SINGLE BAR CHART
This is basically used to visualize a single categorical variable (univariate analysis)
On the horizontal axis, we have the variable… on the vertical axis, we have the frequency.
MULTIPLE / GROUPED BAR CHART.
This is a bar chart that we can use to visualize two categorical variables (bivariate analysis)
We plot the bars next to each other.
COMPONENT/STACKED BAR CHART
Just like the multiple bar chart… also used to compare two categorical variables.
The bars are stacked on each other.
The MBC and CBC are literally the same… using any boils down to preference
PIE CHART / DONUT CHART
This is just like the bar chart, it is use to visualize single categorical variables.
Bar charts make use of bars, pie chart uses sectors.
HISTOGRAM
This is one powerful tool that can be used to visualize the distribution of a single variable.
By knowing the distribution of a data, you can tell a lot about it.
Comparing the histogram with the bar chart and quote or reply with their similarities and differences 👀
SINGLE LINE CHART/ GRAPH
This is basically use to visualize the change of a variable over a TIME.
MULTIPLE LINE CHART
If you want to compare 2 variables and how they change with time, then we use a multiple line chart.
COMPOUND LINE CHART.
This is use to visualize layers of data and also the proportion that makes up the total data.
You can combine 2 charts to form a single one, this is known as COMBINATION CHART (combo chart for short)
For example, you can combine a single line and bar chart
This way you see how the variables changes over a period of time and also how it distributes itself at the same time
BOX PLOT.
This is one special and powerful tool.
Special in the sense that it is a bit different from others in terms of how it is structured but powerful cos it can visualize spread of data.
It can also detect and remove outliers
SCATTER PLOT.
This is basically used for bivariate analysis when we want to compare the strength of association between 2 numerical variable.
We actually have couple of data visualization tools here and there but these are the popular ones that we see in everyday dashboards.
Thanks for making it to the end of this thread.
Please retweet, like and follow me for more threads like this 🥰
I also share resources for data analysis and data science and have a YouTube channel where I teach statistics needed for DA and DA… you can check out the link below
I can also perform both statistical and data analysis for your project and research.
My DM is opened for work 🎉.
We see in the next one and have a nice day ahead 😇
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SPSS is one of the best tool out there when it comes to STATISTICAL ANALYSIS for research and project.
It can also be used for DATA ANALYSIS too
So I’m putting up these thread of steps on how to download for free and install it😊
Retweet cos, it’s a THREAD 🧵
First I need y’all to know that I can perform statistical and data analysis for your projects, research and academics with SPSS, mini tab, stata and excel.
My dm is opened for business 😊
And if you are willing to start DATA ANALYSIS or DATA SCIENCE… I have a YouTube channel where I teach the needed statistics from the basics.
Check my pinned tweet for the syllabus and link below to my statistics playlist ⬇️
One way ANOVA ➡️
Two way ANOVA ↔️
ANCOVA, MANOVA…. Etc
What are they and what are they used for?
This thread will define each of these and their applications to DATA ANALYSIS and DATA SCIENCE.
Retweet because , it’s a THREAD 🧵
Let’s go 💨
Let’s start with definitions.
Analysis of variance - ANOVA for short is a statistical analysis that we use to check if there is a difference in the mean of at least 2 groups by the use of their variance.
In simple words, ANOVA defines DIFFERENT from VARIABILITY 😇
ANOVA is a parametric statistical test, meaning the underlying distribution is NORMAL and it has a test statistics.
The test statistics of ANOVA is an F RATIO given below as
F = variability between groups/ variability within groups.
Your job as a data analyst is to solve problems with your data set, and statistically any decision you make can never be 100% correct, that is there is a form of error in your conclusion or a chance your result is by luck.. this is STATISTICAL SIGNIFICANCE
It’s a thread🧵🪡🤗
Let’s start with a simple logic.
If I am 95% sure of the result of my conclusion, it means I’m 5% not sure 🤔.
If I’m 90% sure, I’m 10% not sure…. As we will see in later these are the loose definition for confidence interval and level of significance.
Statistical significance is basically used in statistics to conclude wether the results we are having after performing statistical analysis is as a result of luck or not.
The fact that you are making that conclusion does not mean you are right (it may be just you’re lucky)