The FloridaLakes dataset includes data on 53 lakes in Florida. Two of the variab

Question

The FloridaLakes dataset includes data on 53 lakes in Florida. Two of the variables recorded were pH (acidity of the lake water) and AvgMercury (average mercury level for a sample of fish from each lake). We wish to use the pH of the lake water (which is easy to measure) to predict average mercury levels in fish, which is harder to measure. A scatterplot of the data is shown below and we see that the conditions for fitting a linear model are reasonably met. Computer output for the regression analysis is shown below. The regression equation is AvgMercury = 1.53-0.152 pH Predictor Coef S Constant5309 0.2035 7.52 0.000 pH E Coef T P -0.15230 0.03031-5.02 0.000 S = 0.281645 R-Sq = 33.1% R-Sq (adj) = 31.8% Click here for the dataset associated with this question (a) Use the fitted model to predict the average mercury level in fish for a lake with a pH of 5.5 Round your answer to three decimal places. The mercury level would be the absolute tolerance is +/-0.001 LINK TO TEXT (b) What is the slope in the model? Round your answer to three decimal places. Interpret the slope in context

Explanation / Answer

a) PH = 5.5
then The mercury level would be = 1.53 - 0.152*5.5 = 0.694

b) Slope b1 = -0.15230

Interpret of Slope:
As Ph increase 1 lake of water then the expected average mercury decrease 0.152

c) t = -5.02
p-value = 0.000

reject H0

d) The 99% confedince inter of the slope is
(-5.02 - 2.6757*0.03031, -5.02 + 2.6757*0.03031)=(-5.1011,-4.9389)

e) R^2 = 0.331 = 33.1%
33.1% of variation in merucay level is explained by the ph value

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