The following are the net sales of Mike\'s Surf Shoppe from 2005 to 2014 Not ava

Question

The following are the net sales of Mike's Surf Shoppe from 2005 to 2014 Not available in Connect Determine the least squares trend equation Use a linear equation ((found the final answers to ? decimal places.) Calculate the points for the years 2007 and 2012. (Hound the final answers to 1 decimal place.) For 2007. Y = For 2012. Y = Estimate the sales for 2016 (Round the final answer to 1 decimal place.) For 2016. r = Does this seem like a reasonable estimate based on the historical data? By how much have sales increased or decreased (per year), on average during the period? (Round the final answer to the nearest whole dollar.)

Explanation / Answer

> tt <- read.csv("clipboard",header=TRUE,sep=" ")
> head(tt)
Year Sales
1 1 58063
2 2 60016
3 3 62076
4 4 64392
5 5 68364
6 6 70331
> sales_lm <- lm(Sales~Year,data=tt)
> summary(sales_lm)

Call:
lm(formula = Sales ~ Year, data = tt)

Residuals:
Min 1Q Median 3Q Max
-988.51 -674.66 -2.79 456.65 1195.27

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54438.7 545.4 99.81 1.13e-13 ***
Year 2684.4 87.9 30.54 1.44e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 798.4 on 8 degrees of freedom
Multiple R-squared: 0.9915, Adjusted R-squared: 0.9904
F-statistic: 932.7 on 1 and 8 DF, p-value: 1.435e-09

> predict(sales_lm,newdata=data.frame(Year=11))
1
83967.13

b) Y = 54438.7 + 2684.4 t

c) For 2007, Y = 62491.9

For 2012, Y = 75913.9.

d.1) For 2016, Y = 83967.1.

d.2) Based on the historical data, this seems a reasonable estimate.

e) The sales increase by $2684.4 per year.

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