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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.
• Work and life could be seriously influenced.
• How can we avoid to be the target?

Problem Statement

• If the bad guy target on someone, there should be some reasons.
• The reason may lies on the age, skin color, and other visiable things on you.
• It could also have relation to your knowledge or awarness to avoid criming. # Data
• The data to drive this reasearch is comming from Australia Bureau of Statistic.
• Link: Crime Victimisation, Australia, 2015-16
• data collecting method: phone call wiht omputer Assisted Interviewing (CAI)
• sample size: 45,086 private dwellings are interviewed in total.
• 28,276 or 75% fully responded to the questions
• the data are shown in percentage(%)

Data Cont.

• the category of victims included in the data:
• Sex
• Age group
• Marital status
• Country of birth
• Level of highest non-school qualification
• Labour force status
• Equivalised weekly household income
• the criminal arre seperated into 4 class and have a total column.
• Physical assault
• Face-to-face threatened assault
  
• Non face-to-face threatened assault
  
• Total threatened assault
• Total assault

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.