Frequency Analysis of Regenerate Vegetative or by Seed and Cross-Classified Plant Species

Question 3_1
Name Onyema Kelechi Stanley
a) Scatter plot would give the best data visualization
b) Linear Regression test is ideal for testing hypothesis
c) There are linear relationships, multivariate normality, absence of auto correlation, and
Homoscedasticity
d) Based on the direction of the dots within the scatter plot, the assumptions are
met.
e) H0a: The length of the fish affected the weight of the parasites
H0b: The length of the fish DOES not affect the weight of the parasites
this is incorrect, see my lecture handouts on regression
e)
Call:
lm(formula = ParasiteWeight ~ FishLength, data = `472901154_Data3..Stick`)
Residuals:
Min 1Q Median 3Q Max
-0.37538 -0.04598 0.00550 0.07281 0.24919
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.16107 0.29998 -0.537 0.5942
FishLength 0.09591 0.04410 2.175 0.0355 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.117 on 41 degrees of freedom
Multiple R-squared: 0.1034, Adjusted R-squared: 0.08157

F-statistic: 4.73 on 1 and 41 DF, p-value: 0.03546
f) a(intercept)=-0.16107, b(slope)= 0.117, R2: 0.1034, F: 4.73, P: 0.0355
*
g) Y = -0.16107b + 0.09591
h) The length of the fish affected the weight of the parasites since
p<0.05
i)
R code reg1 <-
lm(FishLength~ParasiteWeight,data=`472901154_Data3..Stick`)
summary(reg1)
with(`472901154_Data3..Stick`,plot(FishLength,
ParasiteWeight))
abline(h= 0.45)
abline(0, 1)

Question 3_2
a) There are linear relationships, multivariate normality, absence of auto correlation, and
Homoscedasticity
b) answer the question
Call:
lm(formula = ParasiteWeight ~ LiverWeight, data = `472901154_Data3..Stick`)
you have mixed up predictor and response, read the question again
Residuals:
Min 1Q Median 3Q Max
-0.279545 -0.080332 -0.005673 0.097889 0.196839
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.30130 0.05748 5.242 5.13e-06 ***
LiverWeight 3.02524 0.88127 3.433 0.00138 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1089 on 41 degrees of freedom
Multiple R-squared: 0.2233, Adjusted R-squared: 0.2043
F-statistic: 11.78 on 1 and 41 DF, p-value: 0.001378
The parasite weight affects the Weight of the Liver; (F-statistic: 11.78 on 1 and 41
DF, p-value: 0.001378)
c) 3.71g
R code attach(`472901154_Data3..Stick`)
plot(ParasiteWeight, LiverWeight, main =
"scatterplot")
Regression <- lm(ParasiteWeight ~
LiverWeight, data =
`472901154_Data3..Stick`)
summary(Regression)