## What is Interpolation?

Personally, for me to understand difficult statistics procedures I find it easier to see concrete examples, things I can understand and on a rare occasion sometimes I’ll even study ugly math. So, I’m not in the mood for ugly math today, So let’s use M & M’s
The colors around these M&Ms are the interpolated colors the source.
Or maybe we could learn from some crime data from San Francisco.
The value of the data outside has been estimated with color and contour perimeters using interpolation.
Scientists use this technique because there is never enough time or money to measure every point in the area of interest.
Interpolation is based on:
Tobler’s Law of Geography, which states that everything is related to everything else, but near things are more related than distant things.
Points closer together in space are more likely to have similar values than points that are farther apart.
This is called spatial autocorrelation, by the way I Googled most of this.
Interpolation is used in many fields, from photography to geology.
There are many different computer algorithms used to interpolate data points.
Scientists choose between different algorithms based on the type of data and how the data will be used.
Here’s an example of how it’s done with software.
So Interpolation is a method for estimating the value of a function between two known data points or values.
For example, suppose we have data for temperatures and corresponding air velocity from 0 to 2000 in 200 steps. Interpolation can be used to estimate the temperature for non re-corded values such as air velocity of 250 .
First you need to know there’s different types of interpolation and here’s just 2 to get started.
Linear interpolation involves estimating a new value by connecting two adjacent known values points with a straight line.
Spline or Cubic interpolation is an approach that with a sequence of value points, and a curve is constructed whose shape closely follows this sequence.
In Graph Builder, we can see that with these data points plotted with a tight Spline allow us to visualize and forecast what the in between data points would be.
If I turn on the cross hairs I can hover over and interpolate values on the fly.
To better analyze this and attain some prediction values we’ll use the Fit Y by X. Which is a comparison of 2 variables. I’ll choose Air Velocity as the Predictor or Y , and Thermal as the X.
Here are plotted data points. Using the red triangle I’ll add a Flexible Line, under Fit Spline choose then I’ll choose 1.
Now for the prediction of data points, we don’t have.
Under the red triangle of the Fit Line. I’ll save a prediction column back to our data table.
Save Predicteds, I didn’t even now that word existed.
Now when I type in a value for Air Velocity I’ll have my interpolated or predicted value for Temperature.
So that’s Interpolation.
If you like this video, punch the thumbs up, and subscribe so I’ll know to more of these videos.
And remember your homework; throw 5 M&Ms in a low clear dish with water in it. Watch the colors merge.
When you are done, do it again, empty the dish and this time uses several different arrangements of color and position.

## Videos

 bootstrap 201000 correlation 135000 histogram 90500 confidence interval 49500 z score 49500 barchart 40500 box plot 33100 cluster 33100 neural network 27100 chi square test 27100 scatter plot 27100 monte carlo simulation 18100 principal component analysis 14800 random sampling 12100 hypothesis testing 12100 factor analysis 9900 paired t test 9900 one way anova 6600 sample size 5400 two sample t test 5400 two way anova 4400 one sample t test 4400 manova 4400 multiple linear regression 2900 run chart 2900 simple linear regression 2400 non parametric test 1900 stepwise regression 1900 arima model 1900 discriminant analysis 1900 gauge r&r 1600 factorial anova 1300 market basket analysis 1300 regression tree 1300 mosaic plot 720 classification tree 720 multiple logistic regression 590 fractional factorial design 480 full factorial design 320 process capability analysis 260 association analysis 260 randomization test 210 non parametric correlation 210 repeated measures analysis 170 accelerated life testing 170 mixed model analysis 110 model comparison 110 attribute control chart 110 simple logistic regression 110 fit distribution 90 distribution fitting 90 assessing normality 70 variable control chart 70 finding the area under a normal curve 20 pareto plots 10 Finding standardized values Time series smoothing models Capability analysis for multiple responses Msa continuous data Msa attribute data Full factorial analysis Fractional factorial analysis Screening experiment analysis TOTAL 847200

## Videos

 bootstrap 201000 correlation 135000 histogram 90500 confidence interval 49500 z score 49500 barchart 40500 box plot 33100 cluster 33100 neural network 27100 chi square test 27100 scatter plot 27100 monte carlo simulation 18100 principal component analysis 14800 random sampling 12100 hypothesis testing 12100 factor analysis 9900 paired t test 9900 one way anova 6600 sample size 5400 two sample t test 5400 two way anova 4400 one sample t test 4400 manova 4400 multiple linear regression 2900 run chart 2900 simple linear regression 2400 non parametric test 1900 stepwise regression 1900 arima model 1900 discriminant analysis 1900 gauge r&r 1600 factorial anova 1300 market basket analysis 1300 regression tree 1300 mosaic plot 720 classification tree 720 multiple logistic regression 590 fractional factorial design 480 full factorial design 320 process capability analysis 260 association analysis 260 randomization test 210 non parametric correlation 210 repeated measures analysis 170 accelerated life testing 170 mixed model analysis 110 model comparison 110 attribute control chart 110 simple logistic regression 110 fit distribution 90 distribution fitting 90 assessing normality 70 variable control chart 70 finding the area under a normal curve 20 pareto plots 10 Finding standardized values Time series smoothing models Capability analysis for multiple responses Msa continuous data Msa attribute data Full factorial analysis Fractional factorial analysis Screening experiment analysis TOTAL 847200

## Free Data

The best way to learn the craft of predictive analytics or even just preparing data is to find information you’re interested in and tell a story. And finding an interesting data set to share a story can be the most difficult part of creating engaging data visualization. Hopefully this list will help you with finding that interesting cool data.

### Effective Clear Data visualization is a multi-step process.

1st you have to find reliable data, then you need to clean it,  possibly join multiple tables that have conflicting date formatting, get it into the right format, and then uncover the story you will visualize.

I just joined this, pretty valuable.  https://data.world/about/
Discover and share cool data, connect with interesting people, and work together to solve problems faster.

### Yep. Amazon has some free data.  AWS

AWS hosts a variety of public data sets that anyone can access for free.

Previously, large data sets such as the mapping of the Human Genome required hours or days to locate, download, customize, and analyze. Now, anyone can access these data sets via the AWS centralized data repository large dataset.

Go There

### Government and political data

• Data.gov: This is the  go-to resource for government-related data. It claims to have up to 400,000 data sets, both raw data and geo spatial, in a variety of formats.
• The only caveat in using the data sets is you have to make sure you clean them, since many have missing values and characters.
• Socrata is another good place to explore government-related data. One great thing about Socrata is they have some visualization tools that make exploring the data easier.
• City-specific government data: Some cities have their own data portals setup to browse through city-related data. For example, at San Francisco Data you can browse through everything from crime statistics to parking spot available in the city.
• The UN and UN-related sites like UNICEF and the World Health Organization are rich with all kinds of data, from mortality rates to world hunger statistics.
• The Census Bureau houses a ton of information about our lives around income, race, education, population and business.

### Data aggregators

These are the places that house data from all kinds of sources. Sometimes it’s easier to find something here related to a specific category.

• Programmable Web: A really useful resource to explore API’s and also mashups of different API’s.
• Infochimps have a data marketplace that offers thousands of public and propietary data sets for download and API access, in a wide range of categories, from historical Twitter and OK Cupid data, to geo locations data, in different formats. You can even upload you own data if you like.
• Data Market is a good place to explore data related to economics, healthcare, food and agriculture, and the automotive industry.
• Google Public data explorer houses a lot of data from world development indicators, OECD and human development indicators, mostly related to economics data and the world.
• Junar is a great data scraping service that also houses data feeds.

### Social data

Usually, the best place to get social data for an API is the site itself: InstagramGetGlue, Foursquare, pretty much all social media sites have their own API’s. Here are more details on the most popular ones.

• Instagram
• GetGlue
• Foursquare: They have their own API and you can get it through Infochimps, as well.

### Weather data

• Wunderground has detailed weather information and also let’s you search historical data by zip code or city. It gives temperature, wind, precipitation and hourly observations for that day.
• Weatherbase has detailed weather stats on temperature, rain and humidity of nearly 27,000 cities.

### Sports data

These three sites have comprehensive information on teams, players coaches and leaders by season.

### Universities and research

Searching the work of academics who specialize in a particular area is always a great place to find some interesting data.

If you come across specific data that you would like to use, say, in a research paper, the best way to go is to contact the professor or researcher.

UCLA. – One university that makes some of the datasets used in its courses publicly.

### News data

The New York Times has a great API and a really good explorer to access any article in the publication. The data is returned in json format.