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The Quasi-Poisson regression retains the same conditional mean but permitsThis is a mean–variance function specification through quasi-likelihood; it does not assign a full probability distribution to each count. For independent observations, the quasi-score equation is proportional to ${\sum }_i{x}_i(Y_i-{\mu }_i)=0$, so the mean statistical parameter estimates equal those from Poisson regression. The output estimates $\hat{\varphi }=3.515351$ through the Pearson dispersion estimator, and inflates the standard errors by approximately $\sqrt{\hat{\varphi }}=1.875$.

The large residual deviance relative to 728 residual statistical degrees of freedom also signals substantial overdispersion. Daily weather, traffic and other omitted conditions may produce greater count variation than a homogeneous Poisson distribution allows. The Quasi-Poisson regression accounts for that extra marginal variance. It still requires a correct conditional mean and an appropriate independence assumption; a common dispersion parameter alone does not repair serial correlation.

The Pearson dispersion estimator is $\hat{\varphi }=3.099079$, well above the Poisson value one. A Quasi-Poisson regression retains the logarithmic mean model but usesThis is a quasi-likelihood specification; it does not by itself identify a count probability distribution. For a common $\varphi$, the quasi-score equation is the Poisson score divided by $\varphi$, so its roots and fitted means are unchanged. ThusCoefficient standard errors are multiplied by $\sqrt{\hat{\varphi }}$. The altitude standard error becomes $0.00447\sqrt{3.099079}\approx 0.0078691$, giving the requested approximate normal confidence intervalIt excludes zero, so retain altitude on this approximate 5% test. Its test statistic is about $2.412$ after allowing for overdispersion, rather than the Poisson value $4.245$. Rescaling uncertainty cannot repair a systematically misspecified mean; the diagnostics in part (e) motivate reconsidering that mean as well.

The quasibinomial regression retains the logit link and mean function $p_i=(1+e^{-x_i^T\beta }{)}^{-1}$ but uses the working variance function $\mathrm{Var}(Y_i)=\varphi p_i(1-p_i)$. Its quasi-score equation isso multiplying by the common dispersion parameter does not change the coefficient estimates. Iteratively reweighted least squares therefore gives the same fitted means and coefficients as the binomial model. The usual Pearson dispersion estimator isThe estimated coefficient covariance matrix is $\hat{\varphi }(X^{T}WX{)}^{-1}$, with $W_{ii}={\hat{p}}_i(1-{\hat{p}}_i)$. From the intercept standard errors, $(1.08921/1.13031{)}^2\simeq 0.92860$; the rounded slope errors give approximately the same factor. The display uses approximate Student's t-tests with 118 residual degrees of freedom instead of fixed-dispersion normal tests.

For a genuinely individual binary response, the Bernoulli distribution identity $Y_i^2=Y_i$ forces $\mathrm{Var}(Y_i)=p_i(1-p_i)$. Thus $\varphi \ne 1$ is a working quasi-likelihood specification, not a different independent binary distribution with that mean. The mild estimated underdispersion does not establish an improved model, and the mean predictions are unchanged. In particular, a scalar rescaling does not model correlation among users, and usual likelihood-based Akaike information criterion comparisons are unavailable for a family without a specified probability likelihood. The output gives no convincing reason to prefer model3 to model2.

With $X$ having rows $x_i^T=(1,g_i,m_i)$ and ${\mu }_i(\beta )=e^{x_i^T\beta }$, the quasi-score equation isIt is the same coefficient equation as for Poisson maximum likelihood, explaining why the two models have identical coefficient estimates.

Let $W=\mathrm{diag}({\hat{\mu }}_1,\dots ,{\hat{\mu }}_n)$ and let coordinate $2$ denote gender. The model-based standard error is

Quasi-likelihood specifies a mean and a variance function without necessarily specifying a full response distribution. For independent responses, the estimating equation is ${\sum }_i(\partial {\mu }_i/\partial \beta )(y_i-{\mu }_i)/(\varphi V({\mu }_i))=0$. A common scalar dispersion parameter rescales coefficient covariance while leaving the roots of the quasi-score equation unchanged.