We can find a best fit line of the following linear data STAT -> EDIT (Enter Data into L1, L2) Year (x) Revenue (y) 1 $100,000 STAT -> CALC -> LinReg 2 $140,000 Enter output below 3 $200,000 y = ____ x + _____ 4 $260,000 Enter the above equation into Y= 5 $300,000 GRAPH 6 $370,000 7 $390,000 To make a Prediction Predict for 10 2nd -> CALC -> Value Enter x-value you want to predict for (In this problem x=10). Hit ENTER (You may need to zoom out) Take the resulting y value as your predicted answer. But, what if we have something that is both seasonal and increasing over time? The following data is from an amusement park, with attendance measured in the thousands. Find the seasonal index for Fall, Winter, Spring and Quarter Year 1 Year 2 Fall 352 391 Summer by: Winter 156 212 Calculate the quarterly average for each Spring 489 518 each year. (For each year, add Fall, Winter Summer 314 352 Total Spring and Summer, then divide by 4) Average per quarter in Year 1 = ___________ Average per quarter in Year 2 = ___________ Find the seasonal index for every season of every year that we have data for. Divide the actual for each season by the average per season (Step 1) [ie, divide Fall Year 1 by Average per Quarter in Year 1 to get Fall Year 1 Seasonal Index = 352/328 = 1.07] Seasonal Index Year 1 (Step 2) Year 2 (Step 2) Average (Step 3) Fall Winter Spring Summer Calculate the average seasonal index for each season by averaging Year 1 and Year 2 for each season. Once you have the seasonal indexes, to make a prediction for the Year Attendance following year, enter the totals for year 1 and year 2 into the 1 calculator and make a total prediction using the LinReg method 2 described above (“We can find a best fit line of the following data”) 3 (Step 4)
We can find a best fit line of the following linear data Year (x) Revenue (y) 1 $100,000 2 $140,000 3 $200,000 4 $260,000 5 $300,000 6 $370,000 7 $390,000 Predict for 10 STAT -> EDIT (Enter Data into L1, L2) STAT -> CALC -> LinReg Enter output below y = ____ x + _____ Enter the above equation into Y= GRAPH To make a Prediction 2nd -> CALC -> Value Enter x-value you want to predict for (In this problem x=10). Hit ENTER (You may need to zoom out) Take the resulting y value as your predicted answer. But, what if we have something that is both seasonal and increasing over time? The following data is from an amusement park, with attendance measured in the thousands. Quarter Year 1 Year 2 Fall 352 391 Winter 156 212 Spring 489 518 Summer 314 352 Total Find the seasonal index for Fall, Winter, Spring and Summer by: Calculate the quarterly average for each each year. (For each year, add Fall, Winter Spring and Summer, then divide by 4) Average per quarter in Year 1 = ___________ Average per quarter in Year 2 = ___________ Find the seasonal index for every season of every year that we have data for. Divide the actual for each season by the average per season (Step 1) [ie, divide Fall Year 1 by Average per Quarter in Year 1 to get Fall Year 1 Seasonal Index = 352/328 = 1.07] Seasonal Index Year 1 (Step 2) Year 2 (Step 2) Average (Step 3) Fall Winter Spring Summer Calculate the average seasonal index for each season by averaging Year 1 and Year 2 for each season. Once you have the seasonal indexes, to make a prediction for the following year, enter the totals for year 1 and year 2 into the calculator and make a total prediction using the LinReg method described above (“We can find a best fit line of the following data”) Year Attendance 1 2 3 (Step 4)
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