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Introduction

• The following link will help you with creating R Markdown Slidy Presentations
• http://rmarkdown.rstudio.com/slidy_presentation_format.html
• Don’t forget about Bootcamp 4
• A good introduction provides a brief background to the problem, defines important terms, and leads to a strong rationale.

Introduction Cont.

• crime is happening everywhere and everyday.

• people would suffer from different kind of physical and emotional effects after the experiance of crime.

• Use bullet points to help minimise text.
• Add relevant images to make your presentation more appealing
• Remember, you have a maximum of 20 slides to fit everything in.

• Ensure each slide fits on one screen. The reader should not have to scroll down.

Problem Statement

• State the overall problem/question driving the investigation
• Summarise how you will use statistics to solve the problem or answer your question.

Data

• You can use multiple slides to break up your sections.
• Explain how you collected your data.
• There should be enough detail here so that someone else could replicate your data collection.
• Explain the sampling method if known
• Ensure you reference the data source if you have used Open Data

Data Cont.

• List and explain the important variables.
• If you have factors, explain each level.
• Explain the scale of numeric variables.
• Justify everything that you do to preprocess the data.

Descriptive Statistics and Visualisation

• Summarise the important variables in your investigation.
• Use visualisation to highlight interesting features of the data and tell the overall story.
• Explain how you dealt with data issues (if any), e.g. missing data and outliers.
• Here are the examples of R chunks and outputs

plot(Sepal.Length ~ Petal.Length, data = iris)

Decsriptive Statistics Cont.

• You can use the knitr:kable function to print nice HTML tables. Here is an example R code:

iris %>% group_by(Species) %>% summarise(Min = min(Petal.Length,na.rm = TRUE),
                                           Q1 = quantile(Petal.Length,probs = .25,na.rm = TRUE),
                                           Median = median(Petal.Length, na.rm = TRUE),
                                           Q3 = quantile(Petal.Length,probs = .75,na.rm = TRUE),
                                           Max = max(Petal.Length,na.rm = TRUE),
                                           Mean = mean(Petal.Length, na.rm = TRUE),
                                           SD = sd(Petal.Length, na.rm = TRUE),
                                           n = n(),
                                           Missing = sum(is.na(Petal.Length))) -> table1
knitr::kable(table1)

Hypothesis Testing

• Apply an appropriate hypothesis test for your investigation.
• Ensure you state and check any assumptions.
• Report the appropriate values and interpret the results.
• Here is an example:

model1 <- lm(Sepal.Length ~ Petal.Length, data = iris)
model1 %>% summary()

## 
## Call:
## lm(formula = Sepal.Length ~ Petal.Length, data = iris)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.24675 -0.29657 -0.01515  0.27676  1.00269 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   4.30660    0.07839   54.94   <2e-16 ***
## Petal.Length  0.40892    0.01889   21.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4071 on 148 degrees of freedom
## Multiple R-squared:   0.76,  Adjusted R-squared:  0.7583 
## F-statistic: 468.6 on 1 and 148 DF,  p-value: < 2.2e-16

Hypthesis Testing Cont.

• Here are the examples of mathematical equations:

\[H_0: \mu_1 = \mu_2 \]

\[H_A: \mu_1 \ne \mu_2\]

\[S = \sum^n_{i = 1}d^2_i\]

• You can also place equations inline: \(z = \frac{x - \mu}{\sigma}\)

Discussion

• Discuss the major findings of your investigation
• Discuss any strengths and limitations.
• Propose directions for future investigations.
• This is a good place to re-state your findings as a final conclusion. What is the one take home message the reader should leave with?
• Your final conclusion needs to be very clear.

References

• Provide a list of any references you use in the presentation.