{"id":544,"date":"2015-08-01T12:51:23","date_gmt":"2015-08-01T11:51:23","guid":{"rendered":"http:\/\/pcool.dyndns.org:8080\/statsbook\/?page_id=544"},"modified":"2025-07-01T10:53:07","modified_gmt":"2025-07-01T09:53:07","slug":"regression-plot","status":"publish","type":"page","link":"https:\/\/pcool.dyndns.org\/index.php\/regression-plot\/","title":{"rendered":"Regression Plot"},"content":{"rendered":"\n

Essentially, a regression plot is a scatter plot with a fitted regression line. Regression lines could be linear, quadratic, and polynomial amongst others. The example below demonstrates how to create a linear regression plot for Anscombe’s first data set. Download the anscombe.rda<\/a> dataset for this example1<\/a><\/sup>.<\/p>\n\n\n\n

Create a scatterplot<\/a> as discussed:<\/p>\n\n\n\n

Anscombe\u2019s fictional data sets can be shown by:<\/p>\n\n\n\n

anscombe.quartet\n<\/mark>   X1    Y1 X2   Y2 X3    Y3 X4    Y4\n1  10  8.04 10 9.14 10  7.46  8  6.58\n2   8  6.95  8 8.14  8  6.77  8  5.76\n3  13  7.58 13 8.74 13 12.74  8  7.71\n4   9  8.81  9 8.77  9  7.11  8  8.84\n5  11  8.33 11 9.26 11  7.81  8  8.47\n6  14  9.96 14 8.10 14  8.84  8  7.04\n7   6  7.24  6 6.13  6  6.08  8  5.25\n8   4  4.26  4 3.10  4  5.39 19 12.50\n9  12 10.84 12 9.13 12  8.15  8  5.56\n10  7  4.82  7 7.26  7  6.42  8  7.91\n11  5  5.68  5 4.74  5  5.73  8  6.89<\/mark><\/em><\/code><\/pre>\n\n\n\n
The first data set has X1 on the x -axis and Y1 on the y-axis. To create a scatterplot:<\/p>\n\n\n\n
regressionplot <- ggplot(data=anscombe.quartet,<\/em><\/span><\/em><\/span> aes(x = X1,y = Y1)) +<\/em><\/span>\ngeom_point() +<\/em><\/span>\nggtitle(label = \"Anscombe \\'s First Data Set\") +<\/em><\/span>\ntheme_bw()<\/em><\/span><\/code><\/pre>\n\n\n\n
The backslash \\ before the \u2018s is required so the quotation mark does not indicate the end of the title\u2019s text string, but that the quotation mark is part of the title!<\/p>\n\n\n\n
To add a regression line with a 95% confidence interval:<\/p>\n\n\n\n
regressionplot <- regressionplot + <\/span>\ngeom_smooth(aes(x=X1, y=Y1), data=anscombe.quartet, method = 'lm')<\/span>\nregressionplot<\/span><\/code><\/pre>\n\n\n\n
Will show the plot:<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Or without a 95% confidence interval:<\/p>\n\n\n\n
regressionplot2 <- ggplot(data=anscombe.quartet,<\/em><\/span> aes(x = X1,y = Y1)) +<\/em><\/span>\ngeom_point() + <\/em><\/span>\ngeom_smooth(method = 'lm', se = FALSE) +<\/em><\/span>\nggtitle(label = \"Anscombe \\'s First Data Set\") +<\/em><\/span>\ntheme_bw()<\/em><\/span>\nregressionplot2<\/span><\/em><\/code><\/pre>\n\n\n\n
Will show the plot:<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
It is customary to put the independent (explanatory or predictor) variable on the x-axis (abscissa) and the dependent (response or outcome) variable on the y-axis (ordinate). However, it is not always clear which variable is dependent and which independent.<\/p>\n","protected":false},"excerpt":{"rendered":"
Essentially, a regression plot is a scatter plot with a fitted regression line. Regression lines could be linear, quadratic, and polynomial amongst others. The example below demonstrates how to create a linear regression plot for Anscombe’s first data set. Download the anscombe.rda dataset for this example. Create a scatterplot as discussed: Anscombe\u2019s fictional data sets […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-544","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/pages\/544","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/comments?post=544"}],"version-history":[{"count":3,"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/pages\/544\/revisions"}],"predecessor-version":[{"id":4694,"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/pages\/544\/revisions\/4694"}],"wp:attachment":[{"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/media?parent=544"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}