data collected over a period of time example data seems pretty random!!

TIME SERIES

TIME SERIESData collected over a period of time

TIME SERIESData collected over a period of time

Time

Data

ExampleTime SALES

1 Week 1 Monday 50.92 Wednesday 44.63 Friday 57.14 Week 2 Monday 55.45 Wednesday 476 Friday 58.27 Week 3 Monday 62.48 Wednesday 56.49 Friday 63.610 Week 4 Monday 68.611 Wednesday 57.812 Friday 70.513 Week 5 Monday 73.514 Wednesday 63.115 Friday 72.216 Week 6 Monday 73.617 Wednesday 66.618 Friday 79.619 Week 7 Monday 81.620 Wednesday 70.721 Friday 80.922 Week 8 Monday 83.323 Wednesday 76.524 Friday 89.925 Week 9 Monday 90.626 Wednesday 80.327 Friday 90.128 Week 10 Monday 92.229 Wednesday 83.530 Friday 96.4

Time SALES


Data seems pretty random!!

Time SALES



Some go up,

Time SALES



Some go up,

some down

Time SALES



Some go up,

some down

What the?

Drawing a graph helps a bitTime SALES


You can see the general trendTime SALES


Looking closer you can see a cyclic nature to this data

Time SALES


Looking closer you can see a cyclic nature to this data

Time SALES


Every third value is a low one

We want to be more specific about the general trend

Time SALES


We want to be more specific about the general trend, so we calculate a MOVING AVERAGE

Time SALES3 POINT MOVING

AVERAGE1 Week 1 Monday 50.92 Wednesday 44.6 50.93 Friday 57.1 52.44 Week 2 Monday 55.4 53.25 Wednesday 47 53.56 Friday 58.2 55.97 Week 3 Monday 62.4 59.08 Wednesday 56.4 60.89 Friday 63.6 62.910 Week 4 Monday 68.6 63.311 Wednesday 57.8 65.612 Friday 70.5 67.313 Week 5 Monday 73.5 69.014 Wednesday 63.1 69.615 Friday 72.2 69.616 Week 6 Monday 73.6 70.817 Wednesday 66.6 73.318 Friday 79.6 75.919 Week 7 Monday 81.6 77.320 Wednesday 70.7 77.721 Friday 80.9 78.322 Week 8 Monday 83.3 80.223 Wednesday 76.5 83.224 Friday 89.9 85.725 Week 9 Monday 90.6 86.926 Wednesday 80.3 87.027 Friday 90.1 87.528 Week 10 Monday 92.2 88.629 Wednesday 83.5 90.730 Friday 96.4

Our data seems to occur in groups of three, so we will use a 3 POINT moving average

Time SALES

3 POINT MOVING


How does it work?



Calculate the average of the first group of 3 points



3. Calculate the average of the second 3 points

1. Drop the first Monday

2. Pick up the second Monday



3. Calculate the average of the third group of 3 points

1. Drop the first Wednesday

2. Pick up the second Wednesday



3. Calculate the average of the fourth group of 3 points

1. Drop the first Friday

2. Pick up the second Friday



Keep moving the averages down until you have run out of points to include

Remember…



Every moving average needs to include one of each type of value, ie

1 x Monday1 x Wednesday1 x Friday

in some order

Plotting this moving average on the original graph looks like this…



The moving average looks much more linear than the raw data and can be used more effectively as a regression basisTime SALES

3 POINT MOVING


Using the Statistics Mode to calculate a linear regression for the moving average






1. Using ONLY times that actually have moving averages, enter those time values into LIST 1




1. Using ONLY times that actually have moving averages, enter those time values into LIST 1

2. Enter the moving averages into LIST 2




1. Using ONLY times that actually have moving averages, enter those time values into LIST 12. Enter the moving averages into LIST 23. CALC – LINEAR REG

We now have a STRAIGHT line through the data,



We now have a STRAIGHT line through the data, and can use it to make predictions



data collected over a period of time example data seems pretty random!!

Documents

data slide

moving average slide

order slide

general trend slide

regression basis slide

period of time slide

moving averages

calc linear reg slide