QUESTION $1$ Regression analysis was performed on a time series containing $4$ years of quarterly sales. The sales data contains a seasonal effect, but no trend. The following estimated regression equation was obtained. The sales forecasts for Quarters $1$ and $2$ of Year $5$ are a$.132,116$ b$.112,96$ c$.96,80$ d$.112,80$ QUESTION $2$ Regression analysis was performed on a time series containing $5$ years of quarterly sales. The sales data contains both trend and seasonal effects. The following estimated regression equation was obtained. The sales forecast for Quarter $2$ of Year $6$ is a$.124\mathrm{.}4$ b$.116\mathrm{.}4$ c$.100\mathrm{.}4$ d$.140\mathrm{.}03-$All of the following are true about time series methods except a$.$ their forecasts are based solely on past values of the variable or past forecast errors b$.$ they discover a pattern in historical data and project it into the future c$.$ they identify a set of related independent, or explanatory, variables d$.$ they assume that the pattern of the past will continue into the future QUESTION $4$ Exhibit $14-2$ Consider the following time series. t $1234$ Yi $47910$ Refer to Exhibit $14-2.$ The slope of linear trend equation, b $1,$ is a$.1\mathrm{.}25$ b$.2\mathrm{.}5$ c$.1\mathrm{.}0$ d$.2\mathrm{.}0$ QUESTION $5$ Exhibit $14-2$ Consider the following time series. t $1234$ Yi $47910$ Refer to Exhibit $14-2.$ The intercept, b $0,$ is a$.2\mathrm{.}5$ b$.1\mathrm{.}25$ c$.2\mathrm{.}0$ d$.1\mathrm{.}0$ QUESTION $6$ Exhibit $14-2$ Consider the following time series. t $1234$ Yi $47910$ Refer to Exhibit $14-2.$ The forecast for period $5$ is a$.10\mathrm{.}0$ b$.12\mathrm{.}5$ c$.2\mathrm{.}5$ d$.4\mathrm{.}5$ QUESTION $7$ Exhibit $14-2$ Consider the following time series. t $1234$ Yi $47910$ Refer to Exhibit $14-2.$ The forecast for period $10$ is a$.22\mathrm{.}5$ b$.30\mathrm{.}0$ c$.10\mathrm{.}0$ d$.25\mathrm{.}0$