STATS help

Time to check out your data!

Take a look at the names of the columns.

We will be analyzing fine particulate pollution referred to

as PM2.5.

The PM2.5 concentrations are given (Daily Mean P

M2.5 Concentration) as well as the air

quality index for that pollutant (Daily_AQI_Value).

Please take a few minutes right now to learn

more about the Air Quality Index (AQI) which is calculated for four major air pollutants regulated

by the Clean Air Act

:

AQI Brochure

.

Look in your data set for the AQS_Parameter_Code column and scroll down this column.

There are

two different codes you might see here, namely 88101 and 88502.

These denote the reference method

used for measuring mass concentrations of PM2.5.

Code 88101

denotes a single filter 24 hour

balanced model PQ200 PM2.5 sampler with WINS, while Code 88502 denotes an R&P model 2025

PM2.5 sequential air sampler with VSCC.

Each is considered an acceptable method for collecting

the PM2.5 particulate measurements.

If

your data set has both measurement types, you will want to

delete all rows of (either) one of these.

Please edit the name of the sheet with the data on it, giving it the name “PM2.5 Data”.

Create a

second sheet and give that sheet/tab the name “Mod 7” (a

s we will be working in this same file for

the remainder of the term).

For the activities of Module 7 please work in the Mod 7 tab you have

created.

Use Excel’s COUNTIF function to find the number of days with a PM2.5 AQI value over 50.

On

such days, th

e air quality conditions are considered not to be “Good” per the EPA’s Air Quality

Index.

Use this to find the proportion of days for which the air quality was not good in your sample

(phat).

A success will be a day in which the PM2.5 AQI is above 50.

We f

irst must check that the conditions of the Central Limit Theorem apply for estimating

proportions in a population.

1.

The Random and Independent condition is met by the EPA’s collection agencies.

2.

The Large Sample condition must be checked.

If phat is the proportion of days with AQI

above 50, then we need to have both n*phat and n*(1

-

phat) greater than or equal to 10.

3.

The Big Population condition is met for our data.

When these three conditions ar

e met, we can use the Normal distribution to find probabilities

concerning the sample proportion.

If your data set does not meet the Large Sample condition,

obtain a new data set for a different city our county near where you live and then check these

con

ditions again.

Clearly label cells with the names and values for the following:

number of successes in sample,

sample size, sample proportion of successes, z value multiplier for 95% confidence interval, the

estimated standard error and the confidence int

erval.

By hand calculate the estimated standard

error and the confidence interval (using a calculator to do the math) using formulas 7.2 from our

Time to check out your data!

Take a look at the names of the columns. We will be analyzing fine particulate pollution referred to

as PM2.5. The PM2.5 concentrations are given (Daily Mean PM2.5 Concentration) as well as the air

quality index for that pollutant (Daily_AQI_Value). Please take a few minutes right now to learn

more about the Air Quality Index (AQI) which is calculated for four major air pollutants regulated

by the Clean Air Act: AQI Brochure.

Look in your data set for the AQS_Parameter_Code column and scroll down this column. There are

two different codes you might see here, namely 88101 and 88502. These denote the reference method

used for measuring mass concentrations of PM2.5. Code 88101 denotes a single filter 24 hour

balanced model PQ200 PM2.5 sampler with WINS, while Code 88502 denotes an R&P model 2025

PM2.5 sequential air sampler with VSCC. Each is considered an acceptable method for collecting

the PM2.5 particulate measurements. If your data set has both measurement types, you will want to

delete all rows of (either) one of these.

Please edit the name of the sheet with the data on it, giving it the name “PM2.5 Data”. Create a

second sheet and give that sheet/tab the name “Mod 7” (as we will be working in this same file for

the remainder of the term). For the activities of Module 7 please work in the Mod 7 tab you have

created.

Use Excel’s COUNTIF function to find the number of days with a PM2.5 AQI value over 50. On

such days, the air quality conditions are considered not to be “Good” per the EPA’s Air Quality

Index. Use this to find the proportion of days for which the air quality was not good in your sample

(phat). A success will be a day in which the PM2.5 AQI is above 50.

We first must check that the conditions of the Central Limit Theorem apply for estimating

proportions in a population.

1. The Random and Independent condition is met by the EPA’s collection agencies.

2. The Large Sample condition must be checked. If phat is the proportion of days with AQI

above 50, then we need to have both n*phat and n*(1-phat) greater than or equal to 10.

3. The Big Population condition is met for our data.

When these three conditions are met, we can use the Normal distribution to find probabilities

concerning the sample proportion. If your data set does not meet the Large Sample condition,

obtain a new data set for a different city our county near where you live and then check these

conditions again.

Clearly label cells with the names and values for the following: number of successes in sample,

sample size, sample proportion of successes, z value multiplier for 95% confidence interval, the

estimated standard error and the confidence interval. By hand calculate the estimated standard

error and the confidence interval (using a calculator to do the math) using formulas 7.2 from our