instpy 0.0.1

Inferencial Stadistics library

Instpy - Inferencial Stadistics Python

What is Instpy?

instpy is a stadistic library which permit you analyze measures obtaining main stadistical results like

• Histograms
• Qqplots
• Levene test
• Shapiro-Wilk test
• Parametric or Non parametric test.
  With this library you can test multiple or single measures, check if data verify normality condition or not and test measures against stadistic tests. Instpy has class where you can instance an object with following attributes:

#Creating an InferencialStats object
InferencialStats(measures,alpha,is_paired,mean)

measures: list lists of data (int or float)
alpha (Optional): Significance level (float) E.g: 0.05 or 0.01
is_paired (Optional) : bool flag to declare your measures as paired or non paired data
mean (Optional): Scalar value to test against single measure

Methods

The main method of this library.

inferencial_statistics()

It executes an study about its attributes following next workflow

• ¿Single or multiple measures?
• Obtain [Histogram , Qqplot , Shapiro-Wilk test results] of every measure
• Perform Levene Test
• Perform Parametric or Non-parametric test depending on whether measures follow
  normal distribution or non normal distribution

If you want to get some additional features about your analysis you can try with these methods

crit_diff

Display a graphical analisys comapring critical differences from each measures

InferencialStats.crit_diff()

show_hists

Plot a plotly.graph_object.Figure with all measure histograms

InferencialStats.show_hists();

show_qqplots

Plot a plotly.graph_object.Figure with all measure qqplots

InferencialStats.show_qqplots();

get_swtests

Return Pana.DataFrame with Shapiro-Wilk test results confirming if they follow or not a normal distribution

InferencialStats.get_swtests();

---

One method that is not vinculate with an InferencialStats object but library Instpy

get_ranks

Get ranks of input measures

InferencialStats.get_ranks(measures);

---

Installation

#With pip
pip install instpy

Example

import instpy
#Lets create some measures such as normal or uniform data distribution
x = np.random.normal(size=100).tolist()
y = np.random.normal(size=100).tolist()
t = np.random.normal(size=100).tolist()
z = np.random.normal(size=100).tolist()
#---------------------------------------------------------------------
xx = np.random.normal(size=100).tolist()
yx = np.random.normal(size=104).tolist()
tx = np.random.normal(size=110).tolist()
zx = np.random.normal(size=108).tolist()
#---------------------------------------------------------------------

Single measure case

#Create measure parameter
## Single data -------------
single_measure=[x]

res=InferecialStats(single_measure,is_paired=True,mean=80)
#Now lets analyze measure
res.inferencial_statistics()

#In this case it will only one plot
res.show_hists()
res.show_qqplots()
print(res.get_swtests())
print(res.get_t_res())

Multiple measure case

Normal measures

## Multiple data
data_measure=[x,y,z,t]

res=InferecialStats(data_measure,is_paired=True,alpha=0.05)
res.inferencial_statistics()
#-------Results-------
# [x]-->Histograms
# [x]-->Qqplot
# [x]-->Shapiro-Wilk test
# [x]-->Levene Test
# [x]-->Normality Condition
# [x]-->Parametric Test
#      |
#      |- One - Way ANOVA Repeated Measures
# [ ]-->Non Parametric Test

Non normal measures and unpaired

## Multiple data

data_measure=[xx,yx,zx,tx]

res=InferecialStats(data_measure,is_paired=False,alpha=0.05)
res.inferencial_statistics()
#-------Results-------
# [x]-->Histograms
# [x]-->Qqplot
# [x]-->Shapiro-Wilk test
# [x]-->Levene Test
# [ ]-->Normality Condition
# [ ]-->Parametric Test
# [x]-->Non Parametric Test
#      |
#      |- Kruskal

Author - Contact

Carlos Enrique - [e-mail]

LICENSE

MIT