> model1=lm(Photosynthesis ~ Light, data=leafData) > summary(model1) Call: lm(fo

Question

> model1=lm(Photosynthesis ~ Light, data=leafData)

> summary(model1)

Call:

lm(formula = Photosynthesis ~ Light, data = leafData)

Residuals:

     Min       1Q   Median       3Q      Max

-2.29964 -1.16134 -0.01964 1.40777 1.95107

Coefficients:

             Estimate Std. Error t value Pr(>|t|)   

(Intercept) -1.552500   1.067473 -1.454    0.196   

Light        0.053907   0.005103 10.563 4.24e-05 ***

---

Signif. codes: 0 ?**?0.001 ?*?0.01 ??0.05 ??0.1 ??1

Residual standard error: 1.654 on 6 degrees of freedom

Multiple R-squared: 0.949,    Adjusted R-squared: 0.9405

F-statistic: 111.6 on 1 and 6 DF, p-value: 4.235e-05

Photosynthesis = -1.5525 + 0.05391 x 8

how do i conclude this analysis..

type your conclusion based on the analysis in problem 3. Do not just write “reject H0” or “do not reject H0.” Are the intercept and slope significantly different from zero? Is there evidence for a linear relationship?

Explanation / Answer

i) For Slope:

H0: The Slope of the regression is equal to zero

H1: The slope of the regression is not equal to zero

Slope: 0.053907 The p-value of Slope = 4.24e-05

Here P-value < alpha 0.05, So we reject H0

Thus we conclude that the slope of the regression is not equal to zero

ii) For intercept,

H0: The intercept of the regression is equal to zero

H1: The intercept of the regression is not equal to zero

Intercept: -1.552500 The p-value of intercept =0.196

Here P-value > alpha 0.05, So we accept H0

Thus we conclude that the intercept of the regression is equal to zero

iii) For linearship

H0: The regression equation is not best fit to the given data

H1: The regression equation is best fit to the given data

The p-value of regression is 4.235e-05

Here p-value < alpha 0.05, so we reject H0

Thus we conclude that The regression equation is best fit to the given data

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