Worksheet Correlation and RegressionSolve the Statistical question pro<\/p>\n
Question 2
\nJanssenet al. (2007) studied the relationships between a variety of abiotic factors and benthic invertebrate abundance at sites on beaches along the Dutch coast. One of these abiotic factors was the relative height of the site in relationship to the average sea level of the area (NAP). Positive values of NAP indicate sites that are higher than the average sea level, whereas negative values indicate sites that are below the average sea level.
\nThe data are in the file sle251dutch.csv and the relevant variables are the response variable, richness (richness of invertebrate species), and the predictor variable, NAP (relative height of the site in relationship to the average sea level of the area).
\nFormat of sle251dutch.csv data file<\/p>\n
Site NAP richness
\n1 0.045 11
\n2 -1.036 10
\n3 -1.336 13
\n4 0.616 11
\n5 -0.684 10
\n.. .. ..
\nSite The number of the site where the samples were collected
\nNAP Relative height of the site in relationship to the average sea level of the area
\nPredictor variable
\nrichness Richness of invertebrate species
\nResponse variable<\/p>\n
a) Janssenet al. (1996) were interested in modeling the linear relationship between invertebrate richness (response) and the relative height of the site in relationship to the average sea level (predictor). List the following:<\/p>\n
The biological inference of interest<\/p>\n
The biological null hypothesis derived from above<\/p>\n
The statistical null hypothesis (H0) derived from above<\/p>\n
b) Draw a scatterplot of NAP against richness. Draw boxplots for each variable as well. Any evidence of skewness in the distributions or nonlinearity <\/p>\n
To create scatterplot in R
\nGraphs
\nScatterplot
\nSelect x-variable (NAP) and y-variable (richness)
\nCheck Marginal boxplots and Least-squares line
\nUnselect Smooth line and show spread
\nOK<\/p>\n
c) Fit the regression model richness = intercept + slope x NAP.
\nTo fit linear regression and create an ANOVA table in R
\nStatistics
\nFit models
\nLinear regression\u2026
\nYou can enter a name for the results object (Enter name for model:) but its simplest to just use the name that R provides.
\nSelect richnessfrom Response variablelist
\nSelect NAPfrom Explanatory variables list.
\nOK
\nModels
\nHypothesis tests
\nANOVA table
\nSelect Partial, ignoring marginality (\u201cType III\u201d).
\nOK
\nExamine the regression output and identify and interpret the following:<\/p>\n
Sample y-intercept
\nValue (estimate in the R output):
\nInterpretation:<\/p>\n
Slope of regression line (NAP)
\nValue(estimate in the R output):
\nInterpretation:<\/p>\n
t statistic for main H0 (regression slope equals zero)
\nValue:
\nInterpretation:<\/p>\n
P-value for main H0 (regression slope equals zero)
\nValue:
\nInterpretation:<\/p>\n
r2 value (multiple R-squared)
\nValue:
\nInterpretation:<\/p>\n
d) Complete the following ANOVA table from the regression analysis<\/p>\n
Source of variation SS df MS F ratio
\nRegression<\/p>\n
Residual
\nTotal
\n44
\nNote: To get the MS values from the output \u2013 remember to divide the SS value by the df.<\/p>\n
e) What conclusions would you draw from the regression analysis (statistical and biological) <\/p>\n
f) What invertebrate richness would you predict for a new site with an NAP of -2 Simply plug -2 into your regression equation and calculate predicted richness.<\/p>\n","protected":false},"excerpt":{"rendered":"
Worksheet Correlation and RegressionSolve the Statistical question pro Question 2 Janssenet al. (2007) studied the relationships between a variety of abiotic factors and benthic invertebrate abundance at sites on beaches along the Dutch coast. One of these abiotic factors was Read More …<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-94079","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/94079","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/comments?post=94079"}],"version-history":[{"count":0,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/94079\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/media?parent=94079"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/categories?post=94079"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/tags?post=94079"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}