A finished BUS-352 Topic 7 simple regression interpretation example, reading a slope, r-squared and residuals for advertising and sales, and showing why budget allocation blocks a causal reading. Searches like "bus 352 topic 7 assignment example", "bus352 topic 7 sample" and "bus-352 topic 7 example" land here.
What a finished BUS-352 Topic 7 simple regression interpretation looks like
The finished interpretation begins with how the numbers came to exist. Each store's advertising budget was set as a share of its prior-year sales, a fact the example states before fitting anything, because it means larger stores received larger budgets by rule. The fitted line follows, with illustrative estimates: stores spending an extra $1,000 a month on advertising average about $4,200 more in monthly sales, with an r-squared near 0.50. The slope is described as a difference between stores, not a change within one. A residual plot is checked for curvature and for the two largest stores pulling the line. The example then answers the director directly: the slope cannot be read as the payoff from raising small stores' budgets, since budget size partly followed sales. A randomized test in selected stores is proposed instead.
How a BUS-352 Topic 7 example is structured
The example is laid out so the data-generating story arrives before the regression output does. It opens with the director's question, whether raising advertising at the smaller stores would raise their sales, and restates it as a causal question a regression on these data may not be able to answer. A data section describes the forty stores and the budgeting rule, with the observed range of spend stated so later predictions can be checked against it. The model section reports the slope, intercept and r-squared and interprets each in store terms, noting that the intercept describes zero spend, a level no store came near. A diagnostics section reads the residual plot. The interpretation section separates association from effect and explains how the budgeting rule alone could produce the slope. It finishes by proposing the randomized test and what it would measure.
How the budgets were set
Advertising was allocated as a share of prior-year sales, so the example states that rule first, because it shapes every relationship that follows.
The slope as a comparison between stores
An extra $1,000 of advertising goes with about $4,200 more in sales across stores, which describes how stores differ rather than how one store would respond.
An intercept outside the data
Zero advertising lies below every store's actual spend, so the example reports the intercept as a fitting constant and declines to treat it as a forecast.
Residuals checked for shape and leverage
The residual plot is read for curvature and for the two largest stores, whose removal would noticeably change the slope the director is relying on.
A test that could answer the question
Randomly choosing some small stores to receive higher budgets would separate the effect of advertising from the budgeting rule, and the example outlines that design.
Where marks go in BUS-352 Topic 7
One sentence costs more here than any calculation: the claim that each extra advertising dollar generates $4.20 in sales. The computation behind the slope may be flawless, yet the sentence claims an effect the data cannot show, since budgets were allocated from past sales and bigger stores would have both more advertising and more revenue regardless. Interpreting r-squared as the share of sales caused by advertising repeats the error in another form. Papers that treat the intercept as the sales a store would make with no advertising extrapolate far outside the data. Skipping the residual plot misses the two large stores steering the line. A recommendation to raise small stores' budgets on this evidence alone gets the decision wrong, because nothing in these data tested that change.
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BUS-352 Topic 7 questions, answered
What does an r-squared of 0.50 mean for the stores?
That about half of the store-to-store variation in monthly sales lines up with differences in advertising spend under a straight-line fit, which corresponds to a correlation of roughly 0.71. It does not mean advertising produces half of sales, and it leaves the other half of the variation unexplained by this model. Since budgets followed past sales, some of the shared variation reflects store size rather than advertising.
Can the regression be used to predict sales for a new store?
Only within the range of advertising the forty stores actually spent, and only as an estimate for a store resembling them. A prediction for spend far above or below that range assumes the straight line continues where no data exist. Even inside the range, a prediction describes stores like this one on average, and the interval around an individual store's sales will be much wider than the line suggests.
How can a business ever learn whether advertising works?
By changing advertising in a way that is not tied to anything else about the stores. Randomly assigning some comparable stores to higher budgets and others to their usual level, then comparing sales afterward, breaks the link between budget and store size that confounds these data. Many firms run such tests in selected markets for exactly this reason, and the example sketches one the director could approve.