2017-AQI-Value Figure 6.3: Boxplot of JAN-2017-AQIValue, DEC-2017-AQI-Value Table 6.4 Minitab Output of Descriptive Statistics: Jan2017-Daily-PM2.5, Dec2017-Daily-PM2.5 Variable N N* Mean StDev Variance CoefVar Minimum Q1 Jan2017-Daily-PM Dec2017-Daily-PM Variable Median Q3 Maximum Jan2017-Daily-PM Dec2017-Daily-PM

20 20 Histogram (with Normal Curve) of Jan2017-Daily-PM Mean StDev N 31 8 Frequency Jan2017-Daily-PM Figure 6.4: Histogram (with Normal Curve) of Jan2017-Daily-PM2.5

21 21 Histogram (with Normal Curve) of Dec2017-Daily-PM Mean StDev N 31 5 Frequency Dec2017-Daily-PM Figure 6.5: Histogram (with Normal Curve) of Dec2017-Daily-PM2.5 Table 6.5 Minitab Output of Two-Sample T-Test and CI: Jan2017-Daily-PM2.5, Dec2017-Daily-PM2.5 Two-sample T for Jan2017-Daily-PM2.5 vs Dec2017-Daily-PM2.5 N Mean StDev SE Mean Jan2017-Daily-PM Dec2017-Daily-PM Difference = mu (Jan2017-Daily-PM2.5) - mu (Dec2017-Daily-PM2.5) Estimate for difference: % CI for difference: ( , ) T-Test of difference = 0 (vs not =): T-Value = 0.25 P-Value = DF = 60 Both use Pooled StDev =

22 22 25 Boxplot of Jan2017-Daily-PM2.5, Dec2017-Daily-PM Data Jan2017-Daily-PM2.5 Dec2017-Daily-PM2.5 Figure 6.6: Boxplot of Jan2017-Daily-PM2.5, Dec2017-Daily-PM2.5

23 Daily Mean PM2.5 Concentration - January and December 2017 Variable Jan2017-Daily-PM2.5 Dec2017-Daily-PM2.5 Data Index Figure 6.7: Time Series Plot of Jan2017-Daily-PM2.5, Dec2017-Daily-PM Concluding Remarks: In this paper, we have discussed about developing a lesson plan to show how the teaching of basic statistical methods via Minitab and Excel software can help the students to gain the knowledge and insights from the greenhouse gases emissions and air quality index data, in the United States and the World, affecting the health, environment and well-being of our communities. It is hoped that this paper will be helpful in teaching any introductory course in statistics such as courses in Statistical Methods (STA 2023) at Miami Dade College using Minitab and Excel. Further, as there is a great emphasis on statistical literacy and critical thinking in education these days, it is hoped that, with the help of Minitab and Excel, the students will be able to conduct statistical research projects in their STA2023 courses, and will be able to achieve the following: I. Search or web-search any real world data. II. Analyze the data statistically using Minitab and Excelk, that is, Compute descriptive statistics for any real world data; Draw histograms and other statistical graphs for data sets;

24 24 Discuss the distributions of data sets; Other Statistical Analysis. III. Write a statistical research project or report by incorporating the above findings. IV. Present the research project. Finally, it is hoped that by implementing the techniques discussed in this paper in preparing lesson plans will help us to develop in our students the quantitative analytic skills to evaluate and process numerical data, which is one of the Gen Ed Outcomes of Miami Dade College. Acknowledgments First, the author would like to express his thankfulness to Miami Dade College for providing him an opportunity to serve as a mathematics faculty in the college at its Hialeah Campus, without which it was impossible to conduct his research. The author would like to thank the Earth Ethics Institute (EEI) of Miami Dade College for providing us an opportunity to attend the EEI workshop on Health: Connecting People, Places, and Planet - Part II (EEI1015), at Hialeah Campus. Also, the author is thankful to the Earth Ethics Institute of Miami Dade College for accepting this paper dealing with a lesson plan on Statistical Analysis of Some Greenhouse Gases and Air Quality Index Data - A Computer Project-Based Assignment for Teaching STA2023 Course for publication at their website. The author is also thankful to the agencies, such as U.S. Environmental Protection Agency (EPA), Agency for Toxic Substances and Disease Registry (ASTDR), and among others, for providing valuable information resources and data on their websites, which were freely consulted during the preparation of this paper. Further, the author would like to thank his wife for her patience and perseverance for the period during which this paper was prepared. Lastly, the author would like to dedicate this paper to his late parents. References 1. Bluman, A. G. (2013). Elementary Statistics, A Brief Version, 6 th Edition. McGraw-Hill Co., New York. 2. McKenzie, J. and Goldman, R. (2005). The Student Guide to MINITAB Release 14, 14 th Edition. Pearson Addison-Wesley, New York.

25 25 3. Triola, M. F. (2010). Elementary Statistics, 11 th Edition. Addison-Wesley, New York. 4. Triola, M. F. (2014). Elementary Statistics Using Excel, 5 th Edition. Addison-Wesley, New York. 5. Tukey, J. (1977). Exploratory Data Analysis, Addison-Wesley, New York gas/all