Developing Data Products course - Assignment Week 4 - Shiny Application and Reproducible Pitch

Assignment Part 1:

• This presentation is one half of the assignment of week 4, Developing Data Products course from Coursera. he presentation is meant to show information about the second half of the above mentioned assignment (a development project).
• The presentation was generated using RStudio(https://www.rstudio.com) and Slidify(http://slidify.org) framework.

Assignment Part 2:

• The second half of the mentioned assignment was to develop a web application. The application name is New York Air Quality - the R dataset: airquality. This link contain the running application: https://chris1234oz.shinyapps.io/AIR_P/
• This application shows an example of making a web application using R and Shiny framework together.
• The application and the current presentation source codes can be found at The application comprises 3 files: ui.R (UI), server.R (backend)

Application (pitch):

• In this applicaton, use we compare Linear Regression model to relationship between Ozone & one of the air quality value.
• This application is for anyone who is interested Linear regression model & the airquality dataset. The datasetis about the: Daily air quality measurements in New York, May to September 1973.
• Linear regression explained:is one of the most important statistical techniques for business applications. - It’s a statistical methodology that helps estimate the strength and direction of the relationship between two or more variables.
• ur/server.r code: https://github.com/ChrisBly/Developing-Data-Products-course---Assignment-Week-4---Shiny-Application-and-Reproducible-Pitch.git

## 
## Call:
## lm(formula = Ozone ~ Solar.R, data = airquality)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -48.292 -21.361  -8.864  16.373 119.136 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 18.59873    6.74790   2.756 0.006856 ** 
## Solar.R      0.12717    0.03278   3.880 0.000179 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 31.33 on 109 degrees of freedom
## Multiple R-squared:  0.1213, Adjusted R-squared:  0.1133 
## F-statistic: 15.05 on 1 and 109 DF,  p-value: 0.0001793

Conclusion:

• This application is useful for anyone interested in the Linear regression modelling & the relationship of Ozone & one of the air quality value in New York, May to September 1973.