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<h1>Rcode for inference for means (t-interval or t-test)</h1>
<p>The production of a nationally marketed detergent results in certain
workers receiving prolonged exposure to the bacillus subtilis enzyme.
Nineteen workers were tested to determine the effects of these exposures
on various respiratory functions. The airflow rate, FEV1, is the ratio of
a person’s forced expiratory volume to the vital capacity, VC (max. volume
of air a person can exhale after taking a deep breath). If the enzyme has
an effect, it will be to reduce the FEV1/VC ratio. The norm is 0.80 in
persons with no lung dysfunction.</p>
<pre><code class="r">ratio <- c(0.61, 0.7, 0.63, 0.76, 0.67, 0.72, 0.64, 0.82, 0.88, 0.82, 0.78,
0.84, 0.83, 0.82, 0.74, 0.85, 0.73, 0.85, 0.87)
</code></pre>
<p>check summary statistics</p>
<pre><code class="r">summary(ratio)
</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.610 0.710 0.780 0.766 0.835 0.880
</code></pre>
<pre><code class="r">sd(ratio)
</code></pre>
<pre><code>## [1] 0.08591
</code></pre>
<p>Are the data symmetric or approximately normal?</p>
<pre><code class="r">qqnorm(ratio)
qqline(ratio)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>Note that to get a t interval and t test the same function is used.
Type</p>
<pre><code class="r">?t.test
</code></pre>
<p>to check what
options are available. </p>
<p>A 90% confidence interval can be obtained with</p>
<pre><code class="r">t.test(ratio, mu = 0.8, conf.level = 0.9)
</code></pre>
<pre><code>##
## One Sample t-test
##
## data: ratio
## t = -1.709, df = 18, p-value = 0.1046
## alternative hypothesis: true mean is not equal to 0.8
## 90 percent confidence interval:
## 0.7321 0.8005
## sample estimates:
## mean of x
## 0.7663
</code></pre>
<p>Save the output in an object called temp.</p>
<pre><code class="r">temp <- t.test(ratio, mu = 0.8, conf.level = 0.9)
</code></pre>
<p>check what you can read from the output</p>
<pre><code class="r">names(temp)
</code></pre>
<pre><code>## [1] "statistic" "parameter" "p.value" "conf.int" "estimate"
## [6] "null.value" "alternative" "method" "data.name"
</code></pre>
<p>confidence interval</p>
<pre><code class="r">temp$conf.int
</code></pre>
<pre><code>## [1] 0.7321 0.8005
## attr(,"conf.level")
## [1] 0.9
</code></pre>
<p>mean</p>
<pre><code class="r">temp$estimate
</code></pre>
<pre><code>## mean of x
## 0.7663
</code></pre>
<p>degrees of freedom used in calculating the pvalue or the confidence interval</p>
<pre><code class="r">temp$parameter
</code></pre>
<pre><code>## df
## 18
</code></pre>
<p>However the research question is whether the ratio is lower than normal. This is a one sided
hypothesis test, namely the alternative is \( H_A: \mu < 0.80. \)</p>
<pre><code class="r">temp.1sided <- t.test(ratio, mu = 0.8, alternative = "less")
</code></pre>
<p>The pvalue is 0.0523. The data provide evidence that exposure to B. subtilis may reduce the FEV1/VC ratio, but are inconclusive at the 5% significance level.</p>
<p>The R session information (including the OS info, R version and all
packages used):</p>
<pre><code class="r">sessionInfo()
</code></pre>
<pre><code>## R version 3.0.2 (2013-09-25)
## Platform: x86_64-apple-darwin10.8.0 (64-bit)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.5
##
## loaded via a namespace (and not attached):
## [1] evaluate_0.5.1 formatR_0.10 stringr_0.6.2 tools_3.0.2
</code></pre>
<pre><code class="r">Sys.time()
</code></pre>
<pre><code>## [1] "2014-03-06 17:01:08 EST"
</code></pre>
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