Beta Error

https://www.khanacademy.org/math/ap-statistics/tests-significance-ap/error-probabilities-power/v/introduction-to-type-i-and-type-ii-errors

https://www.google.com/search?q=%CE%B2+error&rlz=1C1CHBF_enBD864BD864&oq=%CE%B2+error&aqs=chrome..69i57j0l7.10568j0j8&sourceid=chrome&ie=UTF-8

 

Some quick excel tips

Exercise Sheet 5

1d theke clear na , eta clear korte hobe , In Sha Allah.

 

Lagle onno kono tutorial ba example dekhte hobe.

IDA Old Question Solve

Winter 2019/20:

Normal distribution: meu=152
sigma=4.0

The Normal Distribution has:
Mean=Median=Mode
Symmetry about the center
50% of value less than the mean and 50% greater than the mean

 

1(b) Weibull

1(c)
https://www.statisticshowto.com/triangular-distribution/

(but I need to clear it up about Professor Orth solution)

2a

chi squaRE TEST

DEGREES of freedom

 

STATISTICS what it is?

 

 

Winter 2018/19:

 

Exercise Sheet 4

Data Mining Methods: Unit 4
Correlation and Simple Linear Regression

Interpretation of the correlation coefficient
Possible range: [-1, 1]
-1: perfect negative linear relationship
0: no linear relationship,
1: perfect positive linear relationship.

Regression: Objective

To predict one variable from other variables.
To explain the variability of one variable using the other variables.

Predicts scores on one variable from the scores on a second variable.

Response variable: predicting variable (Y )
Predictor variable: predictions based on this variable (X)

Simple regression:
Only one predictor variable; otherwise multiple regression

Linear regression:

Predictions of the response variable (Y ) is a linear function of  the predictor variable (X)

Wrtie your next 3 hours task on paper. Jiid

Data Preprocessing/Exercise Sheet 2

Theory:
Data Preprocessing in the Data Mining Process:

The data mining/KDD process
Why data preprocessing?

Issues in Data Preprocessing:

Data Cleaning
Data Transformation
Variable Construction
Data Reduction and Discretization
Data Integration

The data mining/KDD Process:
Understanding customer: 10%-20%
Understanding data:20-30
Prepare data: 40-70%
Build Models: 10-20%
Evaluate models: 10%-20%
Take action:10%20%

Why data mining?

Real – world data is dirty
Low data quality anyway a huge problem in data mining
Garbage in,garbage out
Different methods, different requirements

R Working Codes for data mining:

R code is case sensitive:
I am doing it from professors sheet.

dim means dimension

 

This line i could not make work:

hist(Ozone,breaks=25,ylim=(c(0,45)),main=”Original data”)

And another question how the imputation works

 

Exercise 2 (K)= I have to find the answers

 

Exercise 3: Answer:

 

 

R:

Manipulation of Vectors and Numbers
Vectors and Assignment
Extraction of Elements from VectorsMatrices
Basic Manipulations
The Data Frame
Table
Frames
Cumulative Distribution Function

Math Update Sommer Semester

https://www.youtube.com/watch?v=6kScLENCXLg

https://www.mathsisfun.com/calculus/implicit-differentiation.html

https://www.mathway.com/popular-problems/Algebra/200043

 

Pyhton as a new beginning

Same output but the code is in diffrent approach.

 

 

Loop Over Integer Colors

 

Daily German Words, that I learn gradually

vergangenheit
schriftlich
verwendung
sowie
hintereinander
Zeitangabe
vorhaben
prophezeiung
gelöscht.
verlieben
streiten
ich sich freuen
ich sich konzentrieren
ich sich streiten
ich sich interessieren
Ich sich bewerben
ich sich verlieben
Ich wasche mich
Ich wasche meine Hose
jemanden angemelden
ich sich anmelden
jemanden argern
ich etwas vorbereiten
ich sich treffen
ich jemanden treffen
unterhalten
ich jemanden weiterbilden
Ich freue mich
ich sich duschen
ich jemanden erinnern
ich sich erinnern
ich jemanden anziehen
verwendung
Vordergrund
Handlung
Hanelnde
weniger
Ich arbeite jeden tag
Ich wurde gern weniger arbeiten
Kannst du mir helfen ?
Könntest du mir helfe
real
irreale
ubersicht zu den Strukturen
kaltes
genus
du freust dich
wascht dir die hande
dessen
deren
dessen
deren
denen
komparation
aufnehmen

 

 

Funny-witzig
Confuse-verwirren
Differences-Unterschied

Ausdruck-Expressions
Also-I mean
irgendwie- Somehow
ja, auf jeden Fall- yeah, definitely
dann doch meistens=usually
Na ja= well

ganz festlegen mochte=don’t want to say something for sure

 

Was machst du gerade ? = What are you doing at the moment ?

häufig=commonly

unterhaltet=chat

Python Course Started, Alhamdulillah

The Python Mega Course: Build 10 Real World Applications

Bismillahir Rahmanir Rahim. I am exploring this course for growing interest in my career:

Summary: Integers, Floats, Lists, Dictionaries, Tuples, dir, help

In this section you learned that:

  • Integers are for representing whole numbers:
  1. rank = 10
  2. eggs = 12
  3. people = 3
  • Floats represent continuous values:
  1. temperature = 10.2
  2. rainfall = 5.98
  3. elevation = 1031.88
  • Strings represent any text:
  1. message = “Welcome to our online shop!”
  2. name = “John”
  3. serial = “R001991981SW”
  • Lists represent arrays of values that may change during the course of the program:
  1. members = [“Sim Soony”, “Marry Roundknee”, “Jack Corridor”]
  2. pixel_values = [252, 251, 251, 253, 250, 248, 247]
  • Dictionaries represent pairs of keys and values:
  1. phone_numbers = {“John Smith”: “+37682929928”, “Marry Simpons”: “+423998200919”}
  2. volcano_elevations = {“Glacier Peak”: 3213.9, “Rainer”: 4392.1}
  • Keys of a dictionary can be extracted with:
  1. phone_numbers.keys()
  • Values of a dictionary can be extracted with:
  1. phone_numbers.values()
  • Tuples represent arrays of values that are not to be changed during the course of the program:
  1. vowels = (‘a’, ‘e’, ‘i’, ‘o’, ‘u’)
  2. one_digits = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
  • To find out what attributes a type has:
  1. dir(str)
  2. dir(list)
  3. dir(dict)
  • To find out what Python builtin functions there are:
  1. dir(__builtins__)
  • Documentation for a Python command can be found with:
  1. help(str)
  2. help(str.replace)
  3. help(dict.values)

German N declension and adjective learning

Adjective Declensions

 

Weak Nouns (the "N-Declension")

 

Declension Tables

Dative Prepositions

Dative Prepositions

 

Accusative Prepositions

 

Very nicely described:

Two-Way Prepositions

 

Genitive Prepositions

 

Contractions

 

Adverbs

 

Conjunctions

Contradiction/
Disagreement
doch Du bist doch nur zugekifft.
You’re just [saying that because you’re] high.

Q: Das ist doch nicht dein Ernst, oder? A: Doch!
Q: You’re not being serious, are you? A: I am!

Modal Particles

Started Course: Java In Depth Become a Complete Java Engineer

I have started this course from Udemy and will complete soon with all my effort In Sha Allah:

 

Book Recommendation: Effective Java, Second Edition, Joshua Bloch
Harnessing Java by Kishori Shoron
Head First Java

 

1st video:

Installed JDK
Environment Setup
Made Hello.java with Notepad++
then done javac Hello from cmd:
It made Hello class which is platform independent

2nd Video:

Installing Intellij and Setup for Running Java Application:

Section:3 Classes and Objects

2d Array and 3D array some problem but will clear later now I should move on with other topics.