Merge pull request #26 from TrevorFrench/regression-example

fixing example

docs/p4c2-regression.html

@@ -531,11 +531,11 @@ <h2 data-number="16.1" class="anchored" data-anchor-id="linear-regression"><span
 <h2 data-number="16.2" class="anchored" data-anchor-id="multiple-regression"><span class="header-section-number">16.2</span> Multiple Regression</h2>
 <p>If you had more x variables you wanted to add to your linear model, you could add them just like you would in any other math equation. Here’s an example:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">lm</span>(data<span class="sc">$</span>y <span class="sc">~</span> data<span class="sc">$</span>x1 <span class="sc">+</span> data<span class="sc">$</span>x2 <span class="sc">-</span> data<span class="sc">$</span>x3 <span class="sc">*</span> data<span class="sc">$</span>x4)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">lm</span>(data<span class="sc">$</span>y <span class="sc">~</span> data<span class="sc">$</span>x1 <span class="sc">+</span> data<span class="sc">$</span>x2 <span class="sc">+</span> data<span class="sc">$</span>x3 <span class="sc">+</span> data<span class="sc">$</span>x4)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Additionally, you can use the “data” parameter rather than putting the name of the dataset before every variable.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">lm</span>(y <span class="sc">~</span> x1 <span class="sc">+</span> x2 <span class="sc">-</span> x3 <span class="sc">*</span> x4, <span class="at">data =</span> data)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">lm</span>(y <span class="sc">~</span> x1 <span class="sc">+</span> x2 <span class="sc">+</span> x3 <span class="sc">+</span> x4, <span class="at">data =</span> data)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Let’s try a real example with the mtcars dataset.</p>
 <div class="cell">

docs/search.json

@@ -627,7 +627,7 @@
     "href": "p4c2-regression.html#multiple-regression",
     "title": "16  Regression",
     "section": "16.2 Multiple Regression",
-    "text": "16.2 Multiple Regression\nIf you had more x variables you wanted to add to your linear model, you could add them just like you would in any other math equation. Here’s an example:\n\nlm(data$y ~ data$x1 + data$x2 - data$x3 * data$x4)\n\nAdditionally, you can use the “data” parameter rather than putting the name of the dataset before every variable.\n\nlm(y ~ x1 + x2 - x3 * x4, data = data)\n\nLet’s try a real example with the mtcars dataset.\n\nhead(mtcars)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nmpg\ncyl\ndisp\nhp\ndrat\nwt\nqsec\nvs\nam\ngear\ncarb\n\n\n\n\nMazda RX4\n21.0\n6\n160\n110\n3.90\n2.620\n16.46\n0\n1\n4\n4\n\n\nMazda RX4 Wag\n21.0\n6\n160\n110\n3.90\n2.875\n17.02\n0\n1\n4\n4\n\n\nDatsun 710\n22.8\n4\n108\n93\n3.85\n2.320\n18.61\n1\n1\n4\n1\n\n\nHornet 4 Drive\n21.4\n6\n258\n110\n3.08\n3.215\n19.44\n1\n0\n3\n1\n\n\nHornet Sportabout\n18.7\n8\n360\n175\n3.15\n3.440\n17.02\n0\n0\n3\n2\n\n\nValiant\n18.1\n6\n225\n105\n2.76\n3.460\n20.22\n1\n0\n3\n1\n\n\n\n\n\nNow, let’s try to predict mpg and use every other column as a variable then see what the results look like.\n\nlm <- lm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + gear + carb\n            , data = mtcars)\nsummary(lm)\n\n\nCall:\nlm(formula = mpg ~ cyl + disp + hp + drat + wt + qsec + vs + \n    am + gear + carb, data = mtcars)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-3.4506 -1.6044 -0.1196  1.2193  4.6271 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)  \n(Intercept) 12.30337   18.71788   0.657   0.5181  \ncyl         -0.11144    1.04502  -0.107   0.9161  \ndisp         0.01334    0.01786   0.747   0.4635  \nhp          -0.02148    0.02177  -0.987   0.3350  \ndrat         0.78711    1.63537   0.481   0.6353  \nwt          -3.71530    1.89441  -1.961   0.0633 .\nqsec         0.82104    0.73084   1.123   0.2739  \nvs           0.31776    2.10451   0.151   0.8814  \nam           2.52023    2.05665   1.225   0.2340  \ngear         0.65541    1.49326   0.439   0.6652  \ncarb        -0.19942    0.82875  -0.241   0.8122  \n---\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.65 on 21 degrees of freedom\nMultiple R-squared:  0.869, Adjusted R-squared:  0.8066 \nF-statistic: 13.93 on 10 and 21 DF,  p-value: 3.793e-07\n\n\nFrom here, you would likely tweak your model further based on the significance statistics we see here; however, that’s outside the scope of what we’re doing in this book. Take a look in the resources section at the end of this chapter to dive deeper into developing regression models."
+    "text": "16.2 Multiple Regression\nIf you had more x variables you wanted to add to your linear model, you could add them just like you would in any other math equation. Here’s an example:\n\nlm(data$y ~ data$x1 + data$x2 + data$x3 + data$x4)\n\nAdditionally, you can use the “data” parameter rather than putting the name of the dataset before every variable.\n\nlm(y ~ x1 + x2 + x3 + x4, data = data)\n\nLet’s try a real example with the mtcars dataset.\n\nhead(mtcars)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nmpg\ncyl\ndisp\nhp\ndrat\nwt\nqsec\nvs\nam\ngear\ncarb\n\n\n\n\nMazda RX4\n21.0\n6\n160\n110\n3.90\n2.620\n16.46\n0\n1\n4\n4\n\n\nMazda RX4 Wag\n21.0\n6\n160\n110\n3.90\n2.875\n17.02\n0\n1\n4\n4\n\n\nDatsun 710\n22.8\n4\n108\n93\n3.85\n2.320\n18.61\n1\n1\n4\n1\n\n\nHornet 4 Drive\n21.4\n6\n258\n110\n3.08\n3.215\n19.44\n1\n0\n3\n1\n\n\nHornet Sportabout\n18.7\n8\n360\n175\n3.15\n3.440\n17.02\n0\n0\n3\n2\n\n\nValiant\n18.1\n6\n225\n105\n2.76\n3.460\n20.22\n1\n0\n3\n1\n\n\n\n\n\nNow, let’s try to predict mpg and use every other column as a variable then see what the results look like.\n\nlm <- lm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + gear + carb\n            , data = mtcars)\nsummary(lm)\n\n\nCall:\nlm(formula = mpg ~ cyl + disp + hp + drat + wt + qsec + vs + \n    am + gear + carb, data = mtcars)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-3.4506 -1.6044 -0.1196  1.2193  4.6271 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)  \n(Intercept) 12.30337   18.71788   0.657   0.5181  \ncyl         -0.11144    1.04502  -0.107   0.9161  \ndisp         0.01334    0.01786   0.747   0.4635  \nhp          -0.02148    0.02177  -0.987   0.3350  \ndrat         0.78711    1.63537   0.481   0.6353  \nwt          -3.71530    1.89441  -1.961   0.0633 .\nqsec         0.82104    0.73084   1.123   0.2739  \nvs           0.31776    2.10451   0.151   0.8814  \nam           2.52023    2.05665   1.225   0.2340  \ngear         0.65541    1.49326   0.439   0.6652  \ncarb        -0.19942    0.82875  -0.241   0.8122  \n---\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.65 on 21 degrees of freedom\nMultiple R-squared:  0.869, Adjusted R-squared:  0.8066 \nF-statistic: 13.93 on 10 and 21 DF,  p-value: 3.793e-07\n\n\nFrom here, you would likely tweak your model further based on the significance statistics we see here; however, that’s outside the scope of what we’re doing in this book. Take a look in the resources section at the end of this chapter to dive deeper into developing regression models."
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p4c2-regression.qmd

@@ -66,14 +66,14 @@ If you had more x variables you wanted to add to your linear model, you could ad
 
 ```{r}
 #| eval: false
-lm(data$y ~ data$x1 + data$x2 - data$x3 * data$x4)
+lm(data$y ~ data$x1 + data$x2 + data$x3 + data$x4)
 ```
 
 Additionally, you can use the "data" parameter rather than putting the name of the dataset before every variable.
 
 ```{r}
 #| eval: false
-lm(y ~ x1 + x2 - x3 * x4, data = data)
+lm(y ~ x1 + x2 + x3 + x4, data = data)
 ```
 
 Let's try a real example with the mtcars dataset.