 Solution

Each scan() command reads a numeric vector from the entered data: the first creates the nine counts, and the second their nine predictor values. The last command fits a generalized linear model with the Poisson distribution and its default log link, including an intercept, then prints a summary. Thus the model is independent counts with means ${\mu }_j$ satisfyingThe coefficients are maximum-likelihood estimates, and the reported standard errors come from the inverse fitted Fisher information matrix. Increasing the predictor by one multiplies the fitted mean by $e^{0.3106}\simeq 1.364$, about a $36.4\%$ increase. The intercept corresponds to a mean $e^{1.363}\simeq 3.91$ at predictor zero, outside the observed range. Approximate coefficient-to-standard-error ratios are $6.17$ and $8.13$; the slope is strongly different from zero under the assumed model. A $95\%$ Wald confidence interval for the slope is about $[0.236,0.385]$, or $[1.266,1.470]$ for the multiplicative change.

The deviance is twice the log-likelihood advantage of the saturated model over the fitted model. For these counts it iswith the logarithmic term interpreted as zero when a count is zero. The saturated model has nine freely fitted means; the fitted model has two independent regression coefficients, so the residual degrees of freedom are $9-2=7$.

Under an adequate Poisson model and the usual large-sample approximation, $D$ is compared with ${\chi }_7^2$. The reported $13.218$ is rather large relative to seven, but below the $5\%$ upper critical value about $14.07$; its approximate upper-tail probability is $0.067$. Thus there is some indication of lack of fit or overdispersion, but not a rejection at the $5\%$ level on this diagnostic. The deviance-to-d.f. ratio is about $1.89$; it does not by itself prove an overdispersed model, and the chi-square approximation is imperfect for small counts.

There is one inconsistency in the supplied numerical output. Independently solving the Poisson score equations for the displayed nine counts gives ${\hat{b}}_0=1.363454$, ${\hat{b}}_1=0.310563$ and deviance $13.217971$, agreeing with the printed estimates and deviance. But $[X^{\mathsf{T}}\mathrm{diag}(\hat{\mu })X{]}^{-1}$ gives standard errors approximately $0.221295$ and $0.030850$. In particular the printed slope standard error $0.0382$ is not the standard Poisson-model value for these data. The interpretations above describe the supplied numbers; the directly recomputed slope ratio is $10.07$ and its normal-approximation $95\%$ interval is about $[0.2501,0.3710]$. The conclusion of a strong increasing trend is unchanged.