Inelastic, probably, and uncertain by a factor of two: that is how this Unit 3 interpretation for MT445 reads an island ferry's demand regression, each coefficient restated in tickets. Searches like "mt 445 unit 3 assignment example", "mt445 unit 3 sample" and "mt445 unit 3 example" land here.
What a finished MT445 Unit 3 regression output interpretation looks like
Six pages that reproduce the supplied output before commenting on it. The table covers 72 weeks across three seasons, with weekly round-trip walk-on tickets regressed on the adult fare, rain days, an island event indicator, a July-August indicator and two season indicators. Fare carries minus 245.9 with a standard error of 44.3; rain days minus 454.8; event weeks plus 2,074; peak weeks plus 3,403. R-squared is 0.924, adjusted 0.917, on an F of 131.2. A translation table follows, restating every coefficient in tickets per week. The elasticity section evaluates fare at the means, $23.17 and 9,748 tickets, for minus 0.585, then carries the coefficient's 95 percent interval through to an elasticity range of minus 0.37 to minus 0.80. A second supplied specification, run without season indicators, gets the final analytical section.
How a MT445 Unit 3 example is structured
Interpretation runs from the question a manager would ask to the evidence that answers it, so the fare coefficient comes first although it sits second in the table. Every figure is converted into units the operations office uses: a $1 rise in the round-trip fare costs about 246 tickets a week, with weather, events, peak weeks and season held at their levels. Significance gets two sentences and is then put in its place, since a t statistic of minus 5.55 shows the effect is not zero without showing how large it is. The interval does that job, and the paper carries it through to the elasticity. Identification has its own section: fares moved between seasons and in fifteen promotional weeks, which explains why the second specification's minus 65.2 is contaminated by traffic growth. Which way the remaining bias most likely runs is stated at the end.
The table, reproduced first
Six variables and the constant appear exactly as supplied, each with coefficient, standard error and t statistic, and units added in a note. Interpretation waits until the whole output is visible.
Tickets per dollar of fare
Each $1 on the round-trip fare lowers weekly walk-on tickets by about 246, other variables held constant. A $2 rise implies roughly 490 fewer tickets a week, with an interval running from about 315 to 670.
From coefficient to elasticity
At a mean fare of $23.17 and 9,748 tickets, elasticity is minus 0.585. The interval's ends give minus 0.37 and minus 0.80, both inelastic, the one conclusion the paper treats as firm.
Why a second model says minus 65
Without season indicators, the fare coefficient shrinks to minus 65.2 and loses significance. Fares rose $2 each season while traffic grew for other reasons, so that model credits the growth against price.
Promotions placed in slow weeks
All fifteen $4 promotions ran in shoulder weeks the line expected to be quiet. If that expectation held, cheap fares coincided with weak demand, and the true response is probably stronger than estimated.
Where marks go in MT445 Unit 3
Reading a coefficient as an elasticity is the commonest error in this unit, and calling minus 245.9 the price elasticity draws an immediate comment in most sections. Instructors look for each estimate stated in its units and with its interval, not just flagged as significant. Praise for an R-squared above 0.9 as proof the fare effect is right confuses fit with identification, which is the lesson the unit is usually built around. Where two specifications disagree, graders expect the reason, here fare increases that coincided with growth, rather than a preference for whichever number looks better. Omitted-variable paragraphs listing generic worries earn less than one naming a mechanism and its likely direction, such as promotions timed for slow weeks pushing the estimate toward zero. Conclusions that set a fare to the cent overreach what 72 weeks can support.
Get a MT445 Unit 3 example written to your instructions
Send the regression output your Unit 3 assignment supplies, any separate variable definitions, and the rubric. In 24-48h a sample arrives interpreting each coefficient in its own units, carrying the interval through to an elasticity range and naming the likeliest bias, free as a first request. If only raw data came with the prompt, say which software the section expects.
MT445 Unit 3 questions, answered
How do I convert a linear regression coefficient into a price elasticity?
Multiply the coefficient by the ratio of price to quantity at the point being evaluated, usually the sample means. Here minus 245.9 times 23.17 divided by 9,748 gives about minus 0.585. Repeat the calculation at each end of the coefficient's confidence interval and you have an elasticity range, which is often what the question actually wants.
My fare coefficient is significant. Does that make the estimate reliable?
It means data like these would rarely show an effect that large if the true effect were zero. It says nothing about bias from something left out, or from the way prices were set. A significant but biased coefficient is still wrong. MT445 graders generally reward the paper that asks how the price variation arose before trusting what it shows.
What does a second specification add if the prompt asks about only one?
If your assignment supplies a single model, interpret it and discuss what an omitted variable would do to it. Where a second model appears, the comparison is usually the point: a coefficient that moves sharply when a control is added or dropped shows the controls matter for identification. Explain the direction of the change, not only that it happened.