USE PYTHON: Bivariate Analysis

A bivariate analysis is used to find out if there is a relationship between two variables, measure the

changes that occurred, and to what extent. It is one of the simplest forms of statistical analysis.

According to the chosen variables in the previous report, perform the appropriate bivariate analysis. At

least two different techniques from the following list are expected:

$$contingency tables

$$regression analysis

$$correlation coefficients

$$hypothesis testing

Multivariate Analysis

A multivariate analysis is a set of statistical models that examine patterns in multidimensional data by

considering several data variables at once. It is an extension of bivariate analysis which means it involves

multiple variables at the same time to find correlation between them. Perform the appropriate

multivariate analysis. At least one technique from the following list is expected:

$$multiple linear regression

$$multiple logistic regression

$$multivariate analysis of variance $($MANOVA$)$

$$

Visualization

$$Select the suitable plot type to describe the data graphically; $3$to $4$bivariate visuals, $1$

multivariate visual $($other than a heatmap$)$

o Ensure to include all components of the graph

o Create a heatmap for numerical variables

$$Provide a brief comment for each visualization

$$You may use Power BI or Python$/$R to complete this task

Findings and Recommendations

While findings describe what is observed in the scope of statistical analysis, recommendations tie

specific opportunities to specific user needs, and they relate to valuable business objectives.

Interpreting findings in context yields recommendations.

The last section of this report is statistical analysis interpretation, which provides possible answers to some of the research questions. Make a list of research questions that you can solve with this data and carry out this last step an analysis of the answers to these questions with the information found in the bivariate and multivariate analysis. the information: MCI$\_$CATEGORY Assault $123436$Auto Theft $44670$Break and Enter $42615$Robbery $18696$Theft Over $8125$

$$

$($ROWS $237542,$COLUMNS $21)$COLUMNS: $($TARGET: MCI$\_$CATEGORY$),($FEATURES: OCC$\_$DATE', 'Month$\_$Year', 'OCC$\_$YEAR', 'OCC$\_$MONTH', 'OCC$\_$DAY', 'OCC$\_$DOY', 'OCC$\_$DOW', 'OCC$\_$HOUR', 'PREMISES$\_$TYPE', 'OFFENCE', 'NEIGHBOURHOOD', 'LONGITUDE', 'LATITUDE', 'Population', 'Labour$\_$force', 'Employment$\_$population', 'Unemployment$\_$population', 'Unemployment$\_$rate', 'Participation$\_$rate', 'Employment$\_$rate $)$

$$

DATAFRAME: CRIIME$\_$EMPLOY

$$

numeric$\_$columns$\_$final $=\text{'}$OCC$\_$YEAR', 'OCC$\_$DAY', 'OCC$\_$DOY', 'OCC$\_$HOUR', 'LONGITUDE', 'LATITUDE',

$$

$\text{'}$Population$\text{'},$ 'Labour$\_$force', 'Employment$\_$population',

$$

$\text{'}$Unemployment$\_$population', 'Unemployment$\_$rate', 'Participation$\_$rate',

$$

$\text{'}$Employment$\_$rate'

$$

Categorical$\_$columns$\_$final $=\text{'}$Month$\_$Year', 'OCC$\_$MONTH', 'OCC$\_$DOW', 'PREMISES$\_$TYPE', 'OFFENCE', 'MCI$\_$CATEGORY', 'NEIGHBOURHOOD'$],$Date column: OCC$\_$DATE