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Activity5: Univariate and Bivariate Analysis using Visual DashboardActivity 5 Univariate and Bivariate Analysis of Food Poisoning Outbreak Using Visual Dashboard Activity Objectives: After completing

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How to fill out activity-5 univariate and bivariate

01

To fill out activity-5 univariate, follow these steps:

02

Start by gathering the univariate dataset that you want to analyze.

03

Determine the variable or variables that you want to study in the dataset.

04

Calculate various descriptive statistics such as mean, median, mode, range, and standard deviation for the selected variable or variables.

05

Visualize the distribution of the variable or variables using appropriate graphs such as histograms, box plots, or bar charts.

06

Analyze the skewness and kurtosis of the variable or variables to understand their shape.

07

Conduct hypothesis tests or inferential statistics to draw conclusions about the population based on the sample data.

08

Summarize your findings and interpretations in a clear and concise manner, including any significant insights or observations.

09

To fill out activity-5 bivariate, follow these steps:

10

Start by gathering the bivariate dataset that you want to analyze.

11

Select the two variables that you want to study and determine their relationship.

12

Calculate various descriptive statistics for each variable, such as mean, median, mode, range, and standard deviation.

13

Create scatter plots or line graphs to visualize the relationship between the two variables.

14

Analyze the correlation coefficient to understand the strength and direction of the relationship.

15

Conduct hypothesis tests or inferential statistics to examine if the relationship is statistically significant.

16

Summarize your findings and interpretations in a clear and concise manner, highlighting any important patterns or trends.

Who needs activity-5 univariate and bivariate?

01

Activity-5 univariate and bivariate is useful for researchers, statisticians, data analysts, and anyone who wants to analyze and understand the characteristics of a dataset or the relationship between two variables.

02

It is commonly used in fields such as social sciences, economics, psychology, marketing, and healthcare to derive insights, make predictions, and form conclusions based on data analysis.

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